Row 4870

Row ID: 4870 | Dataset Entry | Axioma AXP Content Repository

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I’m currently working on a project where I need to populate a tensor ws_expanded based on certain conditions using a nested loop structure. However, I’ve noticed that reconstructing this loop each time incurs a significant computational cost. Here’s the relevant portion of the code for context:

ws_expanded = torch.empty_like(y_rules, device=y_rules.device, dtype=y_rules.dtype) index = 0

for col, rules in enumerate(rule_paths): for rule in rules: mask = y_rules[:, col] == rule ws_expanded[mask, col] = ws[index][0] index += 1

As you can see, the nested loops iterate over rule_paths and rules to populate ws_expanded based on certain conditions. However, as the size of the tensors increases, reconstructing this loop becomes prohibitively expensive.

I’m exploring ways to optimize this process. Specifically, I’m wondering if there’s a way to assign the weights (ws) to ws_expanded permanently using pointers in PyTorch, thus eliminating the need to reconstruct the loop every time.

Could you please advise on the best approach to handle this situation? Any insights or alternative strategies would be greatly appreciated.

FieldValue
text I’m currently working on a project where I need to populate a tensor ws_expanded based on certain conditions using a nested loop structure. However, I’ve noticed that reconstructing this loop each time incurs a significant computational cost. Here’s the relevant portion of the code for context: ws_expanded = torch.empty_like(y_rules, device=y_rules.device, dtype=y_rules.dtype) index = 0 for col, rules in enumerate(rule_paths): for rule in rules: mask = y_rules[:, col] == rule ws_expanded[mask,…
label r/pytorch
dataType post
communityName r/pytorch
datetime 2024-04-23
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url_encoded Z0FBQUFBQm5Lak9GSE1BSnJxSldmOXVOdjVRTkVUamdIMzZzSjVBeXNVTWJZWnJoTFlYanNQRFdtN2I3QjJaLTUwRkZkSEhwc0ZCTHFVM01xVDJJMkctRkEycjFER0MwcmFDakFaWXdBaFNmeDhKN0xrMFNiWVczTnBXakYxcFh0YWV5ZEhfTzVKeHh4OVh5RlA2WU1YNkVYcjlVMXFRTFpvQkRxUHQxZF8yYUlkRWNJMjVFWVB4d0tkNzByRDkzdXB1U1FHZGV4c3RGYXplVm1tR0Z3NE82V21fU3JwR2dRQT09

Raw Record

{
  "text": "I’m currently working on a project where I need to populate a tensor ws_expanded based on certain conditions using a nested loop structure. However, I’ve noticed that reconstructing this loop each time incurs a significant computational cost. Here’s the relevant portion of the code for context:\n\nws_expanded = torch.empty_like(y_rules, device=y_rules.device, dtype=y_rules.dtype)\nindex = 0\n\nfor col, rules in enumerate(rule_paths):\nfor rule in rules:\nmask = y_rules[:, col] == rule\nws_expanded[mask, col] = ws[index][0]\nindex += 1\n\nAs you can see, the nested loops iterate over rule_paths and rules to populate ws_expanded based on certain conditions. However, as the size of the tensors increases, reconstructing this loop becomes prohibitively expensive.\n\nI’m exploring ways to optimize this process. Specifically, I’m wondering if there’s a way to assign the weights (ws) to ws_expanded permanently using pointers in PyTorch, thus eliminating the need to reconstruct the loop every time.\n\nCould you please advise on the best approach to handle this situation? Any insights or alternative strategies would be greatly appreciated.",
  "label": "r/pytorch",
  "dataType": "post",
  "communityName": "r/pytorch",
  "datetime": "2024-04-23",
  "username_encoded": "Z0FBQUFBQm5LakwyTkpEaTh5MGkwbW5DdGhDSi03QkkwalZNS1ZRSTFETGxwMFNNeHRpemFoWlRYMmh4SUplRWY2LTBTbFF3SjRMcGloRWJlSWtjODBNR2hLeUZVMEJzeGc9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9GSE1BSnJxSldmOXVOdjVRTkVUamdIMzZzSjVBeXNVTWJZWnJoTFlYanNQRFdtN2I3QjJaLTUwRkZkSEhwc0ZCTHFVM01xVDJJMkctRkEycjFER0MwcmFDakFaWXdBaFNmeDhKN0xrMFNiWVczTnBXakYxcFh0YWV5ZEhfTzVKeHh4OVh5RlA2WU1YNkVYcjlVMXFRTFpvQkRxUHQxZF8yYUlkRWNJMjVFWVB4d0tkNzByRDkzdXB1U1FHZGV4c3RGYXplVm1tR0Z3NE82V21fU3JwR2dRQT09"
}
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