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661170a9aa782a546b809036c2274ac389aa5e34
Raptor/validation/operations/gen_tests.py
NiccoloN 661170a9aa reimplement pool lowering 
add pool validation
align PIM ops/codegen/parser with the ISA
move constant materialization to MLIR
rename the PIM verification/materialization passes
better folded-constant handling
2026-03-23 19:14:50 +01:00

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#!/usr/bin/env python3
"""Generate ONNX test models for validating GEMM, Conv, and Pooling implementations."""
import numpy as np
import onnx
from onnx import helper, TensorProto, numpy_helper
from pathlib import Path
OPERATIONS_DIR = Path(__file__).parent
def save_model(model, directory, filename):
"""Save an ONNX model, creating the directory if needed."""
d = OPERATIONS_DIR / directory
d.mkdir(parents=True, exist_ok=True)
path = d / filename
onnx.checker.check_model(model)
onnx.save(model, str(path))
print(f" {path.relative_to(OPERATIONS_DIR)}")
# ---------------------------------------------------------------------------
# GEMM tests
# ---------------------------------------------------------------------------
def gemm_non_square():
"""GEMM with non-square weight matrix: [B, K] @ [K, N], K != N."""
B, K, N = 4, 128, 64
W = numpy_helper.from_array(np.random.default_rng(42).uniform(-1, 1, (K, N)).astype(np.float32), name="W")
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
node = helper.make_node("Gemm", ["A", "W"], ["Y"])
graph = helper.make_graph([node], "gemm_non_square", [A], [Y], initializer=[W])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "gemm/non_square", "gemm_non_square.onnx")
def gemm_with_bias():
"""GEMM with bias: Y = A @ W + C."""
B, K, N = 4, 128, 128
rng = np.random.default_rng(43)
W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
node = helper.make_node("Gemm", ["A", "W", "C"], ["Y"])
graph = helper.make_graph([node], "gemm_with_bias", [A], [Y], initializer=[W, C])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "gemm/with_bias", "gemm_with_bias.onnx")
def gemm_transB():
"""GEMM with transB=1: Y = A @ W^T."""
B, K, N = 4, 128, 64
rng = np.random.default_rng(44)
# W stored as [N, K], transposed during computation
W = numpy_helper.from_array(rng.uniform(-1, 1, (N, K)).astype(np.float32), name="W")
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
node = helper.make_node("Gemm", ["A", "W"], ["Y"], transB=1)
graph = helper.make_graph([node], "gemm_transB", [A], [Y], initializer=[W])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "gemm/transB", "gemm_transB.onnx")
def gemm_alpha_beta():
"""GEMM with alpha and beta: Y = 0.5 * A @ W + 0.25 * C."""
B, K, N = 4, 64, 64
rng = np.random.default_rng(45)
W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
node = helper.make_node("Gemm", ["A", "W", "C"], ["Y"], alpha=0.5, beta=0.25)
graph = helper.make_graph([node], "gemm_alpha_beta", [A], [Y], initializer=[W, C])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "gemm/alpha_beta", "gemm_alpha_beta.onnx")
def gemm_small():
"""Small GEMM: [2, 8] @ [8, 4]."""
B, K, N = 2, 8, 4
rng = np.random.default_rng(46)
W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
node = helper.make_node("Gemm", ["A", "W"], ["Y"])
graph = helper.make_graph([node], "gemm_small", [A], [Y], initializer=[W])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "gemm/small", "gemm_small.onnx")
def gemm_large():
"""Larger GEMM: [8, 256] @ [256, 128]."""
B, K, N = 8, 256, 128
rng = np.random.default_rng(47)
W = numpy_helper.from_array(rng.uniform(-1, 1, (K, N)).astype(np.float32), name="W")
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
node = helper.make_node("Gemm", ["A", "W"], ["Y"])
graph = helper.make_graph([node], "gemm_large", [A], [Y], initializer=[W])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "gemm/large", "gemm_large.onnx")
def gemm_transB_with_bias():
"""GEMM with transB and bias: Y = A @ W^T + C."""
