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https://github.com/BerriAI/litellm.git
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test: use explicit prices in cost calculation checks
This commit is contained in:
Yuneng Jiang
parent
a63b543044
commit
0c466b05ce
2 changed files with 131 additions and 158 deletions
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@ -324,18 +324,15 @@ def test_reasoning_tokens_gemini(_local_model_cost_map):
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)
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def test_image_tokens_with_custom_pricing():
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def test_image_tokens_with_custom_pricing() -> None:
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"""Test that image_tokens in completion are properly costed with output_cost_per_image_token."""
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from unittest.mock import patch
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# Mock model info with image token pricing
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mock_model_info = {
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model_info: Final = {
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"input_cost_per_token": 1e-6,
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"output_cost_per_token": 2e-6,
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"output_cost_per_image_token": 5e-6, # Custom pricing for image tokens in output
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}
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usage = Usage(
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usage: Final = Usage(
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completion_tokens=1720, # text_tokens (600) + image_tokens (1120)
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prompt_tokens=14,
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total_tokens=1734,
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@ -352,36 +349,32 @@ def test_image_tokens_with_custom_pricing():
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),
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)
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with patch(
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"litellm.litellm_core_utils.llm_cost_calc.utils.get_model_info",
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return_value=mock_model_info,
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):
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prompt_cost, completion_cost = generic_cost_per_token(
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model="test-model", usage=usage, custom_llm_provider="gemini"
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)
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prompt_cost, completion_cost = generic_cost_per_token(
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model="test-model",
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usage=usage,
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custom_llm_provider="gemini",
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model_info=cast(ModelInfo, model_info),
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)
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# Expected costs:
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# Prompt: 14 * 1e-6
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# Completion: (600 * 2e-6) + (1120 * 5e-6)
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expected_prompt_cost = 14 * 1e-6
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expected_completion_cost = (600 * 2e-6) + (1120 * 5e-6)
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expected_prompt_cost: Final = 14 * 1e-6
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expected_completion_cost: Final = (600 * 2e-6) + (1120 * 5e-6)
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assert round(prompt_cost, 12) == round(expected_prompt_cost, 12)
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assert round(completion_cost, 12) == round(expected_completion_cost, 12)
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def test_image_tokens_fallback_to_base_cost():
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def test_image_tokens_fallback_to_base_cost() -> None:
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"""Test that image_tokens fall back to base cost when output_cost_per_image_token is not set."""
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from unittest.mock import patch
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# Mock model info without image token pricing
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mock_model_info = {
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model_info: Final = {
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"input_cost_per_token": 1e-6,
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"output_cost_per_token": 2e-6,
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# No output_cost_per_image_token defined
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}
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usage = Usage(
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usage: Final = Usage(
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completion_tokens=1720,
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prompt_tokens=14,
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total_tokens=1734,
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@ -398,19 +391,18 @@ def test_image_tokens_fallback_to_base_cost():
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),
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)
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with patch(
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"litellm.litellm_core_utils.llm_cost_calc.utils.get_model_info",
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return_value=mock_model_info,
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):
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prompt_cost, completion_cost = generic_cost_per_token(
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model="test-model", usage=usage, custom_llm_provider="gemini"
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)
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prompt_cost, completion_cost = generic_cost_per_token(
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model="test-model",
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usage=usage,
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custom_llm_provider="gemini",
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model_info=cast(ModelInfo, model_info),
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)
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# Expected costs:
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# Prompt: 14 * 1e-6
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# Completion: (600 * 2e-6) + (1120 * 2e-6) # image_tokens use base cost
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expected_prompt_cost = 14 * 1e-6
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expected_completion_cost = (600 * 2e-6) + (1120 * 2e-6)
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expected_prompt_cost: Final = 14 * 1e-6
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expected_completion_cost: Final = (600 * 2e-6) + (1120 * 2e-6)
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assert round(prompt_cost, 12) == round(expected_prompt_cost, 12)
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assert round(completion_cost, 12) == round(expected_completion_cost, 12)
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@ -441,16 +433,14 @@ def test_video_input_tokens_gemini_omni_flash_preview(_local_model_cost_map):
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)
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def test_video_tokens_fallback_to_base_cost():
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def test_video_tokens_fallback_to_base_cost() -> None:
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"""Video output tokens fall back to the base output rate when output_cost_per_video_token is not set."""
