wehub-resource-sync
381 lines
16 KiB
Python
381 lines
16 KiB
Python
"""Tests for data_entry_keys."""
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import msgspec
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import pytest
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import torch
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from vllm_omni.data_entry_keys import (
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CodesStruct,
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EmbeddingsStruct,
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HiddenStatesStruct,
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IdsStruct,
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MetaStruct,
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OmniPayload,
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OmniPayloadStruct,
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deserialize_payload,
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flatten_payload,
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serialize_payload,
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to_dict,
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to_struct,
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unflatten_payload,
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)
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from vllm_omni.engine import AdditionalInformationPayload
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class TestOmniPayloadStruct:
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"""Runtime-validated mirror of OmniPayload (msgspec.Struct)."""
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def test_to_struct_validates_dict(self):
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d = {"meta": {"left_context_size": 25, "finished": torch.tensor(False)}}
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s = to_struct(d)
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assert s.meta.left_context_size == 25
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def test_to_struct_rejects_legacy_flat_top_level(self):
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with pytest.raises(msgspec.ValidationError, match="unknown field"):
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to_struct({"code_predictor_codes": torch.zeros(3, 8)})
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def test_to_struct_rejects_legacy_flat_meta_field(self):
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# `left_context_size` at top level (legacy) instead of under `meta`
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with pytest.raises(msgspec.ValidationError, match="unknown field"):
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to_struct({"left_context_size": 25})
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def test_to_struct_rejects_typo_in_subkey(self):
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with pytest.raises(msgspec.ValidationError, match="unknown field"):
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to_struct({"meta": {"finisheed": True}})
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def test_to_struct_rejects_wrong_type(self):
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with pytest.raises(msgspec.ValidationError, match="Expected"):
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to_struct({"meta": {"left_context_size": "not_an_int"}})
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def test_round_trip_dict_struct_dict(self):
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original = {
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"meta": {"left_context_size": 7, "finished": torch.tensor(True)},
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"codes": {"audio": torch.zeros(2, 8)},
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"hidden_states": {"output": torch.zeros(4, 16)},
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}
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s = to_struct(original)
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d = to_dict(s)
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assert sorted(d.keys()) == sorted(original.keys())
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for top, sub in original.items():
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assert sorted(d[top].keys()) == sorted(sub.keys())
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def test_to_dict_drops_unset_fields(self):
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s = OmniPayloadStruct(meta=MetaStruct(left_context_size=10))
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d = to_dict(s)
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assert d == {"meta": {"left_context_size": 10}}
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def test_struct_with_all_categories(self):
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d = {
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"hidden_states": {"output": torch.zeros(1)},
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"embed": {"prefill": torch.zeros(1), "tts_bos": torch.zeros(1)},
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"ids": {"all": [1, 2], "prompt": [1]},
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"codes": {"audio": torch.zeros(1)},
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"meta": {"left_context_size": 3, "num_processed_tokens": 7},
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}
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s = to_struct(d)
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assert isinstance(s.hidden_states, HiddenStatesStruct)
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assert isinstance(s.embed, EmbeddingsStruct)
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assert isinstance(s.ids, IdsStruct)
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assert isinstance(s.codes, CodesStruct)
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assert isinstance(s.meta, MetaStruct)
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assert s.ids.all == [1, 2]
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assert s.meta.num_processed_tokens == 7
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class TestValidatePayload:
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def test_raises_on_unknown_top_level(self):
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from vllm_omni.data_entry_keys import validate_payload
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with pytest.raises(msgspec.ValidationError, match="unknown field"):
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validate_payload({"code_predictor_codes": torch.zeros(3, 8)}, context="test_boundary")
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def test_raises_on_unknown_sub_key(self):
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from vllm_omni.data_entry_keys import validate_payload
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with pytest.raises(msgspec.ValidationError, match="unknown field"):
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validate_payload({"meta": {"finisheed": True}})
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def test_none_is_ok(self):
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from vllm_omni.data_entry_keys import validate_payload
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validate_payload(None) # should not raise
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def test_valid_payload_passes(self):
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from vllm_omni.data_entry_keys import validate_payload
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validate_payload({"meta": {"left_context_size": 5}})
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def test_context_in_error_message(self):
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from vllm_omni.data_entry_keys import validate_payload
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with pytest.raises(msgspec.ValidationError, match="my_call_site"):
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validate_payload({"bad": 1}, context="my_call_site")
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class TestWireEquivalenceStructVsDict:
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"""Producer return-side migration invariant: encoding an ``OmniPayloadStruct``
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via the connector serializer must decode to the same payload as encoding
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the equivalent ``to_dict(struct)``.
