add support for operations: reduceMean, add, mul, div, sigmoid
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NiccoloN
@@ -3,66 +3,108 @@
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ONNX test models used by `validate.py` to verify the Raptor compiler + PIM simulator pipeline.
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Generated tests can be regenerated with:
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```
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python3 validation/operations/gen_tests.py
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```
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## Conv
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| Test | Directory | Input | Output | Kernel | Stride | Padding | Bias | Notes |
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|------|-----------|-------|--------|--------|--------|---------|------|-------|
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| Simple | `conv/simple` | [1,3,3,3] | [1,1,2,2] | 2x2 | 1 | none | no | Basic conv, hand-crafted |
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| With constant | `conv/with_constant` | [1,3,3,3] | [1,1,3,3] | 2x2 | 1 | SAME_UPPER | yes | Hand-crafted, constant weight+bias |
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| Batch 2 | `conv/batch_2` | [2,3,3,3] | [2,1,3,3] | 2x2 | 1 | SAME_UPPER | yes | Batched input |
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| Kernel 3x3 | `conv/kernel_3x3` | [1,1,5,5] | [1,1,3,3] | 3x3 | 1 | none | no | Larger kernel |
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| Stride 2 | `conv/stride_2` | [1,1,6,6] | [1,1,2,2] | 3x3 | 2 | none | no | Strided convolution |
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| Multi channel | `conv/multi_channel` | [1,3,5,5] | [1,4,3,3] | 3x3 | 1 | none | no | 3 in channels, 4 out channels |
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| Pointwise 1x1 | `conv/pointwise_1x1` | [1,8,4,4] | [1,4,4,4] | 1x1 | 1 | none | no | Channel mixing |
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| SAME padding 3x3 | `conv/same_padding_3x3` | [1,1,5,5] | [1,1,5,5] | 3x3 | 1 | SAME_UPPER | no | Spatial dims preserved |
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| Explicit padding | `conv/explicit_padding` | [1,1,4,4] | [1,1,4,4] | 3x3 | 1 | [1,1,1,1] | no | Symmetric explicit pads |
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| With bias 3x3 | `conv/with_bias_3x3` | [1,3,5,5] | [1,2,3,3] | 3x3 | 1 | none | yes | Multi-channel with bias |
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| Large spatial | `conv/large_spatial` | [1,1,8,8] | [1,1,6,6] | 3x3 | 1 | none | no | Larger spatial input |
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## Pool
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| Test | Directory | Input | Output | Kernel | Stride | Padding | Notes |
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|------|-----------|-------|--------|--------|--------|---------|-------|
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| Max basic | `pool/max_basic` | [1,1,4,4] | [1,1,3,3] | 2x2 | 1 | none | Basic max pooling |
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| Max stride 2 multi-channel | `pool/max_stride2_multichannel` | [1,5,6,6] | [1,5,3,3] | 2x2 | 2 | none | Channel-preserving max pool |
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| Max SAME_UPPER | `pool/max_same_upper` | [1,1,5,5] | [1,1,3,3] | 3x3 | 2 | SAME_UPPER | Deprecated auto_pad path |
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| Avg basic | `pool/avg_basic` | [1,3,4,4] | [1,3,3,3] | 2x2 | 1 | none | Basic average pooling |
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| 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` |
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| 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` |
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| 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(...))` |
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## Relu
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| Test | Directory | Input | Output | Notes |
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|------|-----------|-------|--------|-------|
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| Basic | `relu/basic` | [4,8] | [4,8] | Standalone 2D Relu |
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| 4D | `relu/4d` | [2,3,4,4] | [2,3,4,4] | Standalone NCHW Relu |
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| After Conv | `relu/after_conv` | [1,3,5,5] | [1,2,3,3] | Conv 3x3 + bias, then Relu |
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| After Gemm | `relu/after_gemm` | [4,64] | [4,32] | Gemm + bias, then Relu |
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| Test | Directory | Input | Output | Kernel | Stride | Padding | Bias | Notes |
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|------------------|-------------------------|-----------|-----------|--------|--------|------------|------|------------------------------------|
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| Simple | `conv/simple` | [1,3,3,3] | [1,1,2,2] | 2x2 | 1 | none | no | Basic conv, hand-crafted |
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| With constant | `conv/with_constant` | [1,3,3,3] | [1,1,3,3] | 2x2 | 1 | SAME_UPPER | yes | Hand-crafted, constant weight+bias |
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| Batch 2 | `conv/batch_2` | [2,3,3,3] | [2,1,3,3] | 2x2 | 1 | SAME_UPPER | yes | Batched input |
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| Kernel 3x3 | `conv/kernel_3x3` | [1,1,5,5] | [1,1,3,3] | 3x3 | 1 | none | no | Larger kernel |
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| Stride 2 | `conv/stride_2` | [1,1,6,6] | [1,1,2,2] | 3x3 | 2 | none | no | Strided convolution |
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| Multi channel | `conv/multi_channel` | [1,3,5,5] | [1,4,3,3] | 3x3 | 1 | none | no | 3 in channels, 4 out channels |
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| Pointwise 1x1 | `conv/pointwise_1x1` | [1,8,4,4] | [1,4,4,4] | 1x1 | 1 | none | no | Channel mixing |
