This directory contains the ONNX models used by validation/validate.py to validate individual operations through compilation, PIM simulation, and comparison with the ONNX-MLIR reference runtime.

Naming

Every model uses the same path convention:

<category>/<case>/<category>_<case>.onnx

The <category>/<case> pair is the operation ID printed by the validator. Use lowercase snake_case for both components. Keep the generator function, graph name, directory, and filename based on the same operation ID when adding a test.

Generate and validate

Regenerate all generated models from the repository root:

.venv/bin/python validation/operations/gen_tests.py

Run the complete suite with deadlock detection:

.venv/bin/python validation/validate.py \
  --raptor-path build_release/Release/bin/onnx-mlir \
  --onnx-include-dir onnx-mlir/include \
  --operations-dir validation/operations \
  --crossbar-count 64 \
  --crossbar-size 128 \
  --core-count 144 \
  --raptor-extra-arg=--pim-detect-communication-deadlock \
  --raptor-extra-arg=--pim-export-spatial-dataflow=none

Use --compile-only for compiler and deadlock checks, then --run-only to reuse those artifacts for reference execution, simulation, and comparison. The validator prints the complete operation results table before its summary.

Complete inventory

The suite contains 164 models. Tensor shapes, attributes, and constants are defined in gen_tests.py and in the checked-in ONNX models.

Add (5)

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)

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 (31)

Case	Description
`batch_2`	Batched Conv with SAME_UPPER padding and bias.
`batch_4_pointwise`	Pointwise Conv with batch size four.
`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.
`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.
`simple`	Hand-authored basic 2x2 Conv.
`stride_2`	3x3 Conv with stride two.
`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.

Div (6)

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)

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)

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_transB`	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.
`simple`	Basic Gemm with square weights.
`small`	Tiny Gemm for fast focused validation.
`small_k_large_n`	Uses a modest reduction dimension and wide output.
`transA`	Transposes the left-hand matrix.
`transA_transB`	Transposes both matrix operands.
`transB`	Transposes the right-hand weight matrix.
`transB_with_bias`	Combines a transposed weight matrix with bias.
`with_bias`	Basic matrix product with vector bias.

Gemv (5)

Case	Description
`constant`	Vector-matrix product with all inputs constant.
`simple`	Basic single-row vector-matrix product.
`with_heterogeneous_constant`	Adds a non-uniform constant bias pattern.
`with_homogeneous_constant`	Adds a constant bias matching the output shape.
`with_scalar_constant`	Adds a scalar broadcast bias.

MatMul (11)

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.

Mul (5)

Case	Description
`after_conv`	Conv followed by per-channel scaling.
`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)

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)

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)

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)

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)

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`	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)

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

Slice (8)

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)

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)

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)

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.