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

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

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

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(...))`

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

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

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

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

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

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