ttnn.atan2

ttnn.atan2(input_tensor_a: ttnn.Tensor, input_tensor_b: ttnn.Tensor, *, memory_config: ttnn.MemoryConfig = None) → ttnn.Tensor

Element-wise arctangent of input_tensor_a_i / input_tensor_b_i with consideration of the quadrant. Returns signed angles in radians between the vector (input_tensor_b_i, input_tensor_a_i) and the vector (1, 0).

\[\mathrm{output\_tensor}_i = \operatorname{atan2}(\mathrm{input\_tensor\_a}_i, \mathrm{input\_tensor\_b}_i)\]
Parameters:

  • input_tensor_a (ttnn.Tensor) – the input tensor.

  • input_tensor_b (ttnn.Tensor) – the input tensor.

Keyword Arguments:

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

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 and layouts:

Dtypes

Layouts

BFLOAT16, BFLOAT8_B, FLOAT32

TILE, ROW_MAJOR

If the input tensor is ROW_MAJOR layout, it will be internally converted to TILE layout.

The second parameter, input_tensor_b, is the x-coordinate, while the first parameter, input_tensor_a, is the y-coordinate.

Example

# Create two tensors for atan2(y, x)
tensor1 = ttnn.from_torch(
    torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device
)
tensor2 = ttnn.from_torch(
    torch.tensor([[1, 2], [3, 4]], dtype=torch.bfloat16), layout=ttnn.TILE_LAYOUT, device=device
)

# Compute arctangent of y/x with proper quadrant handling
output = ttnn.atan2(tensor1, tensor2)
logger.info(f"Atan2 result: {output}")