For the complete documentation index, see llms.txt. Markdown versions of all pages are available by appending .md to any URL (e.g. /max/get-started.md).
Mojo struct
Conv2dProblemShape
struct Conv2dProblemShape
Defines 2D convolution problem geometry.
Layouts:
- Activation: NHWC (batch, height, width, channels)
- Filter: KRSC (output_channels, filter_h, filter_s, input_channels)
- Output: NHWC (batch, out_height, out_width, output_channels)
For Fprop with stride=1, no dilation, this maps to GEMM as:
- M = N * H_out * W_out
- N = K (output channels)
- K = C * R * S (input channels * filter area)
Fields
- batch (
Int): - in_height (
Int): - in_width (
Int): - in_channels (
Int): - out_channels (
Int): - filter_h (
Int): - filter_w (
Int): - pad_h (
Int): - pad_w (
Int): - stride_h (
Int): - stride_w (
Int): - dilation_h (
Int): - dilation_w (
Int): - groups (
Int):
Implemented traits
AnyType,
Copyable,
ImplicitlyDeletable,
Movable
Methods
__init__
def __init__(out self, batch: Int, in_height: Int, in_width: Int, in_channels: Int, out_channels: Int, filter_h: Int, filter_w: Int, pad_h: Int = Int(0), pad_w: Int = Int(0), stride_h: Int = Int(1), stride_w: Int = Int(1), dilation_h: Int = Int(1), dilation_w: Int = Int(1), groups: Int = Int(1))
out_height
out_width
gemm_m
gemm_n
gemm_k
def gemm_k(self) -> Int
GEMM K dimension = input_channels * filter_height * filter_width.
Returns:
num_m_tiles
num_n_tiles
num_k_tiles
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