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

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(...))

Gemm

Test	Directory	A (input)	W (weight)	Output	transB	alpha	beta	Bias	Notes
Default	gemm/	[10,132]	[132,132]	[10,132]	no	1	1	no	Hand-crafted, 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

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