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fix(cost)!: bill transcriptions by the seconds the provider reports (#50727)
* fix(cost): bill transcriptions by the seconds the provider reports * Update litellm/cost_calculator.py sure Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> --------- Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
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ryan-crabbe-berri
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4 changed files with 50 additions and 95 deletions
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@ -103,9 +103,6 @@ from litellm.llms.openai.cost_calculation import (
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from litellm.llms.openai.cost_calculation import (
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video_output_cost_per_second,
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)
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from litellm.llms.openai.transcriptions.gpt_transformation import (
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OpenAIGPTAudioTranscriptionConfig,
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)
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from litellm.llms.perplexity.cost_calculator import (
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cost_per_token as perplexity_cost_per_token,
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)
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@ -381,38 +378,15 @@ def _transcription_uses_token_pricing(
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return (prompt_tokens_val > 0) or (completion_tokens_val > 0)
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def _bills_transcription_usage_duration(pricing_model: str | None, custom_llm_provider: str | None) -> bool:
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if pricing_model is None or custom_llm_provider is None:
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return False
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transcription_info: Final = _lookup_model_info_or_none(pricing_model, custom_llm_provider)
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transcription_metadata: Final = (
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as_str_mapping(transcription_info.get("provider_specific_entry")) if transcription_info is not None else None
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)
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return transcription_metadata is not None and transcription_metadata.get("transcription_usage_duration") == 1
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def _transcription_duration_for_cost(
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completion_response: object, pricing_model: str | None, model: str | None, custom_llm_provider: str | None
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) -> float:
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def _transcription_duration_for_cost(completion_response: object) -> float:
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billed_duration: Final = _get_transcription_usage_duration(completion_response)
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if billed_duration is not None and billed_duration > 0:
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return billed_duration
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hidden_params: Final = as_str_mapping(attribute_or_none(completion_response, "_hidden_params")) or {}
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measured_duration: Final = normalized_audio_duration_seconds(
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hidden_params.get("audio_transcription_duration", attribute_or_none(completion_response, "duration"))
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)
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if measured_duration is not None and measured_duration > 0:
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return measured_duration
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provider_duration: Final = _get_transcription_usage_duration(completion_response)
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if (
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provider_duration is not None
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and provider_duration > 0
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and any(_bills_transcription_usage_duration(name, custom_llm_provider) for name in (pricing_model, model))
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):
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return provider_duration
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if model is None or custom_llm_provider != LlmProviders.OPENAI.value:
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return 0.0
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transcription_config: Final = ProviderConfigManager.get_provider_audio_transcription_config(
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model=model.removeprefix(f"{LlmProviders.OPENAI.value}/"), provider=LlmProviders.OPENAI
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)
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return (provider_duration or 0.0) if isinstance(transcription_config, OpenAIGPTAudioTranscriptionConfig) else 0.0
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return measured_duration if measured_duration is not None and measured_duration > 0 else 0.0
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OCRPricingField = Literal["ocr_cost_per_page", "ocr_cost_per_credit", "annotation_cost_per_page"]
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@ -2132,9 +2106,7 @@ def completion_cost(
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prompt,
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)
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elif call_type in _TRANSCRIPTION_CALL_TYPES:
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audio_transcription_file_duration = _transcription_duration_for_cost(
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completion_response, model, requested_model, custom_llm_provider
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)
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audio_transcription_file_duration = _transcription_duration_for_cost(completion_response)
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elif call_type in _RERANK_CALL_TYPES:
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if completion_response is not None and isinstance(completion_response, RerankResponse):
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meta_obj = completion_response.meta
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@ -885,7 +885,7 @@ async def _spend_for_one_second_wav(
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@pytest.mark.asyncio
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async def test_custom_named_gpt_transcribe_deployment_bills_the_measured_upload_over_reported_seconds(
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async def test_custom_named_gpt_transcribe_deployment_bills_the_reported_seconds_over_the_measured_upload(
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local_model_cost_map: None, monkeypatch: pytest.MonkeyPatch
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) -> None:
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router: Final = litellm.Router(
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@ -902,7 +902,7 @@ async def test_custom_named_gpt_transcribe_deployment_bills_the_measured_upload_
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monkeypatch, {"text": "hello", "usage": {"type": "duration", "seconds": 2}}, router
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)
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assert spend.cost == pytest.approx(_gpt_transcribe_cost_per_second())
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assert spend.cost == pytest.approx(2 * _gpt_transcribe_cost_per_second())
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@pytest.mark.asyncio
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@ -129,7 +129,7 @@ class TestCostCalculatorReadsDurationFromHiddenParams:
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@patch( # test-quality-ok: isolates duration selection from the model pricing table
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"litellm.cost_calculator.openai_cost_per_second"
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)
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def test_completion_cost_prefers_measured_duration_over_provider_usage_duration(self, mock_cost_fn):
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def test_completion_cost_prefers_provider_usage_duration_over_measured_duration(self, mock_cost_fn):
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mock_cost_fn.return_value = (0.001, 0.0)
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response = TranscriptionResponse(
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text="test",
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@ -148,7 +148,7 @@ class TestCostCalculatorReadsDurationFromHiddenParams:
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mock_cost_fn.assert_called_once()
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_, kwargs = mock_cost_fn.call_args
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assert kwargs["duration"] == 17.5
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assert kwargs["duration"] == 60.0
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@pytest.mark.parametrize(
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("model", "provider"),
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@ -7532,82 +7532,65 @@ def test_whisper_azure():
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assert round(cost, 5) == round(expected_cost, 5)
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@pytest.mark.parametrize(
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("provider", "requested_model", "response_extras", "expected_seconds"),
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[
