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Mojo struct

ConvShape

struct ConvShape[rank: Int]

A shape struct describing the convolution dimensions.

Parameters

• ​rank (Int): Spatial rank of the convolution (1, 2, or 3).

Fields

• ​n (Int):
• ​input_dims (Coord[*#kgen.param_list.tabulate(rank, [idx: __mlir_type.index] Int64)]):
• ​output_dims (Coord[*#kgen.param_list.tabulate(rank, [idx: __mlir_type.index] Int64)]):
• ​filter_dims (Coord[*#kgen.param_list.tabulate(rank, [idx: __mlir_type.index] Int64)]):
• ​c (Int):
• ​f (Int):
• ​stride (Coord[*#kgen.param_list.tabulate(rank, [idx: __mlir_type.index] Int64)]):
• ​dilation (Coord[*#kgen.param_list.tabulate(rank, [idx: __mlir_type.index] Int64)]):
• ​pad_d (Coord[Int64, Int64]):
• ​pad_h (Coord[Int64, Int64]):
• ​pad_w (Coord[Int64, Int64]):
• ​num_groups (Int):

Implemented traits

AnyType, Copyable, Deinitable, ImplicitlyCopyable, Movable, RegisterPassable, TrivialRegisterPassable

Methods

__init__

def __init__(n: Int, input_dims: Coord[*#kgen.param_list.tabulate(rank, [idx: __mlir_type.index] Int64)], output_dims: Coord[*#kgen.param_list.tabulate(rank, [idx: __mlir_type.index] Int64)], filter_dims: Coord[*#kgen.param_list.tabulate(rank, [idx: __mlir_type.index] Int64)], c: Int, f: Int, stride: Coord[*#kgen.param_list.tabulate(rank, [idx: __mlir_type.index] Int64)], dilation: Coord[*#kgen.param_list.tabulate(rank, [idx: __mlir_type.index] Int64)], pad_d: Coord[Int64, Int64], pad_h: Coord[Int64, Int64], pad_w: Coord[Int64, Int64], num_groups: Int) -> Self

d

def d(self) -> Int

Input depth.

Returns:

Int

h

def h(self) -> Int

Input height.

Returns:

Int

w

def w(self) -> Int

Input width.

Returns:

Int

do

def do(self) -> Int

Output depth.

Returns:

Int

ho

def ho(self) -> Int

Output height.

Returns:

Int

wo

def wo(self) -> Int

Output width.

Returns:

Int

q

def q(self) -> Int

Filter window depth.

Returns:

Int

r

def r(self) -> Int

Filter window height.

Returns:

Int

s

def s(self) -> Int

Filter window width.

Returns:

Int

stride_at

def stride_at[axis: Int](self) -> Int

Stride along axis.

Parameters:

• ​axis (Int): Spatial axis to read.

Returns:

Int

dilation_at

def dilation_at[axis: Int](self) -> Int

Dilation along axis.

Parameters:

• ​axis (Int): Spatial axis to read.

Returns:

Int

pad_d_lower

def pad_d_lower(self) -> Int

Depth padding before the first input point.

Returns:

Int

pad_h_lower

def pad_h_lower(self) -> Int

Height padding above the first input row.

Returns:

Int

pad_w_lower

def pad_w_lower(self) -> Int

Width padding left of the first input column.

Returns:

Int

filter_window_flat_size

def filter_window_flat_size(self) -> Int

Returns:

Int

input_image_flat_size

def input_image_flat_size(self) -> Int

Returns:

Int

output_image_flat_size

def output_image_flat_size(self) -> Int

Returns:

Int

output_space_dims

def output_space_dims(self) -> IndexList[rank]

Returns:

IndexList[rank]

output_flat_coord_to_input_offset

def output_flat_coord_to_input_offset(self, n: Int, output_flat_coord: Int) -> Int

Returns:

Int

matmul_M

def matmul_M(self) -> Int

Returns:

Int

matmul_N

def matmul_N(self) -> Int

Returns:

Int

matmul_K

def matmul_K(self) -> Int

Returns:

Int

padded

def padded(self) -> Bool

Returns:

Bool

c_per_group

def c_per_group(self) -> Int

Returns the number of channels per group. Channel count must be divisible by group size.

Returns:

Int

f_per_group

def f_per_group(self) -> Int

Returns the number of filters per group. Filter count must be divisible by group size.

Returns:

Int

f_to_group

def f_to_group(self, f_idx: Int) -> Int

Given a global filter idx, returns the group idx of the group the filter belongs to.

Args:

• ​f_idx (Int): Global filter index across all groups.

Returns:

Int

c_to_group

def c_to_group(self, c_idx: Int) -> Int

Given a global channel idx, returns the group idx of the group the channel belongs to.

Args:

• ​c_idx (Int): Global channel index across all groups.

Returns:

Int

f_in_group

def f_in_group(self, f_idx: Int) -> Int

Given a global filter idx, returns the offset of the filter in its group.

Args:

• ​f_idx (Int): Global filter index across all groups.

Returns:

Int

c_in_group

def c_in_group(self, c_idx: Int) -> Int

Given a global channel idx, returns the offset of the channel in its group.

Args:

• ​c_idx (Int): Global channel index across all groups.

Returns:

Int