Raptor/validation/operations

Validation Operations

ONNX test models used by validate.py to verify the Raptor compiler + PIM simulator pipeline.

Generated tests can be regenerated with:

python3 validation/operations/gen_tests.py

Conv

Test	Directory	Input	Output	Kernel	Stride	Padding	Bias	Notes
Simple	conv/simple	[1,3,3,3]	[1,1,2,2]	2x2	1	none	no	Basic conv, hand-crafted
With constant	conv/with_constant	[1,3,3,3]	[1,1,3,3]	2x2	1	SAME_UPPER	yes	Hand-crafted, constant weight+bias
Batch 2	conv/batch_2	[2,3,3,3]	[2,1,3,3]	2x2	1	SAME_UPPER	yes	Batched input
Kernel 3x3	conv/kernel_3x3	[1,1,5,5]	[1,1,3,3]	3x3	1	none	no	Larger kernel
Stride 2	conv/stride_2	[1,1,6,6]	[1,1,2,2]	3x3	2	none	no	Strided convolution
Multi channel	conv/multi_channel	[1,3,5,5]	[1,4,3,3]	3x3	1	none	no	3 in channels, 4 out channels
Pointwise 1x1	conv/pointwise_1x1	[1,8,4,4]	[1,4,4,4]	1x1	1	none	no	Channel mixing
SAME padding 3x3	conv/same_padding_3x3	[1,1,5,5]	[1,1,5,5]	3x3	1	SAME_UPPER	no	Spatial dims preserved
Explicit padding	conv/explicit_padding	[1,1,4,4]	[1,1,4,4]	3x3	1	[1,1,1,1]	no	Symmetric explicit pads
With bias 3x3	conv/with_bias_3x3	[1,3,5,5]	[1,2,3,3]	3x3	1	none	yes	Multi-channel with bias
Large spatial	conv/large_spatial	[1,1,8,8]	[1,1,6,6]	3x3	1	none	no	Larger spatial input
Grouped two groups	conv/grouped_two_groups	[1,4,4,4]	[1,4,4,4]	1x1	1	none	yes	group=2 channel partitioning
Depthwise grouped	conv/depthwise_grouped	[1,3,4,4]	[1,3,2,2]	3x3	1	none	no	group=3, one input channel per group
Dynamic	conv/dynamic	[1,1,4,4]	[1,1,2,2]	3x3	1	none	no	Runtime input and weight

Gemm

Test	Directory	A (input)	B/W tensor	Output	transB	alpha	beta	Bias	Notes
Simple	gemm/simple	[10,132]	[132,132]	[10,132]	no	1	1	no	Square weights
Non-square	gemm/non_square	[4,128]	[128,64]	[4,64]	no	1	1	no	K != N
With bias	gemm/with_bias	[4,128]	[128,128]	[4,128]	no	1	1	[128]	Bias vector
transB	gemm/transB	[4,128]	[64,128]	[4,64]	yes	1	1	no	Transposed weight
Alpha/beta	gemm/alpha_beta	[4,64]	[64,64]	[4,64]	no	0.5	0.25	[64]	Scaled matmul + bias
Small	gemm/small	[2,8]	[8,4]	[2,4]	no	1	1	no	Tiny matrices
Large	gemm/large	[8,256]	[256,128]	[8,128]	no	1	1	no	Larger matrices
transB + bias	gemm/transB_with_bias	[4,128]	[64,128]	[4,64]	yes	1	1	[64]	Combined
Dynamic	gemm/dynamic	[2,8]	[8,4]	[2,4]	no	1	1	no	Runtime matrix operands
Dynamic transB	gemm/dynamic_transB	[2,8]	[4,8]	[2,4]	yes	1	1	no	Runtime transpose handling
Dynamic bias	gemm/dynamic_bias	[2,8]	[8,4]	[2,4]	no	1	1	[4]	Runtime bias broadcast
Dynamic alpha	gemm/dynamic_alpha	[2,8]	[8,4]	[2,4]	no	0.5	1	no	Runtime alpha scaling
Dynamic beta	gemm/dynamic_beta	[2,8]	[8,4]	[2,4]	no	1	2	[4]	Runtime beta scaling
Dynamic bias + scale	gemm/dynamic_bias_alpha_beta	[2,8]	[8,4]	[2,4]	no	0.5	2	[4]	Runtime operands and bias