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("openai", "openai/gpt-fake-transcribe", {}, 2.0),
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("openai", "openai/gpt-fake-transcribe", {"_audio_transcription_duration": 1.35}, 1.35),
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("openai", "openai/gpt-fake-transcribe", {"duration": 1.34}, 1.34),
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("openai", "openai/whisper-fake", {}, 0.0),
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("azure", "gpt-fake-transcribe", {}, 0.0),
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],
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ids=[
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"undecodable_upload_bills_provider_seconds",
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"measured_duration_still_wins",
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"provider_duration_still_wins",
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"whisper_models_never_bill_provider_seconds",
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"azure_never_bills_provider_seconds",
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],
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)
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def test_transcription_cost_falls_back_to_provider_billed_seconds(
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monkeypatch: pytest.MonkeyPatch,
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provider: str,
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requested_model: str,
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response_extras: Mapping[str, float],
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expected_seconds: float,
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) -> None:
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model: Final = requested_model.removeprefix(f"{provider}/")
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def _per_second_transcription(
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monkeypatch: pytest.MonkeyPatch, provider: str, model: str, response_object: Mapping[str, object]
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) -> TranscriptionResponse:
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monkeypatch.setitem(
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litellm.model_cost,
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model,
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{"input_cost_per_second": 0.01, "litellm_provider": provider, "mode": "audio_transcription"},
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)
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transcription: Final = convert_to_model_response_object(
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response_object={
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"text": "What is the weather in Paris?",
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"usage": {"type": "duration", "seconds": 2},
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**response_extras,
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},
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return convert_to_model_response_object(
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response_object={"text": "What is the weather in Paris?", **response_object},
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model_response_object=TranscriptionResponse(),
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hidden_params={"model": model, "custom_llm_provider": provider},
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response_type="audio_transcription",
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)
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cost: Final = completion_cost(
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completion_response=transcription, model=requested_model, call_type="atranscription"
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@pytest.mark.parametrize(
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("provider", "model", "measured"),
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[
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("openai", "whisper-fake", {}),
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("openai", "whisper-fake", {"_audio_transcription_duration": 2.4}),
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("openai", "whisper-fake", {"duration": 2.4}),
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("openai", "gpt-fake-transcribe", {"_audio_transcription_duration": 2.4}),
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("azure", "whisper-fake", {"_audio_transcription_duration": 2.4}),
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],
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ids=[
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"unmeasured_upload",
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"measured_upload",
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"verbose_json_duration",
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"gpt_transcribe_model",
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"azure",
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],
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)
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def test_transcription_cost_bills_the_seconds_the_provider_billed(
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monkeypatch: pytest.MonkeyPatch, provider: str, model: str, measured: Mapping[str, float]
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) -> None:
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transcription: Final = _per_second_transcription(
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monkeypatch, provider, model, {"usage": {"type": "duration", "seconds": 3}, **measured}
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)
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assert cost == pytest.approx(0.01 * expected_seconds)
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cost: Final = completion_cost(
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completion_response=transcription, model=f"{provider}/{model}", call_type="atranscription"
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)
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assert cost == pytest.approx(0.01 * 3)
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@pytest.mark.parametrize(
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("provider", "measured_seconds", "expected_seconds"),
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[("openai", 1.0, 1.0), ("azure", 1.0, 1.0), ("azure", None, 7.0)],
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ids=["openai_measured_upload_wins", "azure_measured_upload_wins", "unmeasured_upload_bills_provider_seconds"],
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("measured", "expected_seconds"),
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[({"_audio_transcription_duration": 2.4}, 2.4), ({"duration": 2.4}, 2.4), ({}, 0.0)],
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ids=["measured_upload", "reply_duration", "nothing_known"],
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)
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def test_transcription_priced_by_provider_duration_still_bills_the_measured_upload(
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monkeypatch: pytest.MonkeyPatch, provider: str, measured_seconds: float | None, expected_seconds: float
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def test_transcription_cost_without_provider_seconds_bills_the_measured_length(
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monkeypatch: pytest.MonkeyPatch, measured: Mapping[str, float], expected_seconds: float
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) -> None:
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monkeypatch.setitem(
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litellm.model_cost,
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"gpt-fake-transcribe",
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{
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"input_cost_per_second": 0.01,
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"litellm_provider": provider,
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"mode": "audio_transcription",
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"provider_specific_entry": {"transcription_usage_duration": 1},
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},
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)
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measured: Final = {} if measured_seconds is None else {"audio_transcription_duration": measured_seconds}
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transcription: Final = convert_to_model_response_object(
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response_object={"text": "What is the weather in Paris?", "usage": {"type": "duration", "seconds": 7}},
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model_response_object=TranscriptionResponse(),
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hidden_params={"model": "gpt-fake-transcribe", "custom_llm_provider": provider, **measured},
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response_type="audio_transcription",
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)
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transcription: Final = _per_second_transcription(monkeypatch, "openai", "whisper-fake", measured)
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cost: Final = completion_cost(
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completion_response=transcription, model=f"{provider}/gpt-fake-transcribe", call_type="atranscription"
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completion_response=transcription, model="openai/whisper-fake", call_type="atranscription"
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)
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assert cost == pytest.approx(0.01 * expected_seconds)
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