B, K, N = 4, 128, 64
rng = np.random.default_rng(48)
W = numpy_helper.from_array(rng.uniform(-1, 1, (N, K)).astype(np.float32), name="W")
C = numpy_helper.from_array(rng.uniform(-1, 1, (N,)).astype(np.float32), name="C")
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [B, K])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [B, N])
node = helper.make_node("Gemm", ["A", "W", "C"], ["Y"], transB=1)
graph = helper.make_graph([node], "gemm_transB_with_bias", [A], [Y], initializer=[W, C])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "gemm/transB_with_bias", "gemm_transB_with_bias.onnx")
# ---------------------------------------------------------------------------
# Conv tests
# ---------------------------------------------------------------------------
def conv_3x3_kernel():
"""Conv with 3x3 kernel, no padding."""
# Input: [1, 1, 5, 5], Kernel: [1, 1, 3, 3] -> Output: [1, 1, 3, 3]
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 5, 5])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 3, 3])
W = numpy_helper.from_array(
np.random.default_rng(50).uniform(-1, 1, (1, 1, 3, 3)).astype(np.float32), name="W")
node = helper.make_node("Conv", ["X", "W"], ["Y"],
kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
graph = helper.make_graph([node], "conv_3x3", [X], [Y], initializer=[W])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "conv/kernel_3x3", "conv_kernel_3x3.onnx")
def conv_stride2():
"""Conv with 3x3 kernel and stride 2."""
# Input: [1, 1, 6, 6], Kernel: [1, 1, 3, 3], stride 2 -> Output: [1, 1, 2, 2]
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 6, 6])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 2, 2])
W = numpy_helper.from_array(
np.random.default_rng(51).uniform(-1, 1, (1, 1, 3, 3)).astype(np.float32), name="W")
node = helper.make_node("Conv", ["X", "W"], ["Y"],
kernel_shape=[3, 3], strides=[2, 2], pads=[0, 0, 0, 0])
graph = helper.make_graph([node], "conv_stride2", [X], [Y], initializer=[W])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "conv/stride_2", "conv_stride_2.onnx")
def conv_multi_channel():
"""Conv with multiple input and output channels."""
# Input: [1, 3, 5, 5], Kernel: [4, 3, 3, 3] -> Output: [1, 4, 3, 3]
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 5, 5])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 4, 3, 3])
W = numpy_helper.from_array(
np.random.default_rng(52).uniform(-1, 1, (4, 3, 3, 3)).astype(np.float32), name="W")
node = helper.make_node("Conv", ["X", "W"], ["Y"],
kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
graph = helper.make_graph([node], "conv_multi_channel", [X], [Y], initializer=[W])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "conv/multi_channel", "conv_multi_channel.onnx")
def conv_1x1():
"""1x1 pointwise convolution (channel mixing)."""
# Input: [1, 8, 4, 4], Kernel: [4, 8, 1, 1] -> Output: [1, 4, 4, 4]
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 8, 4, 4])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 4, 4, 4])
W = numpy_helper.from_array(
np.random.default_rng(53).uniform(-1, 1, (4, 8, 1, 1)).astype(np.float32), name="W")
node = helper.make_node("Conv", ["X", "W"], ["Y"],
kernel_shape=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0])
graph = helper.make_graph([node], "conv_1x1", [X], [Y], initializer=[W])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "conv/pointwise_1x1", "conv_1x1.onnx")
def conv_same_padding_3x3():
"""Conv 3x3 with SAME_UPPER padding, preserving spatial dimensions."""
