# Copyright (c) ONNX Project Contributors # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import unittest import numpy as np import parameterized import onnx import onnx.reference from onnx import helper, numpy_helper class TestNumpyHelper(unittest.TestCase): def _test_numpy_helper_float_type(self, dtype: np.number) -> None: a = np.random.rand(13, 37).astype(dtype) tensor_def = numpy_helper.from_array(a, "test") self.assertEqual(tensor_def.name, "test") a_recover = numpy_helper.to_array(tensor_def) np.testing.assert_equal(a, a_recover) def _test_numpy_helper_int_type(self, dtype: np.number) -> None: a = np.random.randint( np.iinfo(dtype).min, np.iinfo(dtype).max, dtype=dtype, size=(13, 37) ) tensor_def = numpy_helper.from_array(a, "test") self.assertEqual(tensor_def.name, "test") a_recover = numpy_helper.to_array(tensor_def) np.testing.assert_equal(a, a_recover) def test_float(self) -> None: self._test_numpy_helper_float_type(np.float32) def test_uint8(self) -> None: self._test_numpy_helper_int_type(np.uint8) def test_int8(self) -> None: self._test_numpy_helper_int_type(np.int8) def test_uint16(self) -> None: self._test_numpy_helper_int_type(np.uint16) def test_int16(self) -> None: self._test_numpy_helper_int_type(np.int16) def test_int32(self) -> None: self._test_numpy_helper_int_type(np.int32) def test_int64(self) -> None: self._test_numpy_helper_int_type(np.int64) def test_string(self) -> None: a = np.array(["Amy", "Billy", "Cindy", "David"]).astype(object) tensor_def = numpy_helper.from_array(a, "test") self.assertEqual(tensor_def.name, "test") a_recover = numpy_helper.to_array(tensor_def) np.testing.assert_equal(a, a_recover) def test_bool(self) -> None: a = np.random.randint(2, size=(13, 37)).astype(bool) tensor_def = numpy_helper.from_array(a, "test") self.assertEqual(tensor_def.name, "test") a_recover = numpy_helper.to_array(tensor_def) np.testing.assert_equal(a, a_recover) def test_float16(self) -> None: self._test_numpy_helper_float_type(np.float32) def test_complex64(self) -> None: self._test_numpy_helper_float_type(np.complex64) def test_complex128(self) -> None: self._test_numpy_helper_float_type(np.complex128) def test_from_dict_values_are_np_arrays_of_float(self): map_proto = numpy_helper.from_dict({0: np.array(0.1), 1: np.array(0.9)}) self.assertIsInstance(map_proto, onnx.MapProto) self.assertEqual( numpy_helper.to_array(map_proto.values.tensor_values[0]), np.array(0.1) ) self.assertEqual( numpy_helper.to_array(map_proto.values.tensor_values[1]), np.array(0.9) ) def test_from_dict_values_are_np_arrays_of_int(self): map_proto = numpy_helper.from_dict({0: np.array(1), 1: np.array(9)}) self.assertIsInstance(map_proto, onnx.MapProto) self.assertEqual( numpy_helper.to_array(map_proto.values.tensor_values[0]), np.array(1) ) self.assertEqual( numpy_helper.to_array(map_proto.values.tensor_values[1]), np.array(9) ) def test_from_dict_values_are_np_arrays_of_ints(self): zero_array = np.array([1, 2]) one_array = np.array([9, 10]) map_proto = numpy_helper.from_dict({0: zero_array, 1: one_array}) self.assertIsInstance(map_proto, onnx.MapProto) out_tensor = numpy_helper.to_array(map_proto.values.tensor_values[0]) self.assertEqual(out_tensor[0], zero_array[0]) self.assertEqual(out_tensor[1], zero_array[1]) out_tensor = numpy_helper.to_array(map_proto.values.tensor_values[1]) self.assertEqual(out_tensor[0], one_array[0]) self.assertEqual(out_tensor[1], one_array[1]) def test_from_dict_differing_key_types(self): with self.assertRaises(TypeError): # Differing key types should raise a TypeError numpy_helper.from_dict({0: np.array(0.1), 1.1: np.array(0.9)}) def test_from_dict_differing_value_types(self): with self.assertRaises(TypeError): # Differing value types should raise a TypeError numpy_helper.from_dict({0: np.array(1), 1: np.array(0.9)}) def _to_array_from_array(self, value: