arm_nn_broadcast_walk.h

Machine-readable model

• arm_nn_broadcast_dim_validfunctionCheck that one NHWC dimension of two operands broadcasts to the output dimension.
• arm_nn_broadcast_dims_validfunctionCheck that two NHWC operands broadcast to the given output shape.
• ARM_NN_BROADCAST_WALK_NHWCmacroWalk an NHWC broadcast of two operands, calling a contiguous kernel on each run.

function

arm_nn_broadcast_dim_valid

C

Check that one NHWC dimension of two operands broadcasts to the output dimension.

Include/Internal/arm_nn_broadcast_walk.h:34

static int32_t arm_nn_broadcast_dim_valid(const int32_t dim_1, const int32_t dim_2, const int32_t dim_out)

Check that one NHWC dimension of two operands broadcasts to the output dimension.

TensorFlow Lite broadcast rules: both inputs must be at least 1 (an empty tensor is rejected rather than treated as a no-op), they must be equal or one of them must be 1, and the output dimension must be the larger of the two.

Parameters

Parameters of arm_nn_broadcast_dim_valid

Name	Type	Description
dim_1	const int32_t
dim_2	const int32_t
dim_out	const int32_t

function

arm_nn_broadcast_dims_valid

C

Check that two NHWC operands broadcast to the given output shape.

Include/Internal/arm_nn_broadcast_walk.h:54

static int32_t arm_nn_broadcast_dims_valid(
    const cmsis_nn_dims *dims_1,
    const cmsis_nn_dims *dims_2,
    const cmsis_nn_dims *dims_out
)

Check that two NHWC operands broadcast to the given output shape.

Every kernel that uses ARM_NN_BROADCAST_WALK_NHWC must reject arguments that fail this check, since the walk indexes each input by its own dims and writes the output by the output dims.

Parameters

Parameters of arm_nn_broadcast_dims_valid

Name	Type	Description
dims_1	const cmsis_nn_dims *
dims_2	const cmsis_nn_dims *
dims_out	const cmsis_nn_dims *

macro

ARM_NN_BROADCAST_WALK_NHWC

C

Walk an NHWC broadcast of two operands, calling a contiguous kernel on each run.

Include/Internal/arm_nn_broadcast_walk.h:89

#define ARM_NN_BROADCAST_WALK_NHWC(IN_TYPE, OUT_TYPE, in_1, dims_1, in_2, dims_2, out, dims_out, FULL, SCALAR_1, SCALAR_2)

Walk an NHWC broadcast of two operands, calling a contiguous kernel on each run.

Each input is indexed by its own dims: a dimension of 1 has stride 0 and is broadcast, any other dimension equals the output dimension and strides normally. The longest contiguous run whose shapes agree is handed to the caller’s kernels, so the common cases (identical shapes, a single scalar, per-batch, per-row, per-channel) each cost one call per run.

Preconditions: arm_nn_broadcast_dims_valid(dims_1, dims_2, dims_out) is non-zero.

Parameters

Parameters of ARM_NN_BROADCAST_WALK_NHWC

Name	Description
IN_TYPE	element type of the inputs
OUT_TYPE	element type of the output
in_1	const IN_TYPE * first input
dims_1	const `cmsis_nn_dims` * dims of in_1
in_2	const IN_TYPE * second input
dims_2	const `cmsis_nn_dims` * dims of in_2
out	OUT_TYPE * output, sized by dims_out
dims_out	const `cmsis_nn_dims` * broadcast output dims (see arm_nn_broadcast_dims_valid)
FULL	FULL(const IN_TYPE *a, const IN_TYPE *b, OUT_TYPE *o, int32_t n) elementwise kernel over n elements of a and b
SCALAR_1	SCALAR_1(const IN_TYPE *scalar, const IN_TYPE *vec, OUT_TYPE *o, int32_t n) kernel where *scalar is one element of in_1 broadcast against n elements of in_2
SCALAR_2	SCALAR_2(const IN_TYPE *scalar, const IN_TYPE *vec, OUT_TYPE *o, int32_t n) kernel where *scalar is one element of in_2 broadcast against n elements of in_1; note the operands arrive in reversed order, so an asymmetric kernel must swap them