# Input: [1, 1, 5, 5], Kernel: [1, 1, 3, 3], SAME_UPPER -> Output: [1, 1, 5, 5]
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 5, 5])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 5, 5])
W = numpy_helper.from_array(
np.random.default_rng(54).uniform(-1, 1, (1, 1, 3, 3)).astype(np.float32), name="W")
node = helper.make_node("Conv", ["X", "W"], ["Y"],
kernel_shape=[3, 3], strides=[1, 1], auto_pad="SAME_UPPER")
graph = helper.make_graph([node], "conv_same_3x3", [X], [Y], initializer=[W])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "conv/same_padding_3x3", "conv_same_padding_3x3.onnx")
def conv_explicit_padding():
"""Conv 3x3 with explicit asymmetric padding."""
# Input: [1, 1, 4, 4], Kernel: [1, 1, 3, 3], pads=[1,1,1,1] -> Output: [1, 1, 4, 4]
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 4, 4])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 4, 4])
W = numpy_helper.from_array(
np.random.default_rng(55).uniform(-1, 1, (1, 1, 3, 3)).astype(np.float32), name="W")
node = helper.make_node("Conv", ["X", "W"], ["Y"],
kernel_shape=[3, 3], strides=[1, 1], pads=[1, 1, 1, 1])
graph = helper.make_graph([node], "conv_explicit_pad", [X], [Y], initializer=[W])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "conv/explicit_padding", "conv_explicit_padding.onnx")
def conv_with_bias_3x3():
"""Conv 3x3 with bias."""
# Input: [1, 3, 5, 5], Kernel: [2, 3, 3, 3], Bias: [2] -> Output: [1, 2, 3, 3]
rng = np.random.default_rng(56)
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 5, 5])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2, 3, 3])
W = numpy_helper.from_array(rng.uniform(-1, 1, (2, 3, 3, 3)).astype(np.float32), name="W")
B = numpy_helper.from_array(rng.uniform(-1, 1, (2,)).astype(np.float32), name="B")
node = helper.make_node("Conv", ["X", "W", "B"], ["Y"],
kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
graph = helper.make_graph([node], "conv_with_bias_3x3", [X], [Y], initializer=[W, B])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "conv/with_bias_3x3", "conv_with_bias_3x3.onnx")
def conv_batch_2():
"""Batched conv (batch=2) with SAME_UPPER padding and bias."""
# Input: [2, 3, 3, 3], Kernel: [1, 3, 2, 2], Bias: [1] -> Output: [2, 1, 3, 3]
rng = np.random.default_rng(57)
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 3, 3])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 1, 3, 3])
W = numpy_helper.from_array(rng.uniform(-1, 1, (1, 3, 2, 2)).astype(np.float32), name="W")
B = numpy_helper.from_array(rng.uniform(-1, 1, (1,)).astype(np.float32), name="B")
node = helper.make_node("Conv", ["X", "W", "B"], ["Y"],
kernel_shape=[2, 2], strides=[1, 1], auto_pad="SAME_UPPER")
graph = helper.make_graph([node], "conv_batch_2", [X], [Y], initializer=[W, B])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "conv/batch_2", "conv_batch_2.onnx")
def conv_large_spatial():
"""Conv on larger spatial input: [1, 1, 8, 8] with 3x3 kernel."""
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 8, 8])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 6, 6])
W = numpy_helper.from_array(
np.random.default_rng(58).uniform(-1, 1, (1, 1, 3, 3)).astype(np.float32), name="W")
node = helper.make_node("Conv", ["X", "W"], ["Y"],
kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
graph = helper.make_graph([node], "conv_large_spatial", [X], [Y], initializer=[W])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "conv/large_spatial", "conv_large_spatial.onnx")
# ---------------------------------------------------------------------------
# Pooling tests
# ---------------------------------------------------------------------------
def maxpool_basic():
"""MaxPool 2x2 with stride 1."""
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 4, 4])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 3, 3])
node = helper.make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], strides=[1, 1], pads=[0, 0, 0, 0])
graph = helper.make_graph([node], "maxpool_basic", [X], [Y])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "pool/max_basic", "maxpool_basic.onnx")
def maxpool_stride2_multichannel():
"""MaxPool 2x2 with stride 2 on multiple channels."""