int, check_dtype: bool = True): onnx_model = helper.make_model( helper.make_graph( [helper.make_node("Cast", ["X"], ["Y"], to=value)], "test", [helper.make_tensor_value_info("X", onnx.TensorProto.FLOAT, [4])], [helper.make_tensor_value_info("Y", value, [4])], ) ) ref = onnx.reference.ReferenceEvaluator(onnx_model) if "UINT" in onnx.TensorProto.DataType.Name(value): start = ref.run(None, {"X": np.array([0, 1, 2, 3], dtype=np.float32)}) else: start = ref.run(None, {"X": np.array([0, 1, -2, 3], dtype=np.float32)}) tp = numpy_helper.from_array(start[0], name="check") self.assertEqual(tp.data_type, value) back = numpy_helper.to_array(tp) self.assertEqual(start[0].shape, back.shape) if check_dtype: self.assertEqual(start[0].dtype, back.dtype) again = numpy_helper.from_array(back, name="check") self.assertEqual(tp.data_type, again.data_type) self.assertEqual(tp.name, again.name) self.assertEqual(len(tp.raw_data), len(again.raw_data)) self.assertEqual(list(tp.raw_data), list(again.raw_data)) self.assertEqual(tp.raw_data, again.raw_data) self.assertEqual(tuple(tp.dims), tuple(again.dims)) self.assertEqual(tp.SerializeToString(), again.SerializeToString()) self.assertEqual(tp.data_type, helper.np_dtype_to_tensor_dtype(back.dtype)) @parameterized.parameterized.expand( [ ("FLOAT", onnx.TensorProto.FLOAT), ("UINT8", onnx.TensorProto.UINT8), ("INT8", onnx.TensorProto.INT8), ("UINT16", onnx.TensorProto.UINT16), ("INT16", onnx.TensorProto.INT16), ("INT32", onnx.TensorProto.INT32), ("INT64", onnx.TensorProto.INT64), ("BOOL", onnx.TensorProto.BOOL), ("FLOAT16", onnx.TensorProto.FLOAT16), ("DOUBLE", onnx.TensorProto.DOUBLE), ("UINT32", onnx.TensorProto.UINT32), ("UINT64", onnx.TensorProto.UINT64), ("COMPLEX64", onnx.TensorProto.COMPLEX64), ("COMPLEX128", onnx.TensorProto.COMPLEX128), ("BFLOAT16", onnx.TensorProto.BFLOAT16), ("FLOAT8E4M3FN", onnx.TensorProto.FLOAT8E4M3FN), ("FLOAT8E4M3FNUZ", onnx.TensorProto.FLOAT8E4M3FNUZ), ("FLOAT8E5M2", onnx.TensorProto.FLOAT8E5M2), ("FLOAT8E5M2FNUZ", onnx.TensorProto.FLOAT8E5M2FNUZ), ("FLOAT8E8M0", onnx.TensorProto.FLOAT8E8M0), ("UINT4", onnx.TensorProto.UINT4), ("INT4", onnx.TensorProto.INT4), ("UINT2", onnx.TensorProto.UINT2), ("INT2", onnx.TensorProto.INT2), ("FLOAT4E2M1", onnx.TensorProto.FLOAT4E2M1), ] ) def test_to_array_from_array(self, _: str, data_type: onnx.TensorProto.DataType): self._to_array_from_array(data_type) def test_to_array_from_array_string(self): self._to_array_from_array(onnx.TensorProto.STRING, False) def test_to_float8e8m0_round_modes(self) -> None: # Inputs in [1.0, 2.0): 1.125 has only mantissa bit 20 set, 1.25 only # bit 21, 1.375 bits 20+21, 1.5 bit 22, 1.75 bits 21+22. x = np.array([1.0, 1.125, 1.25, 1.375, 1.5, 1.75], dtype=np.float32) # "up": any non-zero mantissa rounds up to the next power of 2. # Regression: a previous mask of 0x4FFFFF missed bits 20 and 21, # so 1.125 / 1.25 / 1.375 were not rounded up. np.testing.assert_array_equal( numpy_helper.to_float8e8m0(x, round_mode="up").view(np.uint8), [127, 128, 128, 128, 128, 128], ) # "down" truncates: every value in [1.0, 2.0) keeps exponent 127. np.testing.assert_array_equal( numpy_helper.to_float8e8m0(x, round_mode="down").view(np.uint8), [127, 127, 127, 127, 127, 127], ) # "nearest" rounds at bit 22 (i.e., at 1.5), independent of bits 0-21. np.testing.assert_array_equal( numpy_helper.to_float8e8m0(x, round_mode="nearest").view(np.uint8), [127, 127, 127, 127, 128, 128], ) # Unknown round_mode is a programming error. with self.assertRaises(ValueError): numpy_helper.to_float8e8m0( np.array([1.0], dtype=np.float32), round_mode="bogus" ) def test_to_float8e8m0_extreme_values(self) -> None: # NaN/Inf inputs (exponent byte 0xFF) survive every mode/saturate combo. special = np.array([np.nan, np.inf, -np.inf], dtype=np.float32) for mode in ("up", "down", "nearest"): for saturate