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from unittest.mock import patch
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mock_model_info = {
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model_info: Final = {
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"input_cost_per_token": 1e-6,
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"output_cost_per_token": 2e-6,
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}
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usage = Usage(
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usage: Final = Usage(
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completion_tokens=1720,
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prompt_tokens=14,
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total_tokens=1734,
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@ -461,30 +451,27 @@ def test_video_tokens_fallback_to_base_cost():
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prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=14),
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)
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with patch(
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"litellm.litellm_core_utils.llm_cost_calc.utils.get_model_info",
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return_value=mock_model_info,
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):
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prompt_cost, completion_cost = generic_cost_per_token(
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model="test-model", usage=usage, custom_llm_provider="gemini"
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)
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prompt_cost, completion_cost = generic_cost_per_token(
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model="test-model",
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usage=usage,
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custom_llm_provider="gemini",
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model_info=cast(ModelInfo, model_info),
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)
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assert round(prompt_cost, 12) == round(14 * 1e-6, 12)
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assert round(completion_cost, 12) == round((600 + 1120) * 2e-6, 12)
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def test_prompt_audio_and_video_tokens_without_modality_rates_bill_at_tiered_input_rate():
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def test_prompt_audio_and_video_tokens_without_modality_rates_bill_at_tiered_input_rate() -> None:
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"""Prompt audio/video tokens with no modality rate are prompt tokens, so a >200k prompt bills them above-200k."""
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from unittest.mock import patch
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mock_model_info = {
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model_info: Final = {
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"input_cost_per_token": 2e-6,
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"input_cost_per_token_above_200k_tokens": 4e-6,
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"output_cost_per_token": 1.2e-5,
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"output_cost_per_token_above_200k_tokens": 1.8e-5,
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}
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usage = Usage(
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usage: Final = Usage(
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prompt_tokens=816032,
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completion_tokens=4559,
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total_tokens=820591,
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@ -492,23 +479,20 @@ def test_prompt_audio_and_video_tokens_without_modality_rates_bill_at_tiered_inp
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completion_tokens_details=CompletionTokensDetailsWrapper(text_tokens=4559),
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)
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with patch(
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"litellm.litellm_core_utils.llm_cost_calc.utils.get_model_info",
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return_value=mock_model_info,
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):
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prompt_cost, completion_cost = generic_cost_per_token(
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model="test-model", usage=usage, custom_llm_provider="vertex_ai"
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)
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prompt_cost, completion_cost = generic_cost_per_token(
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model="test-model",
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usage=usage,
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custom_llm_provider="vertex_ai",
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model_info=cast(ModelInfo, model_info),
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)
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assert round(prompt_cost, 9) == round(816032 * 4e-6, 9)
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assert round(completion_cost, 9) == round(4559 * 1.8e-5, 9)
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def test_prompt_modality_rates_still_win_over_tiered_input_rate():
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def test_prompt_modality_rates_still_win_over_tiered_input_rate() -> None:
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"""An explicit input_cost_per_audio_token / input_cost_per_video_token beats the fallback, even above 200k."""
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from unittest.mock import patch
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mock_model_info = {
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model_info: Final = {
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"input_cost_per_token": 2e-6,
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"input_cost_per_token_above_200k_tokens": 4e-6,
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"input_cost_per_audio_token": 3e-6,
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@ -516,7 +500,7 @@ def test_prompt_modality_rates_still_win_over_tiered_input_rate():
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"output_cost_per_token": 1.2e-5,
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}
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usage = Usage(
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usage: Final = Usage(
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prompt_tokens=300000,
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completion_tokens=10,
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total_tokens=300010,
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@ -524,11 +508,12 @@ def test_prompt_modality_rates_still_win_over_tiered_input_rate():
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completion_tokens_details=CompletionTokensDetailsWrapper(text_tokens=10),
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)
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with patch(
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"litellm.litellm_core_utils.llm_cost_calc.utils.get_model_info",
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return_value=mock_model_info,
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):
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prompt_cost, _ = generic_cost_per_token(model="test-model", usage=usage, custom_llm_provider="vertex_ai")
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prompt_cost, _ = generic_cost_per_token(
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model="test-model",
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usage=usage,
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custom_llm_provider="vertex_ai",
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model_info=cast(ModelInfo, model_info),
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)
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assert round(prompt_cost, 9) == round(100000 * 4e-6 + 100000 * 3e-6 + 100000 * 1e-6, 9)
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@ -1748,12 +1733,9 @@ def test_gpt55_dated_variants_match_base_reasoning_effort_capabilities(_local_mo
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)
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def test_string_cost_values():
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def test_string_cost_values() -> None:
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"""Test that cost values defined as strings are properly converted to floats."""
