Raptor/validation/operations/INVENTORY.md

Operation test inventory

All 177 checked-in ONNX models, grouped by validator category. Each row describes the regression or operation feature exercised; follow its link for the model. Generation and validation commands remain in the short README.

gen_tests.py defines generated shapes, attributes, constants, and seeds. It regenerates 160 cases; 17 fixtures are checked-in only: the five Gemv cases, mul/after_conv_scalar_constant, and eleven Conv cases (the six strategy_* cases plus input_224_7x7_stride2, kernel_2x2, output_channel_grouping_minimal, relu_conv_store, with_constant). Do not infer the complete inventory from generator output alone. Strategy fixtures target particular lowering paths; actual selection depends on the hardware profile and compiler flags.

Add (5) — add/

Case	Description
after_gemm	Gemm followed by Add with a broadcast bias vector.
basic	Elementwise Add on two inputs with identical shapes.
broadcast_row	Elementwise Add with row-vector broadcasting.
channel_broadcast_1024	NCHW per-channel broadcasting over 1024 channels.
leading_dimension_broadcast	Trailing-dimension broadcasting across leading dimensions.

Concat (3) — concat/

Case	Description
channel_axis	Concatenates two runtime NCHW tensors along the channel axis.
negative_axis	Concatenates tensors using a negative axis.
three_inputs_channel_axis	Concatenates three runtime NCHW tensors along the channel axis.

Conv (42) — conv/

Case	Description
batch_2	Batched Conv with SAME_UPPER padding and bias.
batch_4_pointwise	Pointwise Conv with batch size four.
input_224_7x7_stride2	224x224 RGB Conv with 64 output channels, a 7x7 kernel, stride two, and bias.
depthwise_1024_channels	Depthwise pointwise Conv with 1024 groups.
depthwise_grouped	Depthwise-style grouped Conv with one input channel per group.
dilated_3x3	Conv with a dilated 3x3 kernel.
dynamic	Conv with runtime input and weight tensors.
explicit_padding	3x3 Conv with symmetric explicit padding.
grouped_many_groups	Pointwise Conv with many groups and high channel counts.
grouped_two_groups	Two-group pointwise Conv with bias.
huge_pointwise_1024	Pointwise Conv with 1024 input and output channels.
huge_pointwise_1024_dynamic	The 1024-channel pointwise Conv with runtime weights.
kernel_3x3	Basic 3x3 Conv without padding.
kernel_equals_input_spatial	Conv whose kernel covers the full spatial input.
large_input_channels_1x1	Pointwise Conv with 1024 input channels and modest output width.
large_output_channels_1x1	Pointwise Conv with modest input width and 1024 output channels.
large_spatial	3x3 Conv on a larger spatial input.
multi_channel	3x3 Conv with multiple input and output channels.
non_square_kernel_1x3	Conv with a non-square 1x3 kernel.
non_square_kernel_3x1	Conv with a non-square 3x1 kernel.
non_uniform_stride	Conv with different height and width strides.
output_channel_grouping_minimal	Minimal 64-to-256 pointwise Conv for output-channel grouping.
pointwise_1x1	Basic pointwise channel-mixing Conv.
pointwise_tiled_chain	Relu and chained pointwise Convs with a tiled intermediate.
real_asymmetric_padding	Conv with asymmetric explicit padding.
relu_conv_store	Conv, Relu, and a second Conv to validate an intermediate stored result.
same_lower_3x3	3x3 Conv with SAME_LOWER padding.
same_padding_3x3	3x3 Conv with SAME_UPPER padding.
kernel_2x2	Hand-authored Conv with a 2x2 kernel.
stride_2	3x3 Conv with stride two.
strategy_depthwise_16	16-channel depthwise 3x3 Conv for depthwise lowering.
strategy_input_k_tiled	32-channel 3x3 Conv sized to exercise input-K tiling.
strategy_output_channel_tiled	8-to-192 3x3 Conv sized to exercise output-channel tiling.
strategy_streamed_packed	3-to-16 3x3 Conv on 128x128 input for streamed packed lowering.
strategy_streamed_patch	3-to-16 3x3 Conv on 64x64 input for streamed patch lowering.
strategy_tiled_2d	32-to-192 3x3 Conv sized to exercise 2D tiling.
with_bias_3x3	Multi-channel 3x3 Conv with bias.
with_constant	Hand-authored SAME_UPPER Conv with constant weight and bias.
without_kernel_shape_attr	Conv whose kernel shape is inferred from its weight tensor.
yolo11n_depthwise_head	YOLO11n pointwise-to-depthwise head boundary at 80x80, preserving row fragments.
yolo11n_heavy	Two largest standard YOLO11n Conv-SiLU blocks by MAC count at 64x80x80.
yolo11n_stem	First two YOLO11n Conv-SiLU blocks at 640x640, including the distributed activation boundary.