in (True, False): out = numpy_helper.to_float8e8m0( special, saturate=saturate, round_mode=mode ) np.testing.assert_array_equal( out.view(np.uint8), [0xFF, 0xFF, 0xFF], err_msg=f"mode={mode}, saturate={saturate}", ) # 1.5 * 2**127 has exponent 0xFE with a non-zero mantissa. Under # round_mode="up", saturate=True caps at 0xFE; saturate=False lets # the exponent roll into 0xFF (the NaN slot). near_max = np.array([1.5 * 2.0**127], dtype=np.float32) self.assertEqual( numpy_helper.to_float8e8m0(near_max, saturate=True, round_mode="up").view( np.uint8 )[0], 0xFE, ) self.assertEqual( numpy_helper.to_float8e8m0(near_max, saturate=False, round_mode="up").view( np.uint8 )[0], 0xFF, ) def test_from_array_object_invalid_type(self) -> None: a = np.array([42], dtype=object) with self.assertRaises(NotImplementedError) as cm: numpy_helper.from_array(a) self.assertIn("int", str(cm.exception)) def test_from_list_explicit_dtype(self) -> None: # Verify explicit dtype is honored, not auto-detected seq = numpy_helper.from_list([], dtype=onnx.SequenceProto.MAP) self.assertEqual(seq.elem_type, onnx.SequenceProto.MAP) # Without dtype, empty list defaults to TENSOR seq2 = numpy_helper.from_list([]) self.assertEqual(seq2.elem_type, onnx.SequenceProto.TENSOR) def test_to_dict_mismatched_lengths(self) -> None: # Build a valid map then add an extra key to create a mismatch m = numpy_helper.from_dict({1: np.array(1.0), 2: np.array(2.0)}) m.keys.append(3) with self.assertRaises(IndexError) as cm: numpy_helper.to_dict(m) self.assertIn("not the same", str(cm.exception)) def test_from_dict_empty(self) -> None: with self.assertRaises(ValueError): numpy_helper.from_dict({}) def test_from_dict_unsupported_key_type(self) -> None: with self.assertRaises(TypeError) as cm: numpy_helper.from_dict({1.5: np.array(1), 2.5: np.array(2)}) self.assertIn("Unsupported map key type", str(cm.exception)) @parameterized.parameterized.expand( [ ("uint4", onnx.TensorProto.UINT4), ("int4", onnx.TensorProto.INT4), ("float4e2m1", onnx.TensorProto.FLOAT4E2M1), ] ) def test_to_array_4bit_payload_too_small_raw_data( self, _: str, data_type: int ) -> None: tensor = onnx.TensorProto() tensor.data_type = data_type tensor.dims.extend([1000]) tensor.raw_data = b"\x00" # encodes 2 elements, not 1000 with self.assertRaises(ValueError, msg="payload too small for declared dims"): numpy_helper.to_array(tensor) @parameterized.parameterized.expand( [ ("uint4", onnx.TensorProto.UINT4), ("int4", onnx.TensorProto.INT4), ("float4e2m1", onnx.TensorProto.FLOAT4E2M1), ] ) def test_to_array_4bit_payload_too_small_int32_data( self, _: str, data_type: int ) -> None: tensor = onnx.TensorProto() tensor.data_type = data_type tensor.dims.extend([1000]) tensor.int32_data.append(0) # encodes 8 elements, not 1000 with self.assertRaises(ValueError, msg="payload too small for declared dims"): numpy_helper.to_array(tensor) @parameterized.parameterized.expand( [ ("uint2", onnx.TensorProto.UINT2), ("int2", onnx.TensorProto.INT2), ] ) def test_to_array_2bit_payload_too_small_raw_data( self, _: str, data_type: int ) -> None: tensor = onnx.TensorProto() tensor.data_type = data_type tensor.dims.extend([1000]) tensor.raw_data = b"\x00" # encodes 4 elements, not 1000 with self.assertRaises(ValueError, msg="payload too small for declared dims"): numpy_helper.to_array(tensor) @parameterized.parameterized.expand( [ ("uint2", onnx.TensorProto.UINT2), ("int2", onnx.TensorProto.INT2), ] ) def test_to_array_2bit_payload_too_small_int32_data( self, _: str, data_type: int ) -> None: tensor = onnx.TensorProto() tensor.data_type = data_type tensor.dims.extend([1000]) tensor.int32_data.append(0) # encodes 16 elements, not 1000 with self.assertRaises(ValueError, msg="payload too small for declared dims"): numpy_helper.to_array(tensor) if __name__ == "__main__": unittest.main(verbosity=2)