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from unittest.mock import patch
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# Mock model info with string cost values (as might be read from config.yaml)
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mock_model_info = {
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model_info: Final = {
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"input_cost_per_token": "3e-7", # String representation of scientific notation
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"output_cost_per_token": "6e-7", # String representation of scientific notation
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"input_cost_per_audio_token": "0.000001", # String representation of decimal
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@ -1765,7 +1747,7 @@ def test_string_cost_values():
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# Test usage with various token types
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# Note: prompt_tokens must equal sum of details to avoid double-counting adjustment
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# text_tokens(700) + audio_tokens(100) + cached_tokens(200) + cache_creation_tokens(150) = 1150
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usage = Usage(
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usage: Final = Usage(
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prompt_tokens=1150,
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completion_tokens=500,
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total_tokens=1650,
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@ -1785,18 +1767,16 @@ def test_string_cost_values():
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),
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)
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# Mock get_model_info to return our mock model info
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with patch(
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"litellm.litellm_core_utils.llm_cost_calc.utils.get_model_info",
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return_value=mock_model_info,
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):
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prompt_cost, completion_cost = generic_cost_per_token(
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model="test-model", usage=usage, custom_llm_provider="test-provider"
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)
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prompt_cost, completion_cost = generic_cost_per_token(
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model="test-model",
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usage=usage,
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custom_llm_provider="test-provider",
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model_info=cast(ModelInfo, model_info),
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)
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# Calculate expected costs manually
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# Prompt cost = text_tokens * input_cost + audio_tokens * audio_cost + cached_tokens * cache_read_cost + cache_creation_tokens * cache_creation_cost
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expected_prompt_cost = (
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expected_prompt_cost: Final = (
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700 * 3e-7 # text tokens
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+ 100 * 1e-6 # audio tokens
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+ 200 * 1.5e-8 # cached tokens
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@ -1804,7 +1784,7 @@ def test_string_cost_values():
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)
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# Completion cost = text_tokens * output_cost + audio_tokens * audio_output_cost
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expected_completion_cost = 450 * 6e-7 + 50 * 2e-6 # text tokens # audio tokens
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expected_completion_cost: Final = 450 * 6e-7 + 50 * 2e-6 # text tokens # audio tokens
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# Assert costs are calculated correctly
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assert round(prompt_cost, 12) == round(expected_prompt_cost, 12)
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@ -1918,19 +1898,17 @@ def test_calculate_cost_component_with_string_values():
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assert cost == 0.0
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def test_string_cost_values_edge_cases():
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def test_string_cost_values_edge_cases() -> None:
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"""Test edge cases for string cost value handling."""
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from unittest.mock import patch
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# Test with mixed string and float cost values
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mock_model_info = {
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model_info: Final = {
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"input_cost_per_token": "1e-6", # String
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"output_cost_per_token": 2e-6, # Float
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"input_cost_per_audio_token": "invalid", # Invalid string
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"output_cost_per_audio_token": None, # None value
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}
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usage = Usage(
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usage: Final = Usage(
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prompt_tokens=1000,
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completion_tokens=500,
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total_tokens=1500,
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@ -1946,30 +1924,26 @@ def test_string_cost_values_edge_cases():
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),
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)
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with patch(
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"litellm.litellm_core_utils.llm_cost_calc.utils.get_model_info",
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return_value=mock_model_info,
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):
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prompt_cost, completion_cost = generic_cost_per_token(
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model="test-model", usage=usage, custom_llm_provider="test-provider"
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)
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prompt_cost, completion_cost = generic_cost_per_token(
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model="test-model",
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usage=usage,
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custom_llm_provider="test-provider",
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model_info=cast(ModelInfo, model_info),
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)
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# Expected costs:
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# Prompt: 1000 * 1e-6 + 100 * 0 (invalid string becomes 0)
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# Completion: 500 * 2e-6 + 50 * 2e-6 (audio tokens fall back to base cost when output_cost_per_audio_token is None)
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expected_prompt_cost = 1000 * 1e-6
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expected_completion_cost = 500 * 2e-6 + 50 * 2e-6
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expected_prompt_cost: Final = 1000 * 1e-6
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expected_completion_cost: Final = 500 * 2e-6 + 50 * 2e-6
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assert round(prompt_cost, 12) == round(expected_prompt_cost, 12)
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assert round(completion_cost, 12) == round(expected_completion_cost, 12)
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def test_string_cost_values_with_threshold():
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def test_string_cost_values_with_threshold() -> None:
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"""Test that string cost values work correctly with threshold pricing."""