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Guards against regressions where the wire format diverges between the two
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paths (e.g. msgspec adds a struct tag, or ``to_dict`` drops a non-default
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sub-field that ``omit_defaults`` retains).
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"""
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@staticmethod
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def _round_trip(obj):
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from vllm_omni.distributed.omni_connectors.utils.serialization import (
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OmniMsgpackDecoder,
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OmniMsgpackEncoder,
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)
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return OmniMsgpackDecoder().decode(OmniMsgpackEncoder().encode(obj))
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@staticmethod
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def _assert_decoded_equal(a, b):
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if isinstance(a, dict):
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assert isinstance(b, dict)
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assert sorted(a.keys()) == sorted(b.keys())
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for k in a:
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TestWireEquivalenceStructVsDict._assert_decoded_equal(a[k], b[k])
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elif isinstance(a, torch.Tensor):
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assert isinstance(b, torch.Tensor)
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assert a.dtype == b.dtype
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assert a.shape == b.shape
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assert torch.equal(a, b)
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elif isinstance(a, list):
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assert isinstance(b, list) and len(a) == len(b)
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for x, y in zip(a, b, strict=True):
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TestWireEquivalenceStructVsDict._assert_decoded_equal(x, y)
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else:
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assert a == b
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def test_basic_payload(self):
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struct = OmniPayloadStruct(
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codes=CodesStruct(audio=torch.tensor([1, 2, 3], dtype=torch.long)),
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meta=MetaStruct(left_context_size=10, finished=torch.tensor(True, dtype=torch.bool)),
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)
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self._assert_decoded_equal(self._round_trip(struct), self._round_trip(to_dict(struct)))
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def test_nested_sub_structs(self):
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# Exercises depth-2 sub-struct encoding (embed.*) which was the case that
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# exposed schema drift in the #1829 migration.
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struct = OmniPayloadStruct(
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codes=CodesStruct(audio=torch.tensor([5, 6], dtype=torch.long)),
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meta=MetaStruct(finished=torch.tensor(False, dtype=torch.bool)),
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embed=EmbeddingsStruct(
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speech_token=torch.tensor([[1, 2, 3]]),
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speech_feat=torch.tensor([[[0.1, 0.2], [0.3, 0.4]]]),
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embedding=torch.tensor([[0.5, 0.6]]),
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),
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)
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self._assert_decoded_equal(self._round_trip(struct), self._round_trip(to_dict(struct)))
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class TestFlattenPayload:
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def test_basic_nested_to_dotted(self):
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nested = {
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"codes": {"audio": torch.tensor([1.0])},
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"meta": {"finished": torch.tensor(True, dtype=torch.bool), "left_context_size": 5},
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}
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flat = flatten_payload(nested)
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assert torch.equal(flat["codes.audio"], torch.tensor([1.0]))
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assert flat["meta.finished"].item() is True
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assert flat["meta.left_context_size"] == 5
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assert "codes" not in flat
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assert "meta" not in flat
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def test_top_level_keys_preserved(self):
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nested = {
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"latent": torch.tensor([9.0]),
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"generated_len": 42,
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}
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flat = flatten_payload(nested)
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assert torch.equal(flat["latent"], torch.tensor([9.0]))
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assert flat["generated_len"] == 42
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def test_hidden_states_layers_expanded(self):
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nested = {
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"hidden_states": {
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"output": torch.tensor([1.0]),
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"layers": {
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0: torch.tensor([2.0]),
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24: torch.tensor([3.0]),
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},
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},
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}
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flat = flatten_payload(nested)
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assert torch.equal(flat["hidden_states.output"], torch.tensor([1.0]))
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assert torch.equal(flat["hidden_states.layer_0"], torch.tensor([2.0]))
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assert torch.equal(flat["hidden_states.layer_24"], torch.tensor([3.0]))
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assert "hidden_states.layers" not in flat