MatMul

Test	Directory	A input	B tensor	Output	Notes
Basic	matmul/basic	[2,3]	[3,4]	[2,4]	Direct 2D MatMul rewrite path
Left constant	matmul/left_constant	[2,3]	[3,4]	[2,4]	Constant LHS transpose rewrite path
Dynamic	matmul/dynamic	[2,3]	[3,4]	[2,4]	Runtime matrix operands
Batched 3D	matmul/batched_3d	[2,2,3]	[2,3,4]	[2,2,4]	Matching-batch direct batched lowering
Batched 3D dynamic	matmul/batched_3d_dynamic	[2,2,3]	[2,3,4]	[2,2,4]	Batched runtime operands
Batched left const	matmul/batched_left_constant	[2,2,3]	[2,3,4]	[2,2,4]	Batched constant-LHS transpose path
Batched RHS broadcast	matmul/batched_rhs_broadcast	[2,2,3]	[3,4]	[2,2,4]	Rank-2 RHS broadcast across batch
Batched LHS broadcast	matmul/batched_lhs_broadcast	[2,3]	[2,3,4]	[2,2,4]	Rank-2 LHS broadcast across batched RHS

Gemv

Test	Directory	Input	W (weight)	Output	Bias	Notes
Simple	gemv/simple	[1,132]	[132,132]	[1,132]	no	Single-sample matmul
Constant	gemv/constant	(none)	[132,132]	[1,132]	no	All inputs constant
Homogeneous const	gemv/with_homogeneous_constant	[1,132]	[132,132]	[1,132]	[1,132]	Bias matches output shape
Heterogeneous const	gemv/with_heterogeneous_constant	[1,132]	[132,132]	[1,132]	[1,132]	Different constant pattern
Scalar const	gemv/with_scalar_constant	[1,132]	[132,132]	[1,132]	[1,1]	Scalar bias, broadcast

Pool

Test	Directory	Input	Output	Kernel	Stride	Padding	Notes
Max basic	pool/max_basic	[1,1,4,4]	[1,1,3,3]	2x2	1	none	Basic max pooling
Max stride 2 multi-channel	pool/max_stride2_multichannel	[1,5,6,6]	[1,5,3,3]	2x2	2	none	Channel-preserving max pool
Max SAME_UPPER	pool/max_same_upper	[1,1,5,5]	[1,1,3,3]	3x3	2	SAME_UPPER	Deprecated auto_pad path
Avg basic	pool/avg_basic	[1,3,4,4]	[1,3,3,3]	2x2	1	none	Basic average pooling
Avg explicit padding	pool/avg_explicit_padding	[1,2,4,4]	[1,2,2,2]	3x3	2	[1,1,1,1]	count_include_pad=0
Avg include pad	pool/avg_include_pad	[1,2,4,4]	[1,2,2,2]	3x3	2	[1,1,1,1]	count_include_pad=1
Max after Conv	pool/max_after_conv	[1,3,6,6]	[1,4,2,2]	Conv 3x3 then Pool 2x2	2	none	Regression for pool(conv(...))

ReduceMean

Test	Directory	Input	Output	Axes	Keepdims	Notes
Basic	reduce_mean/basic	[4,8]	[4,1]	[1]	1	Reduce feature dimension, preserving rank
Keepdims 0	reduce_mean/keepdims_0	[4,8]	[4]	[1]	0	Reduce feature dimension, dropping reduced axis
4D spatial	reduce_mean/4d_spatial	[1,3,4,4]	[1,3,1,1]	[2,3]	1	Reduce H and W on NCHW input
After Conv	reduce_mean/after_conv	[1,3,5,5]	[1,2,1,1]	[2,3]	1	Conv 3x3 + bias, then spatial ReduceMean

