ttnn.gt_

ttnn.gt_(input_tensor_a: ttnn.Tensor, input_tensor_b: ttnn.Tensor or Number) → ttnn.Tensor

Performs Greater than in-place operation on input_a and input_b and returns the tensor with the same layout as input_tensor

\[\mathrm{{input\_tensor\_a}} > \mathrm{{input\_tensor\_b}}\]

Parameters:

input_tensor_a (ttnn.Tensor) – the input tensor.
input_tensor_b (ttnn.Tensor or Number) – the input tensor.

Returns:

ttnn.Tensor – the output tensor.

Binary elementwise operations, C=op(A,B), support input tensors A and B in row major and tile layout, in interleaved or sharded format (height, width or block sharded), in DRAM or L1. A and B are completely independent, and can have different tensor specs.

Broadcast of A and B operands is supported up to dimension 5 (DNCHW). Any dimensions of size 1 in either A or B will be expanded to match the other input, and data will be duplicated along that dimension. For example, if the shape of A is [2,1,1,32] and B is [1,16,8,1], the output shape will be [2,16,8,32]. The size of dimensions higher than 5 must match between A and B.

The output C also supports row major and tile layout, interleaved or sharded format (height, width or block sharded), in DRAM or L1. The tensor spec of C is independent of A and B, and can be explicitly set using the optional output tensor input; if not provided, the operation will attempt a best decision at an appropriate tensor spec. The dimensions of C, or equivalently the optional output tensor, must match the broadcast-matched size of A and B.

Performance considerations: Elementwise operations operate natively in tile format, tiled tensors are preferred as an input, and row-major tensors are tilized and untilized during the operation. L1 sharded layout is preferred, with no broadcast and matching tensor specs for A, B and C.

Note

Supported dtypes, layouts, and ranks:

Dtypes	Layouts	Ranks
BFLOAT16, BFLOAT8_B	TILE	2, 3, 4

Example

>>> tensor1 = ttnn.from_torch(torch.tensor([[2, 2], [2, 2]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> tensor2 = ttnn.from_torch(torch.tensor([[1, 1], [1, 1]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device)
>>> ttnn.gt_(tensor1, tensor2/scalar)