Row 5407

Row ID: 5407 | Dataset Entry | Axioma AXP Content Repository

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I have the following input matrix

inp_tensor = torch.tensor( [[0.7860, 0.1115, 0.0000, 0.6524, 0.6057, 0.3725, 0.7980, 0.0000], [1.0000, 0.1115, 0.0000, 0.6524, 0.6057, 0.3725, 0.0000, 1.0000]])

and indices of the zero elements

mask_indices = torch.tensor( [[7, 2], [2, 6]])

How can I exclude the nonzero elements from the multiplication with the following matrix:

my_tensor = torch.tensor( [[0.8823, 0.9150, 0.3829], [0.9593, 0.3904, 0.6009], [0.2566, 0.7936, 0.9408], [0.1332, 0.9346, 0.5936], [0.8694, 0.5677, 0.7411], [0.4294, 0.8854, 0.5739], [0.2666, 0.6274, 0.2696], [0.4414, 0.2969, 0.8317]])

That is, instead of multiplying it including the zeros:

a = torch.mm(inp_tensor, my_tensor) print(a) tensor([[1.7866, 2.5468, 1.6330], [2.2041, 2.5388, 2.3315]])

I want to exclude the zero elements (and the corresponding rows of my\_tensor):

inp_tensor = torch.tensor( [[0.7860, 0.1115, 0.6524, 0.6057, 0.3725, 0.7980]]) # remove the zero elements my_tensor = torch.tensor( [[0.8823, 0.9150, 0.3829], [0.9593, 0.3904, 0.6009], [0.1332, 0.9346, 0.5936], [0.8694, 0.5677, 0.7411], [0.4294, 0.8854, 0.5739], [0.2666, 0.6274, 0.2696]]) # remove the corresponding zero elements rows b = torch.mm(inp_tensor, my_tensor) print(b) >>> tensor([[1.7866, 2.5468, 1.6330]]) inp_tensor = torch.tensor([[1.0000, 0.1115, 0.6524, 0.6057, 0.3725, 1.0000]]) # remove the zero elements my_tensor = torch.tensor( [ [0.8823, 0.9150, 0.3829], [0.9593, 0.3904, 0.6009], [0.1332, 0.9346, 0.5936], [0.8694, 0.5677, 0.7411], [0.4294, 0.8854, 0.5739], [0.4414, 0.2969, 0.8317]]) # remove the corresponding zero elements rows c = torch.mm(inp_tensor, my_tensor) print(c) >>> tensor([[2.2041, 2.5388, 2.3315]]) print(torch.cat([b,c])) >>> tensor([[1.7866, 2.5468, 1.6330], [2.2041, 2.5388, 2.3315]])

I need this to be efficient (i.e., no for loops), as my tensors are quite large, and also to maintain the gradient (i.e., if I call optimizer.backward() that the relevant parameters from the computational graph be updated)

Field	Value
text	I have the following input matrix inp_tensor = torch.tensor( [[0.7860, 0.1115, 0.0000, 0.6524, 0.6057, 0.3725, 0.7980, 0.0000], [1.0000, 0.1115, 0.0000, 0.6524, 0.6057, 0.3725, 0.0000, 1.0000]]) and indices of the zero elements mask_indices = torch.tensor( [[7, 2], [2, 6]]) How can I exclude the nonzero elements from the multiplication with the following matrix: my_tensor = torch.tensor( [[0.8823, 0.9150, 0.3829], [0.9593, …
label	r/pytorch
dataType	post
communityName	r/pytorch
datetime	2024-04-29
username_encoded	Z0FBQUFBQm5LakwyTnBac3pDZDdBZXFoWDNOWmRJbjczU3ZwWmp3WmtnOXk0Q2J2YUlBNE1WVmtqQjhiZHkzVkhPWDhUb3ZzTnNEOHVvOWEyTE40NUFRcjFjd0tnNHlac3c9PQ==
url_encoded	Z0FBQUFBQm5Lak9GQTN3Y3duOTgybTVNWkZDSjdYaXhsMVBJUkNJUlR3QjBPZkxXOGhWSExULWFFRGNoV0tyTGUyUXp4VXlPc0wzVUUxWUJ4MEtpU0g4NGt4ZmVkNnkwM0J0VGRLUElNNmdXc0g1YkJpdnBrLXMxT1YzSTlOQ1RDcFZnaGM5Wk4wYXJHTFpkNVRoOFdnNHJyWU9uc1NKWnh4c1RsWGVsXzZaRjllZ2p0UnFrMndtZFhCRmhaTGZ0SmEyYWZLd3ZIRUM0VFNrYnBEckctR21jZndLc1VydXMwUT09

