new ops tests for matmul, grouped conv, concat and reshape

related fixes

validation/operations/gen_tests.py

@@ -154,6 +154,33 @@ def conv_large_spatial():
     save_model(model, "conv/large_spatial", "conv_large_spatial.onnx")
 
 
+def conv_grouped_two_groups():
+    """Grouped Conv with two groups, pointwise kernels, and bias."""
+    rng = np.random.default_rng(59)
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 4, 4, 4])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 4, 4, 4])
+    W = numpy_helper.from_array(rng.uniform(-1, 1, (4, 2, 1, 1)).astype(np.float32), name="W")
+    B = numpy_helper.from_array(rng.uniform(-1, 1, (4,)).astype(np.float32), name="B")
+    node = helper.make_node("Conv", ["X", "W", "B"], ["Y"],
+                            kernel_shape=[1, 1], strides=[1, 1], pads=[0, 0, 0, 0], group=2)
+    graph = helper.make_graph([node], "conv_grouped_two_groups", [X], [Y], initializer=[W, B])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "conv/grouped_two_groups", "conv_grouped_two_groups.onnx")
+
+
+def conv_depthwise_grouped():
+    """Depthwise-style grouped Conv with one input channel per group."""
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 3, 4, 4])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 3, 2, 2])
+    W = numpy_helper.from_array(
+        np.random.default_rng(60).uniform(-1, 1, (3, 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], group=3)
+    graph = helper.make_graph([node], "conv_depthwise_grouped", [X], [Y], initializer=[W])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "conv/depthwise_grouped", "conv_depthwise_grouped.onnx")
+
+
 # ---------------------------------------------------------------------------
 # GEMM tests
 # ---------------------------------------------------------------------------
@@ -252,6 +279,33 @@ def gemm_transB_with_bias():
     save_model(model, "gemm/transB_with_bias", "gemm_transB_with_bias.onnx")
 
 
+# ---------------------------------------------------------------------------
+# MatMul tests
+# ---------------------------------------------------------------------------
+
+def matmul_basic():
+    """Direct 2D MatMul with constant RHS."""
+    A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 3])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
+    B = numpy_helper.from_array(np.random.default_rng(49).uniform(-1, 1, (3, 4)).astype(np.float32), name="B")
+    node = helper.make_node("MatMul", ["A", "B"], ["Y"])
+    graph = helper.make_graph([node], "matmul_basic", [A], [Y], initializer=[B])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "matmul/basic", "matmul_basic.onnx")
+
+
+def matmul_batched_3d():
+    """Batched 3D MatMul with matching batch dimensions."""
+    rng = np.random.default_rng(50)
+    A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 2, 3])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 2, 4])
+    B = numpy_helper.from_array(rng.uniform(-1, 1, (2, 3, 4)).astype(np.float32), name="B")
+    node = helper.make_node("MatMul", ["A", "B"], ["Y"])
+    graph = helper.make_graph([node], "matmul_batched_3d", [A], [Y], initializer=[B])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "matmul/batched_3d", "matmul_batched_3d.onnx")
+
+
 # ---------------------------------------------------------------------------
 # Pooling tests
 # ---------------------------------------------------------------------------
@@ -607,6 +661,36 @@ def gather_axis0_matrix_indices():
     save_model(model, "gather/axis0_matrix_indices", "gather_axis0_matrix_indices.onnx")
 
 
+# ---------------------------------------------------------------------------
+# Concat tests
+# ---------------------------------------------------------------------------
+
+def concat_channel_axis():
+    """Concat two runtime NCHW tensors along the channel axis."""
+    A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 1, 2, 2])
+    B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [1, 2, 2, 2])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 3, 2, 2])
+    node = helper.make_node("Concat", ["A", "B"], ["Y"], axis=1)
+    graph = helper.make_graph([node], "concat_channel_axis", [A, B], [Y])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "concat/channel_axis", "concat_channel_axis.onnx")
+
+
+# ---------------------------------------------------------------------------
+# Reshape tests
+# ---------------------------------------------------------------------------
+
+def reshape_same_rank():
+    """Runtime tensor Reshape with a static shape initializer and unchanged rank."""
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [2, 3])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [3, 2])
+    shape = make_int64_initializer("shape", [3, 2])
+    node = helper.make_node("Reshape", ["X", "shape"], ["Y"])
+    graph = helper.make_graph([node], "reshape_same_rank", [X], [Y], initializer=[shape])
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
+    save_model(model, "reshape/same_rank", "reshape_same_rank.onnx")
+
+
 # ---------------------------------------------------------------------------
 # Add tests
 # ---------------------------------------------------------------------------
@@ -757,6 +841,12 @@ if __name__ == "__main__":
     conv_with_bias_3x3()
     conv_batch_2()
     conv_large_spatial()
+    conv_grouped_two_groups()
+    conv_depthwise_grouped()
+
+    print("\nGenerating MatMul tests:")
+    matmul_basic()
+    matmul_batched_3d()
 
     print("\nGenerating Pooling tests:")
     maxpool_basic()
@@ -802,6 +892,12 @@ if __name__ == "__main__":
     gather_axis1()
     gather_axis0_matrix_indices()
 
+    print("\nGenerating Concat tests:")
+    concat_channel_axis()
+
+    print("\nGenerating Reshape tests:")
+    reshape_same_rank()
+
     print("\nGenerating Add tests:")
     add_basic()
     add_broadcast_row()