ttnn.fill_rm

ttnn.fill_rm(N: number, C: number, H: number, W: number, hOnes: number, wOnes: number, any: ttnn.tensor, val_hi: number, val_lo: number, *, memory_config: ttnn.MemoryConfig | None = None, queue_id: int | None = 0) → ttnn.Tensor

Generates an NCHW row-major tensor and fill it with high values up to hOnes, wOnes in each HW tile with the rest padded with high values. So for H=2, W=3, hFill=1, wFill=2 the following tensor will be generated:

+------------> W
| hi hi lo
| lo lo lo
|
v H

H, W are expected to be multiples of 32.

The ‘any’ Tensor arg is only used to pass the device and resulting tensor dtype.

val_hi/lo are expected to be floats.

Argument	Description	Data type	Valid range	Required
N	Batch count of output tensor	int	N > 0	Yes
C	Channel count of output tensor	int	C > 0	Yes
H	Height count of output tensor	int	H > 0	Yes
W	Width count of output tensor	int	W > 0	Yes
hOnes	Height of high values region	int	hOnes <= H	Yes
wOnes	Width of high values region	int	wOnes <= W	Yes
any	Any input tensor with desired device and data types for output tensor	tt_lib.tensor.Tensor		Yes
val_hi	High value to use	float		Yes
val_lo	Low value to use	float		Yes

Parameters:

• N (number) – Batch count of output tensor.

• C (number) – Channel count of output tensor.

• H (number) – Height count of output tensor.

• W (number) – Width count of output tensor.

• hOnes (number) – Height of high values region.

• wOnes (number) – Width of high values region.

• any (ttnn.tensor) – Any input tensor with desired device and data types for output tensor. value greater than 0

• val_hi (number) – High value to use.

• val_lo (number) – Low value to use.

Keyword Arguments:

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

• queue_id (int, optional) – command queue id. Defaults to 0.

Returns:

ttnn.Tensor – the output tensor.