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def test_mixed_nested_and_top_level(self):
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nested: OmniPayload = {
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"codes": {"audio": torch.tensor([1.0])},
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"latent": torch.tensor([2.0]),
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"meta": {"finished": torch.tensor(False, dtype=torch.bool)},
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}
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flat = flatten_payload(nested)
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assert set(flat.keys()) == {"codes.audio", "latent", "meta.finished"}
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class TestUnflattenPayload:
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def test_basic_dotted_to_nested(self):
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flat = {
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"codes.audio": torch.tensor([1.0]),
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"meta.finished": torch.tensor(True, dtype=torch.bool),
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"meta.left_context_size": 5,
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}
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nested = unflatten_payload(flat)
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assert torch.equal(nested["codes"]["audio"], torch.tensor([1.0]))
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assert nested["meta"]["finished"].item() is True
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assert nested["meta"]["left_context_size"] == 5
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def test_top_level_keys_preserved(self):
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flat = {"latent": torch.tensor([9.0]), "generated_len": 42}
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nested = unflatten_payload(flat)
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assert torch.equal(nested["latent"], torch.tensor([9.0]))
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assert nested["generated_len"] == 42
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def test_hidden_states_layers_collected(self):
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flat = {
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"hidden_states.output": torch.tensor([1.0]),
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"hidden_states.layer_0": torch.tensor([2.0]),
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"hidden_states.layer_24": torch.tensor([3.0]),
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}
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nested = unflatten_payload(flat)
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assert torch.equal(nested["hidden_states"]["output"], torch.tensor([1.0]))
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assert torch.equal(nested["hidden_states"]["layers"][0], torch.tensor([2.0]))
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assert torch.equal(nested["hidden_states"]["layers"][24], torch.tensor([3.0]))
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class TestFlattenUnflattenRoundTrip:
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def test_round_trip_simple(self):
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original: OmniPayload = {
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"codes": {"audio": torch.tensor([1.0, 2.0])},
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"meta": {"finished": torch.tensor(True, dtype=torch.bool), "left_context_size": 10},
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"ids": {"prompt": [1, 2, 3]},
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"latent": torch.tensor([5.0]),
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}
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restored = unflatten_payload(flatten_payload(original))
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assert torch.equal(restored["codes"]["audio"], original["codes"]["audio"])
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assert restored["meta"]["finished"].item() is True
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assert restored["meta"]["left_context_size"] == 10
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assert restored["ids"]["prompt"] == [1, 2, 3]
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assert torch.equal(restored["latent"], original["latent"])
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def test_round_trip_with_layers(self):
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original = {
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"hidden_states": {
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"output": torch.tensor([1.0]),
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"layers": {0: torch.tensor([2.0]), 24: torch.tensor([3.0])},
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},
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}
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restored = unflatten_payload(flatten_payload(original))
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assert torch.equal(restored["hidden_states"]["output"], torch.tensor([1.0]))
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assert torch.equal(restored["hidden_states"]["layers"][0], torch.tensor([2.0]))
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assert torch.equal(restored["hidden_states"]["layers"][24], torch.tensor([3.0]))
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def test_round_trip_all_categories(self):
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original: OmniPayload = {
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"hidden_states": {"output": torch.tensor([1.0]), "last": torch.tensor([2.0])},
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"embed": {"prefill": torch.tensor([3.0]), "tts_bos": torch.tensor([4.0])},
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"codes": {"audio": torch.tensor([5.0]), "ref": torch.tensor([6.0])},
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"ids": {"all": [1, 2], "prompt": [3, 4]},
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"meta": {"finished": torch.tensor(False, dtype=torch.bool), "ar_width": 8},
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}
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restored = unflatten_payload(flatten_payload(original))
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assert torch.equal(restored["hidden_states"]["output"], torch.tensor([1.0]))
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assert torch.equal(restored["hidden_states"]["last"], torch.tensor([2.0]))
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assert torch.equal(restored["embed"]["prefill"], torch.tensor([3.0]))
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assert torch.equal(restored["embed"]["tts_bos"], torch.tensor([4.0]))
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assert torch.equal(restored["codes"]["audio"], torch.tensor([5.0]))
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assert torch.equal(restored["codes"]["ref"], torch.tensor([6.0]))
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assert restored["ids"]["all"] == [1, 2]