Raw Record

{
  "text": "I have the following input matrix\n\n    inp_tensor = torch.tensor(\n            [[0.7860, 0.1115, 0.0000, 0.6524, 0.6057, 0.3725, 0.7980, 0.0000],\n            [1.0000, 0.1115, 0.0000, 0.6524, 0.6057, 0.3725, 0.0000, 1.0000]])\n\nand indices of the zero elements\n\n    mask_indices = torch.tensor(\n    [[7, 2],\n    [2, 6]])\n\nHow can I exclude the nonzero elements from the multiplication with the following matrix:\n\n    my_tensor = torch.tensor(\n            [[0.8823, 0.9150, 0.3829],\n            [0.9593, 0.3904, 0.6009],\n            [0.2566, 0.7936, 0.9408],\n            [0.1332, 0.9346, 0.5936],\n            [0.8694, 0.5677, 0.7411],\n            [0.4294, 0.8854, 0.5739],\n            [0.2666, 0.6274, 0.2696],\n            [0.4414, 0.2969, 0.8317]])\n\nThat is, instead of multiplying it including the zeros:\n\n    a = torch.mm(inp_tensor, my_tensor)\n    print(a)\n    tensor([[1.7866, 2.5468, 1.6330],\n            [2.2041, 2.5388, 2.3315]])\n\nI want to exclude the zero elements (and the corresponding rows of my\\_tensor):\n\n    inp_tensor = torch.tensor(\n            [[0.7860, 0.1115, 0.6524, 0.6057, 0.3725, 0.7980]]) # remove the zero elements\n    \n    my_tensor = torch.tensor(\n            [[0.8823, 0.9150, 0.3829],\n            [0.9593, 0.3904, 0.6009],\n            [0.1332, 0.9346, 0.5936],\n            [0.8694, 0.5677, 0.7411],\n            [0.4294, 0.8854, 0.5739],\n            [0.2666, 0.6274, 0.2696]]) # remove the corresponding zero elements rows\n    \n    b = torch.mm(inp_tensor, my_tensor)\n    print(b)\n    >>> tensor([[1.7866, 2.5468, 1.6330]])\n    \n    inp_tensor = torch.tensor([[1.0000, 0.1115, 0.6524, 0.6057, 0.3725, 1.0000]]) # remove the zero elements\n    \n    my_tensor = torch.tensor(\n            [\n            [0.8823, 0.9150, 0.3829],                \n            [0.9593, 0.3904, 0.6009],\n            [0.1332, 0.9346, 0.5936],\n            [0.8694, 0.5677, 0.7411],\n            [0.4294, 0.8854, 0.5739],\n            [0.4414, 0.2969, 0.8317]])  # remove the corresponding zero elements rows\n    \n    c = torch.mm(inp_tensor, my_tensor)\n    print(c)\n    >>> tensor([[2.2041, 2.5388, 2.3315]])\n    print(torch.cat([b,c]))\n    >>> tensor([[1.7866, 2.5468, 1.6330],\n            [2.2041, 2.5388, 2.3315]])\n\nI need this to be efficient (i.e., no for loops), as my tensors are quite large, and also to maintain the gradient (i.e., if I call optimizer.backward() that the relevant parameters from the computational graph be updated)",
  "label": "r/pytorch",
  "dataType": "post",
  "communityName": "r/pytorch",
  "datetime": "2024-04-29",
  "username_encoded": "Z0FBQUFBQm5LakwyTnBac3pDZDdBZXFoWDNOWmRJbjczU3ZwWmp3WmtnOXk0Q2J2YUlBNE1WVmtqQjhiZHkzVkhPWDhUb3ZzTnNEOHVvOWEyTE40NUFRcjFjd0tnNHlac3c9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9GQTN3Y3duOTgybTVNWkZDSjdYaXhsMVBJUkNJUlR3QjBPZkxXOGhWSExULWFFRGNoV0tyTGUyUXp4VXlPc0wzVUUxWUJ4MEtpU0g4NGt4ZmVkNnkwM0J0VGRLUElNNmdXc0g1YkJpdnBrLXMxT1YzSTlOQ1RDcFZnaGM5Wk4wYXJHTFpkNVRoOFdnNHJyWU9uc1NKWnh4c1RsWGVsXzZaRjllZ2p0UnFrMndtZFhCRmhaTGZ0SmEyYWZLd3ZIRUM0VFNrYnBEckctR21jZndLc1VydXMwUT09"
}

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Entry Information

Entry ID: 5407
Repository: Axioma AXP
Dataset: arrmlet/reddit_dataset_36
Total Entries: 100,000