add relu lowering

add relu validation
add spatial compute helper
minor refactors

validation/operations/gen_tests.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-"""Generate ONNX test models for validating GEMM, Conv, and Pooling implementations."""
+"""Generate ONNX test models for validating GEMM, Conv, Pooling, and Relu implementations."""
 
 import numpy as np
 import onnx
@@ -327,6 +327,60 @@ def maxpool_after_conv():
     save_model(model, "pool/max_after_conv", "maxpool_after_conv.onnx")
 
 
+# ---------------------------------------------------------------------------
+# Relu tests
+# ---------------------------------------------------------------------------
+
+def relu_basic():
+    """Standalone Relu on a simple 2D tensor."""
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [4, 8])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [4, 8])
+    node = helper.make_node("Relu", ["X"], ["Y"])
+    graph = helper.make_graph([node], "relu_basic", [X], [Y])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "relu/basic", "relu_basic.onnx")
+
+
+def relu_4d():
+    """Standalone Relu on an NCHW tensor."""
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3, 4, 4])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 4, 4])
+    node = helper.make_node("Relu", ["X"], ["Y"])
+    graph = helper.make_graph([node], "relu_4d", [X], [Y])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "relu/4d", "relu_4d.onnx")
+
+
+def relu_after_conv():
+    """Conv followed by Relu."""
+    rng = np.random.default_rng(60)
+    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")
+    conv = helper.make_node("Conv", ["X", "W", "B"], ["C"],
+                            kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
+    relu = helper.make_node("Relu", ["C"], ["Y"])
+    graph = helper.make_graph([conv, relu], "relu_after_conv", [X], [Y], initializer=[W, B])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "relu/after_conv", "relu_after_conv.onnx")
+
+
+def relu_after_gemm():
+    """Gemm followed by Relu."""
+    B, K, N = 4, 64, 32
+    rng = np.random.default_rng(61)
+    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])
+    gemm = helper.make_node("Gemm", ["A", "W", "C"], ["G"])
+    relu = helper.make_node("Relu", ["G"], ["Y"])
+    graph = helper.make_graph([gemm, relu], "relu_after_gemm", [A], [Y], initializer=[W, C])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "relu/after_gemm", "relu_after_gemm.onnx")
+
+
 # ---------------------------------------------------------------------------
 # Main
 # ---------------------------------------------------------------------------
@@ -361,4 +415,10 @@ if __name__ == "__main__":
     avgpool_include_pad()
     maxpool_after_conv()
 
+    print("\nGenerating Relu tests:")
+    relu_basic()
+    relu_4d()
+    relu_after_conv()
+    relu_after_gemm()
+
     print("\nDone.")