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Mojo struct

ConvIm2colParams

struct ConvIm2colParams

Runtime conv geometry for the online im2col A-operand loader.

SM100/Apple M5 (compute_capability == 5). The fused conv path (AppleM5MatMul.run_conv) never materialises the [M, K] im2col matrix; instead each A MMA-fragment is gathered directly from the NHWC input by the address math below. This struct carries the per-launch conv parameters the gather needs, and is DevicePassable (all-Int32 POD, device_type = Self) so it can cross enqueue_function directly.

The (M, K) -> NHWC map mirrors nn/conv/gpu/im2col_matmul_2d.mojo bit-for-bit so the fused result matches the materialised path:

m -> (batch, h_out, w_out): batch = m // HW_out; spatial = m % HW_out; h_out = spatial // W_out; w_out = spatial % W_out k -> (r, s, c): r = k // (SC); sc = k % (SC); s = sc // C; c = sc % C h_in = h_outstride_h - pad_h + r; w_in = w_outstride_w - pad_w + s OOB (h_in/w_in outside [0,H)/[0,W)) -> 0 in_idx = batch*(HWC) + h_in*(WC) + w_inC + c

Dilation is assumed 1 (enforced at the conv dispatch gate); the map matches the materialiser, which also hardcodes dilation 1.

Fields

H (Int32):
W (Int32):
C (Int32):
R (Int32):
S (Int32):
H_out (Int32):
W_out (Int32):
pad_h (Int32):
pad_w (Int32):
stride_h (Int32):
stride_w (Int32):

Implemented traits

AnyType, Copyable, DevicePassable, ImplicitlyCopyable, ImplicitlyDeletable, Movable

`comptime` members

`device_type`

comptime device_type = ConvIm2colParams

Methods

`init`

def __init__(out self)

Zero-init all fields, for the dense matmul path that ignores conv.

`get_type_name`

static def get_type_name() -> String

Returns:

String

Fields​

Implemented traits​

comptime members​

device_type​

Methods​

__init__​

get_type_name​