comaniac commented on a change in pull request #7197:
URL: https://github.com/apache/tvm/pull/7197#discussion_r551479201
##########
File path: src/auto_scheduler/feature.cc
##########
@@ -1462,12 +1462,18 @@ void GetPerStoreFeaturesFromMeasurePairs(const
Array<MeasureInput>& inputs,
if (find_res == task_cache.end()) {
if (inputs[i]->task->compute_dag.defined()) { // the measure input is
complete
task = inputs[i]->task;
- } else { // the measure input is incomplete
- // rebuild task for incomplete measure pairs read from file
- Array<te::Tensor> tensors = (*workload_key_to_tensors)(workload_key);
- task = SearchTask(ComputeDAG(tensors), workload_key,
inputs[i]->task->target,
- inputs[i]->task->target_host,
inputs[i]->task->hardware_params,
- inputs[i]->task->layout_rewrite_option);
+ } else {
+ // The measure input is incomplete, rebuild task for incomplete
measure pairs read from file
+ try {
+ Array<te::Tensor> tensors = (*workload_key_to_tensors)(workload_key);
+ task = SearchTask(ComputeDAG(tensors), workload_key,
inputs[i]->task->target,
+ inputs[i]->task->target_host,
inputs[i]->task->hardware_params,
+ inputs[i]->task->layout_rewrite_option);
+ } catch (std::exception& e) {
+ // Cannot build ComputeDAG from workload key, the task may have not
been registered in
+ // this search round
+ continue;
Review comment:
Yeah I know. I was thinking a case that you have a `log.json` which has
no records for the task you are going to tune, so all of them are ignored when
loading. I was thinking to have a message by the end of feature extraction like
we previously did ("encountered XXX errors which are safely ignored"), but
since that message has been removed, maybe we're good for now.
##########
File path: python/tvm/auto_scheduler/cost_model/xgb_model.py
##########
@@ -141,6 +146,12 @@ def update(self, inputs, results):
self.inputs.extend(inputs)
self.results.extend(results)
+ if len(self.inputs) - self.last_train_length < self.last_train_length
/ 5:
Review comment:
Now I got your point, but I'm not sure if reduces training frequency
could solve the problem in general. At the first glance, we could stop to train
the cost model if 1) the accuracy of the current one is sufficient, 2) or we
already have sufficient number of records. Your solution is similar to 2, so
I'm curious if we already have sufficient number of records, then how much
accuracy could be further improved if we train the model again next time with
>20% more data?
On the other hand, I'm wondering if we could leverage the first solution.
For example, we calculate the test accuracy of the measured records after every
round. If the accuracy is higher than the threshold, then we skip the training
in the next round.
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