Jagged Tensor Operators

Stable API

torch.ops.fbgemm.jagged_to_padded_dense(values, offsets, max_lengths, padding_value=0) → Tensor

Converts a jagged tensor into a dense tensor, padding with a specified padding value.

Parameters:

• values (Tensor) – Jagged tensor values

• offsets (Tensor[]) – A list of jagged offset tensors, one for each jagged dimension.

• max_lengths (int[]) – A list with max_length for each jagged dimension.

• padding_value (float) – Value to set in the empty areas of the dense output, outside of the jagged tensor coverage.

Returns:

the padded dense tensor

Return type:

Tensor

Example

>>> values = torch.tensor([[1,1],[2,2],[3,3],[4,4]])
>>> offsets = torch.tensor([0, 1, 3])
>>> torch.ops.fbgemm.jagged_to_padded_dense(values, [offsets], [3], 7)
tensor([[[1, 1],
         [7, 7],
         [7, 7]],
        [[2, 2],
         [3, 3],
         [7, 7]]])

Other API

torch.ops.fbgemm.jagged_2d_to_dense(values, x_offsets, max_sequence_length) → Tensor

Converts a jagged tensor, with a 2D values array into a dense tensor, padding with zeros.

Parameters:

• values (Tensor) – 2D tensor containing the values of the jagged tensor.

• x_offsets (Tensor) – 1D tensor containing the starting point of each jagged row in the values tensor.

• max_sequence_length (int) – Maximum length of any row in the jagged dimension.

Returns:

The padded dense tensor

Return type:

Tensor

Example

>>> values = torch.tensor([[1,1],[2,2],[3,3],[4,4]])
>>> x_offsets = torch.tensor([0, 1, 3])
>>> torch.ops.fbgemm.jagged_2d_to_dense(values, x_offsets, 3)
tensor([[[1, 1],
         [0, 0],
         [0, 0]],
        [[2, 2],
         [3, 3],
         [0, 0]]])

torch.ops.fbgemm.jagged_1d_to_dense(values, offsets, max_sequence_length, padding_value) -> Tensor)

Converts a jagged tensor, with a 1D values array, into a dense tensor, padding with a specified padding value.

Parameters:

• values (Tensor) – 1D tensor containing the values of the jagged tensor.

• offsets (Tensor) – 1D tensor containing the starting point of each jagged row in the values tensor.

• max_sequence_length (int) – Maximum length of any row in the jagged dimension.

• padding_value (int) – Value to set in the empty areas of the dense output, outside of the jagged tensor coverage.

Returns:

the padded dense tensor

Return type:

Tensor

Example

>>> values = torch.tensor([1,2,3,4])
>>> offsets = torch.tensor([0, 1, 3])
>>> torch.ops.fbgemm.jagged_1d_to_dense(values, x_offsets, 3, 0)
tensor([[1, 0, 0],
        [2, 3, 0]])

torch.ops.fbgemm.dense_to_jagged(dense, x_offsets, total_L) -> (Tensor, Tensor[])

Converts a dense tensor into a jagged tensor, given the desired offsets of the resulting dense tensor.

Parameters:

• dense (Tensor) – A dense input tensor to be converted

• x_offsets (Tensor[]) – A list of jagged offset tensors, one for each jagged dimension.

• total_L (int, Optional) – Total number of values in the resulting jagged tensor.

Returns:

Values and offsets of the resulting jagged tensor. Offsets are identital to those that were input.

Return type:

(Tensor, Tensor[])

Example

>>> dense = torch.tensor([[[1, 1], [0, 0], [0, 0]], [[2, 2], [3, 3], [0, 0]]])
>>> x_offsets = torch.tensor([0, 1, 3])
>>> torch.ops.fbgemm.dense_to_jagged(dense, [x_offsets])
(tensor([[1, 1],
         [2, 2],
         [3, 3]]), [tensor([0, 1, 3])])

torch.ops.fbgemm.jagged_dense_elementwise_add(x_values, x_offsets, y) → Tensor

Adds a jagged tensor to a dense tensor, resulting in dense tensor. Jagged tensor input will be padded with zeros for the purposes of the addition.

Parameters:

• x_values (Tensor) – Jagged tensor values

• offsets (Tensor[]) – A list of jagged offset tensors, one for each jagged dimension.

• y (Tensor) – A dense tensor

Returns:

The sum of jagged input tensor + y

Return type:

Tensor

torch.ops.fbgemm.jagged_dense_elementwise_add_jagged_output(x_values, x_offsets, y) -> (Tensor, Tensor[])

Adds a jagged tensor to a dense tensor and, resulting in a jagged tensor with the same structure as the input jagged tensor.

Parameters:

• x_values (Tensor) – Jagged tensor values

• x_offsets (Tensor[]) – A list of jagged offset tensors, one for each jagged dimension.

• y (Tensor) – A dense tensor

Returns:

Values and offsets of the resulting jagged tensor. Offsets are identital to those that were input.

Return type:

(Tensor, Tensor[])

torch.ops.fbgemm.jagged_dense_dense_elementwise_add_jagged_output(x_values, x_offsets, y_0, y_1) -> (Tensor, Tensor[])

Adds a jagged tensor to the sum of two dense tensors, resulting in a jagged tensor with the same structure as the input jagged tensor.

Parameters:

• x_values (Tensor) – Jagged tensor values

• x_offsets (Tensor[]) – A list of jagged offset tensors, one for each jagged dimension.

• y_0 (Tensor) – A dense tensor

• y_1 (Tensor) – A dense tensor

Returns:

Values and offsets of the resulting jagged tensor. Offsets are identital to those that were input.

Return type:

(Tensor, Tensor[])

torch.ops.fbgemm.jagged_dense_elementwise_mul(x_values, x_offsets, y) -> (Tensor, Tensor[])

Elementwise-multiplies a jagged tensor a dense tensor and, resulting in a jagged tensor with the same structure as the input jagged tensor.

Parameters:

• x_values (Tensor) – Jagged tensor values

• x_offsets (Tensor[]) – A list of jagged offset tensors, one for each jagged dimension.

• y (Tensor) – A dense tensor

Returns:

Values and offsets of the resulting jagged tensor. Offsets are identital to those that were input.

Return type:

(Tensor, Tensor[])

torch.ops.fbgemm.batched_dense_vec_jagged_2d_mul(Tensor v, Tensor a_values, Tensor a_offsets) → Tensor

Batched vector matrix multiplication of a batched dense vector with a jagged tensor, dense vector is in size (B * H, max_N) and jagged tensor is in size (B, max_N, H * D) where max_N is the maximum size of jagged dimension. B * H is the batch size and each multiplies is max_N with [max_N, D]

Parameters:

• v (Tensor) – dense vector tensor

• a_values (Tensor) – Jagged tensor values

• a_offsets (Tensor []) – A list of jagged offset tensors, one for each jagged dimension.

Returns:

output of batch matmul in size (B * H, D)

Return type:

Tensor

torch.ops.fbgemm.stacked_jagged_1d_to_dense(*args, **kwargs)

torch.ops.fbgemm.stacked_jagged_2d_to_dense(*args, **kwargs)