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 5, 6, 6])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 5, 3, 3])
node = helper.make_node("MaxPool", ["X"], ["Y"], kernel_shape=[2, 2], strides=[2, 2], pads=[0, 0, 0, 0])
graph = helper.make_graph([node], "maxpool_stride2_multichannel", [X], [Y])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "pool/max_stride2_multichannel", "maxpool_stride2_multichannel.onnx")
def maxpool_same_upper():
"""MaxPool 3x3 with SAME_UPPER padding."""
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 5, 5])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 3, 3])
node = helper.make_node("MaxPool", ["X"], ["Y"], kernel_shape=[3, 3], strides=[2, 2], auto_pad="SAME_UPPER")
graph = helper.make_graph([node], "maxpool_same_upper", [X], [Y])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "pool/max_same_upper", "maxpool_same_upper.onnx")
def avgpool_basic():
"""AveragePool 2x2 with stride 1."""
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 4, 4])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 3, 3, 3])
node = helper.make_node("AveragePool", ["X"], ["Y"], kernel_shape=[2, 2], strides=[1, 1], pads=[0, 0, 0, 0])
graph = helper.make_graph([node], "avgpool_basic", [X], [Y])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "pool/avg_basic", "avgpool_basic.onnx")
def avgpool_explicit_padding():
"""AveragePool 3x3 with explicit padding, excluding pad from the divisor."""
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2, 4, 4])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2, 2, 2])
node = helper.make_node("AveragePool", ["X"], ["Y"],
kernel_shape=[3, 3], strides=[2, 2], pads=[1, 1, 1, 1], count_include_pad=0)
graph = helper.make_graph([node], "avgpool_explicit_padding", [X], [Y])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "pool/avg_explicit_padding", "avgpool_explicit_padding.onnx")
def avgpool_include_pad():
"""AveragePool 3x3 with explicit padding, including pad in the divisor."""
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 2, 4, 4])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 2, 2, 2])
node = helper.make_node("AveragePool", ["X"], ["Y"],
kernel_shape=[3, 3], strides=[2, 2], pads=[1, 1, 1, 1], count_include_pad=1)
graph = helper.make_graph([node], "avgpool_include_pad", [X], [Y])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "pool/avg_include_pad", "avgpool_include_pad.onnx")
def maxpool_after_conv():
"""Conv followed by MaxPool to validate pooling on lowered conv results."""
rng = np.random.default_rng(59)
X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 6, 6])
Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 4, 2, 2])
W = numpy_helper.from_array(rng.uniform(-1, 1, (4, 3, 3, 3)).astype(np.float32), name="W")
conv = helper.make_node("Conv", ["X", "W"], ["C"], kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
pool = helper.make_node("MaxPool", ["C"], ["Y"], kernel_shape=[2, 2], strides=[2, 2], pads=[0, 0, 0, 0])
graph = helper.make_graph([conv, pool], "maxpool_after_conv", [X], [Y], initializer=[W])
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
save_model(model, "pool/max_after_conv", "maxpool_after_conv.onnx")
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
if __name__ == "__main__":
print("Generating GEMM tests:")
gemm_non_square()
gemm_with_bias()
gemm_transB()
gemm_alpha_beta()
gemm_small()
gemm_large()
gemm_transB_with_bias()
print("\nGenerating Conv tests:")
conv_3x3_kernel()
conv_stride2()
conv_multi_channel()
conv_1x1()
conv_same_padding_3x3()
conv_explicit_padding()
conv_with_bias_3x3()
conv_batch_2()
conv_large_spatial()
print("\nGenerating Pooling tests:")
maxpool_basic()
maxpool_stride2_multichannel()
maxpool_same_upper()
avgpool_basic()
avgpool_explicit_padding()
avgpool_include_pad()
maxpool_after_conv()
print("\nDone.")
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