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assert restored["ids"]["prompt"] == [3, 4]
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assert restored["meta"]["finished"].item() is False
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assert restored["meta"]["ar_width"] == 8
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class TestSerializeDeserializePayload:
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def test_tensor_round_trip(self):
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original: OmniPayload = {
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"hidden_states": {"output": torch.tensor([[1.0, 2.0], [3.0, 4.0]])},
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}
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wire = serialize_payload(original)
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assert isinstance(wire, AdditionalInformationPayload)
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restored = deserialize_payload(wire)
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assert torch.equal(restored["hidden_states"]["output"], original["hidden_states"]["output"])
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def test_list_round_trip(self):
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original: OmniPayload = {
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"ids": {"prompt": [10, 20, 30]},
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}
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wire = serialize_payload(original)
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restored = deserialize_payload(wire)
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assert restored["ids"]["prompt"] == [10, 20, 30]
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def test_finished_tensor_round_trip(self):
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original: OmniPayload = {
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"meta": {"finished": torch.tensor(True, dtype=torch.bool), "left_context_size": 5},
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}
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wire = serialize_payload(original)
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restored = deserialize_payload(wire)
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assert isinstance(restored["meta"]["finished"], torch.Tensor)
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assert restored["meta"]["finished"].dtype == torch.bool
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assert restored["meta"]["finished"].item() is True
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assert restored["meta"]["left_context_size"] == 5
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def test_mixed_types_round_trip(self):
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original: OmniPayload = {
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"hidden_states": {"output": torch.tensor([1.0, 2.0])},
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"ids": {"all": [1, 2, 3]},
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"meta": {"finished": torch.tensor(False, dtype=torch.bool), "ar_width": 4},
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"codes": {"audio": torch.tensor([3.0])},
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}
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wire = serialize_payload(original)
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restored = deserialize_payload(wire)
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assert torch.equal(restored["hidden_states"]["output"], original["hidden_states"]["output"])
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assert restored["ids"]["all"] == [1, 2, 3]
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assert restored["meta"]["finished"].item() is False
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assert restored["meta"]["ar_width"] == 4
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assert torch.equal(restored["codes"]["audio"], original["codes"]["audio"])
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def test_hidden_states_layers_round_trip(self):
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original = {
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"hidden_states": {
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"output": torch.tensor([1.0]),
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"layers": {0: torch.tensor([2.0]), 24: torch.tensor([3.0])},
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},
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}
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wire = serialize_payload(original)
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restored = deserialize_payload(wire)
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assert torch.equal(restored["hidden_states"]["output"], torch.tensor([1.0]))
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assert torch.equal(restored["hidden_states"]["layers"][0], torch.tensor([2.0]))
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assert torch.equal(restored["hidden_states"]["layers"][24], torch.tensor([3.0]))
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def test_tensor_dtype_preserved(self):
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# bfloat16 excluded: numpy() doesn't support it; callers must cast before serializing.
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for dtype in [torch.float16, torch.float32, torch.int64, torch.int32, torch.bool]:
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original: OmniPayload = {"codes": {"audio": torch.tensor([1], dtype=dtype)}}
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wire = serialize_payload(original)
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restored = deserialize_payload(wire)
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assert restored["codes"]["audio"].dtype == dtype, f"dtype mismatch for {dtype}"
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def test_tensor_shape_preserved(self):
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t = torch.randn(3, 4, 5)
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original: OmniPayload = {"hidden_states": {"output": t}}
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wire = serialize_payload(original)
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restored = deserialize_payload(wire)
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assert restored["hidden_states"]["output"].shape == (3, 4, 5)
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assert torch.allclose(restored["hidden_states"]["output"], t)
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def test_empty_payload_returns_none(self):
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assert serialize_payload({}) is None
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def test_none_values_skipped(self):
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original: OmniPayload = {"meta": {"finished": None}}
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wire = serialize_payload(original)
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assert wire is None
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