[GitHub] [tvm] comaniac commented on a change in pull request #7197: [Fix][Autoscheduler] Costmodel enhancement & bug fix for graph debug runtime

[GitBox <gi...@apache.org>]

comaniac commented on a change in pull request #7197:
URL: https://github.com/apache/tvm/pull/7197#discussion_r551199732



##########
File path: python/tvm/auto_scheduler/task_scheduler.py
##########
@@ -82,11 +82,12 @@ def make_search_policies(
     if isinstance(search_policy, str):
         policy_type, model_type = search_policy.split(".")
         if model_type == "xgb":
-            cost_model = XGBModel(num_warmup_sample=len(tasks) * num_measures_per_round)
-            if load_model_file:
-                logger.info("TaskScheduler: Load pretrained model...")
-                cost_model.load(load_model_file)
-            elif load_log_file:
+            cost_model = XGBModel(
+                num_warmup_sample=len(tasks) * num_measures_per_round,
+                model_file=load_model_file,
+            )
+            if load_log_file:
+                logger.info("TaskScheduler: Reload measured states and pretrain model...")

Review comment:
       ```suggestion
                   logger.info("TaskScheduler: Reload measured states and pretrained model...")
   ```
   

##########
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:
       Should we have a warning here? Otherwise it may be confusing.

##########
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:
       ```suggestion
           if len(inputs) < self.last_train_length / 5:
   ```
   Could you explain a bit more on this logic or make it more straightforward?




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