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from unittest.mock import patch
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# Mock model info with string cost values including threshold pricing
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mock_model_info = {
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model_info: Final = {
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"input_cost_per_token": "1e-6", # String base cost
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"output_cost_per_token": "2e-6", # String base cost
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"input_cost_per_token_above_200k_tokens": "5e-7", # String threshold cost (lower)
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@ -1977,23 +1951,22 @@ def test_string_cost_values_with_threshold():
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}
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# Test usage above threshold
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usage = Usage(
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usage: Final = Usage(
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prompt_tokens=250000, # Above 200k threshold
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completion_tokens=1000,
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total_tokens=251000,
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)
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with patch(
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"litellm.litellm_core_utils.llm_cost_calc.utils.get_model_info",
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return_value=mock_model_info,
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):
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prompt_cost, completion_cost = generic_cost_per_token(
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model="test-model", usage=usage, custom_llm_provider="test-provider"
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)
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prompt_cost, completion_cost = generic_cost_per_token(
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model="test-model",
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usage=usage,
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custom_llm_provider="test-provider",
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model_info=cast(ModelInfo, model_info),
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)
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# Expected costs using threshold pricing (string values converted to float)
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expected_prompt_cost = 250000 * 5e-7 # threshold cost
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expected_completion_cost = 1000 * 1e-6 # threshold cost
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expected_prompt_cost: Final = 250000 * 5e-7 # threshold cost
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expected_completion_cost: Final = 1000 * 1e-6 # threshold cost
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assert round(prompt_cost, 12) == round(expected_prompt_cost, 12)
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assert round(completion_cost, 12) == round(expected_completion_cost, 12)
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@ -1,26 +1,28 @@
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import litellm
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from typing import Final
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import pytest
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import litellm
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from litellm.cost_calculator import completion_cost
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from litellm.llms.base_llm.ocr.transformation import OCRPage, OCRResponse, OCRUsageInfo
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def test_ocr_cost_prefers_credit_pricing_when_pages_processed_is_none(monkeypatch):
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monkeypatch.setattr(
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litellm,
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"get_model_info",
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lambda model, custom_llm_provider=None: {"ocr_cost_per_credit": 0.003},
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def test_ocr_cost_prefers_credit_pricing_when_pages_processed_is_none() -> None:
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model: Final = "litellm-test-credit-ocr"
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litellm.register_model(
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{f"reducto/{model}": {"litellm_provider": "reducto", "mode": "ocr", "ocr_cost_per_credit": 0.003}}
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)
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response = OCRResponse(
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response: Final = OCRResponse(
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pages=[OCRPage(index=0, markdown="credit priced")],
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model="parse-v3",
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model=model,
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usage_info=OCRUsageInfo(pages_processed=None, credits=10),
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)