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| SAME padding 3x3 | `conv/same_padding_3x3` | [1,1,5,5] | [1,1,5,5] | 3x3 | 1 | SAME_UPPER | no | Spatial dims preserved |
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| Explicit padding | `conv/explicit_padding` | [1,1,4,4] | [1,1,4,4] | 3x3 | 1 | [1,1,1,1] | no | Symmetric explicit pads |
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| With bias 3x3 | `conv/with_bias_3x3` | [1,3,5,5] | [1,2,3,3] | 3x3 | 1 | none | yes | Multi-channel with bias |
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| Large spatial | `conv/large_spatial` | [1,1,8,8] | [1,1,6,6] | 3x3 | 1 | none | no | Larger spatial input |
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## Gemm
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| Test | Directory | A (input) | W (weight) | Output | transB | alpha | beta | Bias | Notes |
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|------|-----------|-----------|------------|--------|--------|-------|------|------|-------|
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| Default | `gemm/` | [10,132] | [132,132] | [10,132] | no | 1 | 1 | no | Hand-crafted, square weights |
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| Non-square | `gemm/non_square` | [4,128] | [128,64] | [4,64] | no | 1 | 1 | no | K != N |
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| With bias | `gemm/with_bias` | [4,128] | [128,128] | [4,128] | no | 1 | 1 | [128] | Bias vector |
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| transB | `gemm/transB` | [4,128] | [64,128] | [4,64] | yes | 1 | 1 | no | Transposed weight |
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| Alpha/beta | `gemm/alpha_beta` | [4,64] | [64,64] | [4,64] | no | 0.5 | 0.25 | [64] | Scaled matmul + bias |
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| Small | `gemm/small` | [2,8] | [8,4] | [2,4] | no | 1 | 1 | no | Tiny matrices |
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| Large | `gemm/large` | [8,256] | [256,128] | [8,128] | no | 1 | 1 | no | Larger matrices |
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| transB + bias | `gemm/transB_with_bias` | [4,128] | [64,128] | [4,64] | yes | 1 | 1 | [64] | Combined |
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| Test | Directory | A (input) | W (weight) | Output | transB | alpha | beta | Bias | Notes |
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|---------------|-------------------------|-----------|------------|----------|--------|-------|------|-------|------------------------------|
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| Default | `gemm/` | [10,132] | [132,132] | [10,132] | no | 1 | 1 | no | Hand-crafted, square weights |
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| Non-square | `gemm/non_square` | [4,128] | [128,64] | [4,64] | no | 1 | 1 | no | K != N |
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| With bias | `gemm/with_bias` | [4,128] | [128,128] | [4,128] | no | 1 | 1 | [128] | Bias vector |
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| transB | `gemm/transB` | [4,128] | [64,128] | [4,64] | yes | 1 | 1 | no | Transposed weight |
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| Alpha/beta | `gemm/alpha_beta` | [4,64] | [64,64] | [4,64] | no | 0.5 | 0.25 | [64] | Scaled matmul + bias |
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| Small | `gemm/small` | [2,8] | [8,4] | [2,4] | no | 1 | 1 | no | Tiny matrices |
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| Large | `gemm/large` | [8,256] | [256,128] | [8,128] | no | 1 | 1 | no | Larger matrices |
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| transB + bias | `gemm/transB_with_bias` | [4,128] | [64,128] | [4,64] | yes | 1 | 1 | [64] | Combined |
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## Gemv
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| Test | Directory | Input | W (weight) | Output | Bias | Notes |
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|------|-----------|-------|------------|--------|------|-------|
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| Simple | `gemv/simple` | [1,132] | [132,132] | [1,132] | no | Single-sample matmul |
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| Constant | `gemv/constant` | _(none)_ | [132,132] | [1,132] | no | All inputs constant |
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| Homogeneous const | `gemv/with_homogeneous_constant` | [1,132] | [132,132] | [1,132] | [1,132] | Bias matches output shape |
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| Heterogeneous const | `gemv/with_heterogeneous_constant` | [1,132] | [132,132] | [1,132] | [1,132] | Different constant pattern |
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| Scalar const | `gemv/with_scalar_constant` | [1,132] | [132,132] | [1,132] | [1,1] | Scalar bias, broadcast |
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| Test | Directory | Input | W (weight) | Output | Bias | Notes |
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|---------------------|------------------------------------|----------|------------|---------|---------|----------------------------|
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| Simple | `gemv/simple` | [1,132] | [132,132] | [1,132] | no | Single-sample matmul |
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| Constant | `gemv/constant` | _(none)_ | [132,132] | [1,132] | no | All inputs constant |
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| Homogeneous const | `gemv/with_homogeneous_constant` | [1,132] | [132,132] | [1,132] | [1,132] | Bias matches output shape |
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| Heterogeneous const | `gemv/with_heterogeneous_constant` | [1,132] | [132,132] | [1,132] | [1,132] | Different constant pattern |
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| Scalar const | `gemv/with_scalar_constant` | [1,132] | [132,132] | [1,132] | [1,1] | Scalar bias, broadcast |
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## Pool
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| Test | Directory | Input | Output | Kernel | Stride | Padding | Notes |
