tkonolige commented on code in PR #11479:
URL: https://github.com/apache/tvm/pull/11479#discussion_r887321142
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python/tvm/autotvm/tuner/xgboost_cost_model.py:
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@@ -329,15 +338,16 @@ def _get_feature(self, indexes):
for i, fea in zip(need_extract, feas):
fea_cache[i] = fea.value if fea.status == StatusKind.COMPLETE
else None
- feature_len = None
+ feature_len = -1
for idx in indexes:
if fea_cache[idx] is not None:
- feature_len = fea_cache[idx].shape[-1]
- break
+ feature_len = max(fea_cache[idx].shape[-1], feature_len)
ret = np.empty((len(indexes), feature_len), dtype=np.float32)
for i, ii in enumerate(indexes):
t = fea_cache[ii]
+ if t.shape[0] < feature_len:
+ t = np.pad(t, (0, feature_len - t.shape[0]))
Review Comment:
I think autoscheduler pads the extra features with zero, but I don't know if
that would also be a good approach here.
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