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cost = completion_cost(
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cost: Final = completion_cost(
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completion_response=response,
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model="reducto/parse-v3",
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model=f"reducto/{model}",
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custom_llm_provider="reducto",
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call_type="ocr",
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)
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@ -28,25 +30,28 @@ def test_ocr_cost_prefers_credit_pricing_when_pages_processed_is_none(monkeypatc
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assert cost == 0.03
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def test_ocr_cost_prefers_zero_credit_pricing_over_page_pricing(monkeypatch):
|
||||
monkeypatch.setattr(
|
||||
litellm,
|
||||
"get_model_info",
|
||||
lambda model, custom_llm_provider=None: {
|
||||
"ocr_cost_per_credit": 0.0,
|
||||
"ocr_cost_per_page": 0.5,
|
||||
},
|
||||
def test_ocr_cost_prefers_zero_credit_pricing_over_page_pricing() -> None:
|
||||
model: Final = "litellm-test-zero-credit-ocr"
|
||||
litellm.register_model(
|
||||
{
|
||||
f"reducto/{model}": {
|
||||
"litellm_provider": "reducto",
|
||||
"mode": "ocr",
|
||||
"ocr_cost_per_credit": 0.0,
|
||||
"ocr_cost_per_page": 0.5,
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
response = OCRResponse(
|
||||
response: Final = OCRResponse(
|
||||
pages=[OCRPage(index=0, markdown="free credit priced")],
|
||||
model="parse-v3",
|
||||
model=model,
|
||||
usage_info=OCRUsageInfo(pages_processed=2, credits=10),
|
||||
)
|
||||
|
||||
cost = completion_cost(
|
||||
cost: Final = completion_cost(
|
||||
completion_response=response,
|
||||
model="reducto/parse-v3",
|
||||
model=f"reducto/{model}",
|
||||
custom_llm_provider="reducto",
|
||||
call_type="ocr",
|
||||
)
|
||||
|
|
@ -54,22 +59,21 @@ def test_ocr_cost_prefers_zero_credit_pricing_over_page_pricing(monkeypatch):
|
|||
assert cost == 0.0
|
||||
|
||||
|
||||
def test_ocr_cost_falls_back_to_page_pricing(monkeypatch):
|
||||
monkeypatch.setattr(
|
||||
litellm,
|
||||
"get_model_info",
|
||||
lambda model, custom_llm_provider=None: {"ocr_cost_per_page": 0.5},
|
||||
def test_ocr_cost_falls_back_to_page_pricing() -> None:
|
||||
model: Final = "litellm-test-page-ocr"
|
||||
litellm.register_model(
|
||||
{f"mistral/{model}": {"litellm_provider": "mistral", "mode": "ocr", "ocr_cost_per_page": 0.5}}
|
||||
)
|
||||
|
||||
response = OCRResponse(
|
||||
response: Final = OCRResponse(
|
||||
pages=[OCRPage(index=0, markdown="page priced")],
|
||||
model="mistral-ocr-latest",
|
||||
model=model,
|
||||
usage_info=OCRUsageInfo(pages_processed=2),
|
||||
)
|
||||
|
||||
cost = completion_cost(
|
||||
cost: Final = completion_cost(
|
||||
completion_response=response,
|
||||
model="mistral/mistral-ocr-latest",
|
||||
model=f"mistral/{model}",
|
||||
custom_llm_provider="mistral",
|
||||
call_type="ocr",
|
||||
)
|
||||
|
|
@ -77,22 +81,19 @@ def test_ocr_cost_falls_back_to_page_pricing(monkeypatch):
|
|||
assert cost == 1.0
|
||||
|
||||
|
||||
def test_ocr_cost_returns_zero_when_no_pricing_and_no_pages(monkeypatch):
|
||||
monkeypatch.setattr(
|
||||
litellm,
|
||||
"get_model_info",
|
||||
lambda model, custom_llm_provider=None: {},
|
||||
)
|
||||
def test_ocr_cost_returns_zero_when_no_pricing_and_no_pages() -> None:
|
||||
model: Final = "litellm-test-unpriced-ocr"
|
||||
litellm.register_model({f"reducto/{model}": {"litellm_provider": "reducto", "mode": "ocr"}})
|
||||
|
||||
response = OCRResponse(
|
||||
response: Final = OCRResponse(
|
||||
pages=[OCRPage(index=0, markdown="unpriced")],
|
||||
model="parse-v3",
|
||||
model=model,
|
||||
usage_info=OCRUsageInfo(pages_processed=None, credits=5),
|
||||
)
|
||||
|
||||
cost = completion_cost(
|
||||
cost: Final = completion_cost(
|
||||
completion_response=response,
|
||||
model="reducto/parse-v3",
|
||||
model=f"reducto/{model}",
|
||||
custom_llm_provider="reducto",
|
||||
call_type="ocr",
|
||||
)
|
||||
|
|
@ -100,23 +101,22 @@ def test_ocr_cost_returns_zero_when_no_pricing_and_no_pages(monkeypatch):
|
|||
assert cost == 0.0
|
||||
|
||||
|
||||
def test_ocr_cost_raises_when_pages_processed_missing_for_page_pricing(monkeypatch):
|
||||
monkeypatch.setattr(
|
||||
litellm,
|
||||
"get_model_info",
|
||||
lambda model, custom_llm_provider=None: {"ocr_cost_per_page": 0.5},
|
||||
def test_ocr_cost_raises_when_pages_processed_missing_for_page_pricing() -> None:
|
||||
model: Final = "litellm-test-missing-page-ocr"
|
||||
litellm.register_model(
|
||||
{f"mistral/{model}": {"litellm_provider": "mistral", "mode": "ocr", "ocr_cost_per_page": 0.5}}
|
||||
)
|
||||
|
||||
response = OCRResponse(
|
||||
response: Final = OCRResponse(
|
||||
pages=[OCRPage(index=0, markdown="missing pages")],
|
||||
model="mistral-ocr-latest",
|
||||
model=model,
|
||||
usage_info=OCRUsageInfo(pages_processed=None),
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="OCR response pages_processed is None"):
|
||||
completion_cost(
|
||||
completion_response=response,
|
||||
model="mistral/mistral-ocr-latest",
|
||||
model=f"mistral/{model}",
|
||||
custom_llm_provider="mistral",
|
||||
call_type="ocr",
|
||||
)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue