Fix several issues regarding recent mapping update

[jcwchen]

Description

• Add missing case for SequenceProto.MAP in numpy_helper.to_list.
• Fix issue New added function generates DeprecatedWarning #4552, make tensor_dtype_to_field not throw deprecation warning by introducing a identical dictionary _STORAGE_TENSOR_TYPE_TO_FIELD. Same case for NP_TYPE_TO_TENSOR_TYPE.
• Partially fix printable_graph error and INVALID_GRAPH (ONNXRuntime) when using bf16 datatype #4549: enable printable_attribute to handle bfloat16.

Motivation and Context

Recent #4270 refactored existing code, but I forgot to add the case for SequenceProto.MAP in to_list.

cc @gramalingam Please review this PR. Sorry that I should catch this in advance before my PR got merged. Thank you!

[linkerzhang]

LGTM.

[xadupre]

It is possible to add a unit test to check the warning is raised when using the dictionary and not raised when using the function?

[jcwchen]

It is possible to add a unit test to check the warning is raised when using the dictionary and not raised when using the function?

Good idea. I just added tests by TestHelperMappingFunctions and actually np_dtype_to_tensor_dtype needs to be fixed as well. With the added tests, now I am confident that these new introduced helpers should not unexpectedly throw a deprecation warnings. Please review it. Thank you!

[xadupre]

Misspelling deprecation

[jcwchen]

Corrected. Thanks!

[xadupre]

I wonder if it is worth checking what happens in case of bfloat16.

[jcwchen]

Tests for bfloat16 looks useful since the mapping for bfloat16 is quite confusing. Adding these tests can prevent future regression. Just added them. PTAL. Thanks!

[jcwchen]

Please note that I think BFLOAT16 is missing here.

[xadupre]

I can be done in another PR but I would check all the types:

    def test_numeric_types(self):  # type: ignore
        dtypes = [
            np.float16,
            np.float32,
            np.float64,
            np.int8,
            np.int16,
            np.int32,
            np.int64,
            np.uint8,
            np.uint16,
            np.uint32,
            np.uint64,
            np.complex64,
            np.complex128,
        ]
        for dt in dtypes:
            with self.subTest(dtype=dt):
                t = np.array([0, 1, 2], dtype=dt)
                ot = from_array(t)
                u = to_array(ot)
                self.assertEqual(t.dtype, u.dtype)
                assert_almost_equal(t, u)

    def test_make_tensor(self):  # type: ignore
        for pt, dt in TENSOR_TYPE_TO_NP_TYPE.items():
            if pt == TensorProto.BFLOAT16:
                continue
            with self.subTest(dt=dt, pt=pt, raw=False):
                if pt == TensorProto.STRING:
                    t = np.array([["i0", "i1", "i2"], ["i6", "i7", "i8"]], dtype=dt)
                else:
                    t = np.array([[0, 1, 2], [6, 7, 8]], dtype=dt)
                ot = make_tensor("test", pt, t.shape, t, raw=False)
                self.assertFalse(ot is None)
                u = to_array(ot)
                self.assertEqual(t.dtype, u.dtype)
                self.assertEqual(t.tolist(), u.tolist())
            with self.subTest(dt=dt, pt=pt, raw=True):
                t = np.array([[0, 1, 2], [6, 7, 8]], dtype=dt)
                if pt == TensorProto.STRING:
                    with self.assertRaises(TypeError):
                        make_tensor("test", pt, t.shape, t.tobytes(), raw=True)
                else:
                    ot = make_tensor("test", pt, t.shape, t.tobytes(), raw=True)
                    self.assertFalse(ot is None)
                    u = to_array(ot)
                    self.assertEqual(t.dtype, u.dtype)
                    assert_almost_equal(t, u)

[jcwchen]

Thank you @xadupre for the suggestion. It looks good to me, but I am inclined to have it in another PR (this PR more focuses on bug fixes) Feel free to let me know if you still have other concern.