ttnn.clone

ttnn.clone(input: ttnn.Tensor, *, dtype: ttnn.DataType = None, memory_config: ttnn.MemoryConfig = None, compute_kernel_config: ttnn.ComputeKernelConfig = None) → ttnn.Tensor

Clones the input tensor, creating a copy with the specified memory configuration and converting its data type to dtype. This operation does not alter the tensor’s layout.

Parameters:

input (ttnn.Tensor) – the input tensor to be cloned.

Keyword Arguments:

  • dtype (ttnn.DataType, optional) – the target data type of the cloned tensor. Defaults to None.

  • memory_config (ttnn.MemoryConfig, optional) – the memory configuration for the clone, options include DRAM_MEMORY_CONFIG or L1_MEMORY_CONFIG. Defaults to None.

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

Returns:

ttnn.Tensor – the cloned output tensor.

Note

  • ROW_MAJOR_LAYOUT: Returns the tensor unpadded in the last two dimensions.

  • TILE_LAYOUT: Pads the tensor to ensure its width and height are multiples of 32.

  • If the input’s current layout matches the specified layout, padding adjustments are applied to the last two dimensions as necessary.

Example

>>> tensor = ttnn.from_torch(torch.rand([1, 32, 32], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> output = ttnn.clone(tensor, dtype=ttnn.bfloat16, memory_config=ttnn.DRAM_MEMORY_CONFIG)