ttnn.sum

ttnn.sum(input_a: ttnn.Tensor, dim: number, keepdim: bool = False, *, memory_config: ttnn.MemoryConfig = None, compute_kernel_config: ttnn.ComputeKernelConfig = None, scalar: float = 1.0, correction: bool = True) → ttnn.Tensor

Computes the sum of the input tensor input_a along the specified dimension dim. If no dimension is provided, sum is computed over all dimensions yielding a single value.

Parameters:

  • input_a (ttnn.Tensor) – the input tensor. Must be on the device.

  • dim (number) – dimension value to reduce over.

  • keepdim (bool, optional) – keep original dimension size. Defaults to False.

Keyword Arguments:

  • memory_config (ttnn.MemoryConfig, optional) – Memory configuration for the operation. Defaults to None.

  • compute_kernel_config (ttnn.ComputeKernelConfig, optional) – Compute kernel configuration for the operation. Defaults to None.

  • scalar (float, optional) – A scaling factor to be applied to the input tensor. Defaults to 1.0.

  • correction (bool, optional) – Applies only to ttnn.std() - whether to apply Bessel’s correction (i.e. N-1). Defaults to True.

Returns:

ttnn.Tensor – the output tensor.

Note

The input tensor supports the following data types and layouts:

Input Tensor

dtype

layout

FLOAT32

ROW_MAJOR, TILE

BFLOAT16

ROW_MAJOR, TILE

BFLOAT8_B

ROW_MAJOR, TILE

The output tensor will match the data type and layout of the input tensor.

Memory Support:

  • Interleaved: DRAM and L1

  • Sharded (L1): Width, Height, and ND sharding

  • Output sharding/layout will mirror the input

Example

input_a = ttnn.rand(1, 2), dtype=torch.bfloat16, device=device)
output = ttnn.sum(input_a, dim, memory_config)