Relu

Test	Directory	Input	Output	Notes
Basic	relu/basic	[4,8]	[4,8]	Standalone 2D Relu
4D	relu/4d	[2,3,4,4]	[2,3,4,4]	Standalone NCHW Relu
After Conv	relu/after_conv	[1,3,5,5]	[1,2,3,3]	Conv 3x3 + bias, then Relu
After Gemm	relu/after_gemm	[4,64]	[4,32]	Gemm + bias, then Relu

Sigmoid

Test	Directory	Input	Output	Notes
Basic	sigmoid/basic	[4,8]	[4,8]	Standalone 2D Sigmoid
4D	sigmoid/4d	[2,3,4,4]	[2,3,4,4]	Standalone NCHW Sigmoid
After Gemm	sigmoid/after_gemm	[4,64]	[4,32]	Gemm + bias, then Sigmoid

Softmax

Test	Directory	Input	Output	Axis	Notes
Basic	softmax/basic	[3,5]	[3,5]	1	Row-wise softmax over features
3D last axis	softmax/3d_last_axis	[2,3,4]	[2,3,4]	2	Last-dimension normalization
Channel axis	softmax/channel_axis	[1,3,2,2]	[1,3,2,2]	1	NCHW channel-wise softmax

Resize

Test	Directory	Input	Output	Mode	Notes
Nearest 2x	resize/nearest_2x	[1,1,2,3]	[1,1,4,6]	nearest	NCHW upsampling with scales [1,1,2,2]
Non-uniform scales	resize/non_uniform	[1,1,2,3]	[1,1,6,6]	nearest	Different height/width scaling factors
Explicit sizes	resize/with_sizes	[1,1,2,3]	[1,1,3,5]	nearest	Sizes input used instead of scales

Split

Test	Directory	Input	Outputs	Axis	Notes
Basic	split/basic	[2,6]	[2,2], [2,4]	1	Two-way split with explicit sizes
Equal three-way	split/equal_three_way	[2,6]	[2,2], [2,2], [2,2]	1	Optional split input omitted

Gather

Test	Directory	Input	Indices	Output	Axis	Notes
Axis 1	gather/axis1	[3,4]	[2]	[3,2]	1	Select two columns
Axis 0 matrix indices	gather/axis0_matrix_indices	[4,3]	[2,2]	[2,2,3]	0	Gather rows with 2D indices

Concat

Test	Directory	Input(s)	Output	Axis	Notes
Channel axis	concat/channel_axis	A:[1,1,2,2], B:[1,2,2,2]	[1,3,2,2]	1	Runtime NCHW channel concat

Reshape

Test	Directory	Input	Output	Notes
Same rank	reshape/same_rank	[2,3]	[3,2]	Runtime tensor with static shape initializer

Add

Test	Directory	Input(s)	Output	Notes
Basic	add/basic	A:[4,8], B:[4,8]	[4,8]	Elementwise add, same-shape inputs
Broadcast row	add/broadcast_row	A:[4,8], B:[8]	[4,8]	Row-vector broadcasting via initializer
After Gemm	add/after_gemm	A:[4,64], D:[32]	[4,32]	Gemm + bias, then Add with broadcast vector

Mul

Test	Directory	Input(s)	Output	Notes
Basic	mul/basic	A:[4,8], B:[4,8]	[4,8]	Elementwise multiply, same-shape inputs
Scalar constant	mul/scalar_constant	X:[4,8], S:[1]	[4,8]	Scalar broadcasting via initializer
After Conv	mul/after_conv	X:[1,3,5,5], S:[1,2,1,1]	[1,2,3,3]	Conv 3x3 + bias, then per-channel scaling

Div

Test	Directory	Input(s)	Output	Notes
Basic	div/basic	X:[4,8], D:[4,8]	[4,8]	Elementwise divide by same-shape constant tensor
Scalar constant	div/scalar_constant	X:[4,8], S:[1]	[4,8]	Scalar broadcasting via initializer
After Gemm	div/after_gemm	A:[4,64], D:[32]	[4,32]	Gemm + bias, then Div with positive broadcast vector