Div (6) — div/

Case	Description
after_gemm	Gemm followed by Div with a broadcast divisor vector.
basic	Elementwise Div by a same-shape constant tensor.
channel_broadcast_1024	Div with NCHW per-channel broadcasting over 1024 channels.
leading_dimension_broadcast	Div with trailing-dimension broadcasting.
runtime_scalar_rhs	Div of a runtime tensor by a scalar initializer.
scalar_constant	Div with scalar broadcasting on a 2D tensor.

Gather (5) — gather/

Case	Description
3d_input_axis1	Gathers along axis one of a 3D input.
axis0_matrix_indices	Gathers rows using a 2D indices tensor.
axis1	Gathers selected columns from a 2D tensor.
negative_axis	Gathers using a negative axis.
negative_indices	Gathers with negative indices along axis zero.

Gemm (21) — gemm/

Case	Description
alpha_beta	Applies non-default alpha and beta scaling with bias.
bias_rank2_broadcast	Broadcasts a rank-2 bias across output rows.
dynamic	Uses both matrix operands at runtime.
dynamic_alpha	Uses runtime operands with non-default alpha scaling.
dynamic_beta	Uses runtime operands and bias with non-default beta scaling.
dynamic_bias	Uses runtime matrix operands and runtime bias.
dynamic_bias_alpha_beta	Combines runtime operands and bias with alpha and beta scaling.
dynamic_transpose_b	Transposes a runtime right-hand matrix.
huge_1024	Uses 1024-wide inner and output dimensions.
large	Exercises larger rectangular matrices.
large_k_small_n	Uses a large reduction dimension and narrow output.
non_square	Uses different reduction and output widths.
scalar_bias	Broadcasts a scalar bias to the full output.
square_weights	Basic Gemm with square weights.
small	Tiny Gemm for fast focused validation.
small_k_large_n	Uses a modest reduction dimension and wide output.
transpose_a	Transposes the left-hand matrix.
transpose_a_and_b	Transposes both matrix operands.
transpose_b	Transposes the right-hand weight matrix.
transpose_b_with_bias	Combines a transposed weight matrix with bias.
with_bias	Basic matrix product with vector bias.

Gemv (5) — gemv/

Case	Description
all_constant	Vector-matrix product with all inputs constant.
constant_weight	Basic single-row vector-matrix product with constant weights.
non_uniform_bias	Adds a non-uniform constant bias pattern.
uniform_bias	Adds a uniform constant bias pattern.
scalar_bias	Adds a scalar broadcast bias.

MatMul (12) — matmul/

Case	Description
basic	Direct 2D MatMul with constant right-hand matrix.
batched_3d	Batched 3D MatMul with matching batch dimensions.
batched_3d_dynamic	Batched 3D MatMul with both operands at runtime.
batched_left_constant	Batched 3D MatMul with constant left-hand matrix.
batched_lhs_broadcast	Broadcasts a 2D left-hand matrix across a batched right-hand tensor.
batched_rhs_broadcast	Broadcasts a 2D right-hand matrix across a batched left-hand tensor.
dynamic	Direct 2D MatMul with both operands at runtime.
huge_1024	Uses 1024-wide inner and output dimensions.
left_constant	Direct 2D MatMul with constant left-hand matrix.
matrix_vector	Matrix-vector multiplication producing a 1D output.
vector_matrix	Vector-matrix multiplication producing a 1D output.
yolo_attention	YOLO11n rank-4 dynamic MatMul-scale-transpose-MatMul attention chain.

Mul (6) — mul/

Case	Description
after_conv	Conv followed by per-channel scaling.
after_conv_scalar_constant	Conv followed by Mul with a scalar constant.
basic	Elementwise Mul on two inputs with identical shapes.
channel_broadcast_1024	Mul with NCHW per-channel broadcasting over 1024 channels.
leading_dimension_broadcast	Mul with trailing-dimension broadcasting.
scalar_constant	Mul with scalar broadcasting.

Pool (15) — pool/

Case	Description
avg_basic	AveragePool with a 2x2 kernel and unit stride.
avg_ceil_mode	AveragePool with ceil mode enabled.
avg_explicit_padding	Explicitly padded AveragePool excluding pad from the divisor.
avg_include_pad	Explicitly padded AveragePool including pad in the divisor.
avg_large_channels	AveragePool with a large channel count and small spatial extent.
avg_non_uniform_stride	AveragePool with different height and width strides.
avg_real_asymmetric_padding	AveragePool with asymmetric explicit padding.
max_after_conv	Conv followed by MaxPool.
max_basic	MaxPool with a 2x2 kernel and unit stride.
max_ceil_mode	MaxPool with ceil mode enabled.
max_global_style_kernel_equals_input	MaxPool whose kernel covers the full spatial input.
max_non_square_kernel	MaxPool with a non-square kernel.
max_real_asymmetric_padding	MaxPool with asymmetric explicit padding.
max_same_upper	MaxPool with SAME_UPPER padding.
max_stride2_multichannel	Multi-channel MaxPool with stride two.