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|----------------------------|---------------------------------|-----------|-----------|------------------------|--------|------------|----------------------------------|
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| Max basic | `pool/max_basic` | [1,1,4,4] | [1,1,3,3] | 2x2 | 1 | none | Basic max pooling |
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| Max stride 2 multi-channel | `pool/max_stride2_multichannel` | [1,5,6,6] | [1,5,3,3] | 2x2 | 2 | none | Channel-preserving max pool |
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| Max SAME_UPPER | `pool/max_same_upper` | [1,1,5,5] | [1,1,3,3] | 3x3 | 2 | SAME_UPPER | Deprecated auto_pad path |
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| Avg basic | `pool/avg_basic` | [1,3,4,4] | [1,3,3,3] | 2x2 | 1 | none | Basic average pooling |
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| 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` |
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| 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` |
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| 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(...))` |
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## ReduceMean
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| Test | Directory | Input | Output | Axes | Keepdims | Notes |
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|------------|--------------------------|-----------|-----------|-------|----------|-------------------------------------------------|
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| Basic | `reduce_mean/basic` | [4,8] | [4,1] | [1] | 1 | Reduce feature dimension, preserving rank |
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| Keepdims 0 | `reduce_mean/keepdims_0` | [4,8] | [4] | [1] | 0 | Reduce feature dimension, dropping reduced axis |
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| 4D spatial | `reduce_mean/4d_spatial` | [1,3,4,4] | [1,3,1,1] | [2,3] | 1 | Reduce H and W on NCHW input |
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| After Conv | `reduce_mean/after_conv` | [1,3,5,5] | [1,2,1,1] | [2,3] | 1 | Conv 3x3 + bias, then spatial ReduceMean |
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## Relu
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| Test | Directory | Input | Output | Notes |
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|------------|-------------------|-----------|-----------|----------------------------|
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| Basic | `relu/basic` | [4,8] | [4,8] | Standalone 2D Relu |
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| 4D | `relu/4d` | [2,3,4,4] | [2,3,4,4] | Standalone NCHW Relu |
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| After Conv | `relu/after_conv` | [1,3,5,5] | [1,2,3,3] | Conv 3x3 + bias, then Relu |
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| After Gemm | `relu/after_gemm` | [4,64] | [4,32] | Gemm + bias, then Relu |
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## Sigmoid
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| Test | Directory | Input | Output | Notes |
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|------------|----------------------|-----------|-----------|---------------------------|
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| Basic | `sigmoid/basic` | [4,8] | [4,8] | Standalone 2D Sigmoid |
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| 4D | `sigmoid/4d` | [2,3,4,4] | [2,3,4,4] | Standalone NCHW Sigmoid |
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| After Gemm | `sigmoid/after_gemm` | [4,64] | [4,32] | Gemm + bias, then Sigmoid |
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## Add
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| Test | Directory | Input(s) | Output | Notes |
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|---------------|---------------------|------------------|--------|---------------------------------------------|
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| Basic | `add/basic` | A:[4,8], B:[4,8] | [4,8] | Elementwise add, same-shape inputs |
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| Broadcast row | `add/broadcast_row` | A:[4,8], B:[8] | [4,8] | Row-vector broadcasting via initializer |
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| After Gemm | `add/after_gemm` | A:[4,64], D:[32] | [4,32] | Gemm + bias, then Add with broadcast vector |
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## Mul
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| Test | Directory | Input(s) | Output | Notes |
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|-----------------|-----------------------|--------------------------|-----------|-------------------------------------------|
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| Basic | `mul/basic` | A:[4,8], B:[4,8] | [4,8] | Elementwise multiply, same-shape inputs |
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| Scalar constant | `mul/scalar_constant` | X:[4,8], S:[1] | [4,8] | Scalar broadcasting via initializer |
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| 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 |
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## Div
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| Test | Directory | Input(s) | Output | Notes |
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|-----------------|-----------------------|------------------|--------|------------------------------------------------------|
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| Basic | `div/basic` | X:[4,8], D:[4,8] | [4,8] | Elementwise divide by same-shape constant tensor |
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| Scalar constant | `div/scalar_constant` | X:[4,8], S:[1] | [4,8] | Scalar broadcasting via initializer |
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| After Gemm | `div/after_gemm` | A:[4,64], D:[32] | [4,32] | Gemm + bias, then Div with positive broadcast vector |
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