ttnn.fmod

ttnn.fmod() → None

Performs an eltwise-fmod operation.

\[\mathrm{{output\_tensor}} = \verb|fmod|(\mathrm{{input\_tensor\_a,input\_tensor\_b}})\]

Args:

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

Keyword Args:

memory_config (ttnn.MemoryConfig, optional): memory configuration for the operation. Defaults to None. sub_core_grids (ttnn.CoreRangeSet, optional): sub core grids for the operation. Defaults to None. sub_device_id (ttnn.SubDeviceId, optional): sub device ID for core resolution. Mutually exclusive with sub_core_grids. Defaults to None.

Returns:

ttnn.Tensor: the output tensor.

Binary elementwise operations, C=op(A,B), support input tensors A and B in tile and row major layouts (unless dtype requires specific layout, e.g. BFLOAT8_B or BFLOAT4_B), in interleaved or sharded format (height, width or block sharded), in DRAM or L1. A and B are independent, and can have different tensor specs, with restrictions as in notes below.

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 supports the same layouts and formats as A and B, under the same dtype restriction. For operations that return a new tensor, the layout and memory configuration of C are independent of A and B. The memory configuration can be set with memory_config, and the full tensor spec with output_tensor where the operation takes one; if neither is given, the operation will attempt a best decision at an appropriate tensor spec. The dimensions of C, or of output_tensor if given, must match the broadcast-matched size of A and B. By default, C takes the dtype of A unless an operation-specific rule applies: comparison ops return a boolean mask, and a mixed float pair follows A rather than the wider dtype. There is no boolean dtype, so the mask is carried as 1 for true and 0 for false in the output dtype. Where supported, the output dtype can be overridden by the dtype argument or output_tensor. In-place operations write into A, so C is A and keeps its layout, memory configuration and dtype.

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, BFLOAT4_B, FLOAT32, INT32	TILE, ROW_MAJOR

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

Operands must have the same dtype.

An INT32 tensor with a floating-point scalar is promoted to FLOAT32 before computation, including integral-valued floats such as 2.0. The default output dtype is FLOAT32. Use an integer scalar (for example, 2 instead of 2.0) to retain integer-scalar semantics.

Example

# Create two tensors for floating-point modulo
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 floating-point remainder of division
output = ttnn.fmod(tensor1, tensor2)
logger.info(f"Floating-point modulo result: {output}")