ReduceMean (17) — reduce_mean/

Case	Description
4d_spatial	Reduces height and width of an NCHW tensor while preserving rank.
4d_spatial_keepdims_0	Reduces NCHW height and width while dropping those axes.
after_conv	Conv followed by a spatial ReduceMean.
all_axes_keepdims_0	Reduces all axes to a scalar.
all_axes_keepdims_1	Reduces all axes while preserving rank.
basic	Reduces a feature dimension while preserving rank.
channel_axis_nchw	Reduces the channel axis of an NCHW tensor.
keepdims_0	Reduces a feature dimension and drops that axis.
large_dimension_1024	Reduces a dimension of length 1024.
legacy_axes_1_2_keepdims_1	Opset-18 reduction over multiple positive axes.
legacy_axis1_keepdims_0	Opset-18 reduction over one axis while dropping it.
legacy_axis1_keepdims_1	Opset-18 reduction over one axis while preserving rank.
legacy_empty_axes_noop	Opset-18 empty-axes no-op followed by Relu.
legacy_nchw_spatial	Opset-18 spatial reduction on NCHW input.
legacy_negative_axis	Opset-18 reduction using a negative axis.
legacy_reduce_all_keepdims_1	Opset-18 all-axis reduction with the axes input omitted.
negative_axis	ReduceMean using a negative axis.

Relu (4) — relu/

Case	Description
4d	Standalone Relu on an NCHW tensor.
after_conv	Conv followed by Relu.
after_gemm	Gemm followed by Relu.
basic	Standalone Relu on a 2D tensor.

Reshape (4) — reshape/

Case	Description
4d_to_2d_flatten	Flattens a 4D tensor into a 2D view.
infer_dim_minus_one	Uses -1 to infer one output dimension.
same_rank	Changes shape without changing rank.
zero_copies_input_dim	Uses 0 to copy an input dimension.

Resize (6) — resize/

Case	Description
height_only	Nearest-neighbor resize of only the height dimension.
nearest_2x	Nearest-neighbor upsampling by a factor of two.
nearest_downsample	Nearest-neighbor downsampling.
non_uniform_scales	Nearest-neighbor resize with different spatial scales.
width_only	Nearest-neighbor resize of only the width dimension.
with_sizes	Resize using explicit output sizes instead of scales.

Sigmoid (3) — sigmoid/

Case	Description
4d	Standalone Sigmoid on an NCHW tensor.
after_gemm	Gemm followed by Sigmoid.
basic	Standalone Sigmoid on a 2D tensor.

Slice (8) — slice/

Case	Description
2d_basic	Slices a 2D tensor with explicit axes and unit steps.
after_conv	Conv followed by a spatial crop.
default_axes	Omits axes and steps to use positional defaults.
large_channel_1024	Slices a channel range from a 1024-channel tensor.
nchw_spatial_crop	Crops the spatial axes of an NCHW tensor.
negative_axis	Slices using a negative axis.
negative_indices	Slices using negative indices.
step2	Slices using a positive step greater than one.

Softmax (5) — softmax/

Case	Description
3d_last_axis	Softmax over the last axis of a 3D tensor.
basic	Softmax over the last dimension of a 2D tensor.
channel_axis	Softmax over the channel axis of an NCHW tensor.
large_dimension_1024	Softmax over a last dimension of length 1024.
negative_axis	Softmax using a negative axis.

Split (4) — split/

Case	Description
basic	Splits a 2D tensor into two explicit output sizes.
equal_three_way	Splits a 2D tensor evenly into three outputs.
negative_axis	Splits using a negative axis.
uneven_channel_axis_4d	Splits an NCHW channel axis into uneven outputs.

Sub (6) — sub/

Case	Description
after_gemm	Gemm followed by Sub with a broadcast constant vector.
basic	Elementwise Sub on two runtime inputs with identical shapes.
broadcast_row	Sub with a broadcast row-vector right-hand constant.
channel_broadcast_1024	Sub with NCHW per-channel broadcasting over 1024 channels.
constant_lhs_broadcast	Sub with a broadcast constant left-hand operand.
leading_dimension_broadcast	Sub with trailing-dimension broadcasting.