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 \
  --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 and writes the same rows to validation_results.csv.

Complete inventory

The suite contains 165 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 (32)

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.
`yolo11n_stem`	First two YOLO11n `Conv-SiLU` blocks at `640x640`, including the distributed activation boundary.

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.