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llama-stack-mirror/tests/verifications/test_results/fireworks.json
ehhuang 2fcb70b789
test(verification): overwrite test result instead of creating new ones (#1934) 
# What does this PR do?


## Test Plan
(myenv) ➜ llama-stack python tests/verifications/generate_report.py
--providers fireworks,together,openai --run-tests
2025-04-10 16:59:28 -07:00

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"longrepr": "request = <FixtureRequest for <Function test_chat_non_streaming_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-case0]>>\nopenai_client = <openai.OpenAI object at 0x123af3ac0>\nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_non_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=False,\n )\n \n assert response.choices[0].message.role == \"assistant\"\n> assert len(response.choices[0].message.tool_calls) > 0\nE TypeError: object of type 'NoneType' has no len()\n\ntests/verifications/openai_api/test_chat_completion.py:223: TypeError"
},
"teardown": {
"duration": 0.0004129580920562148,
"outcome": "passed"
}
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"nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_non_streaming_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]",
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"llama-stack",
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"metadata": {
"model": "accounts/fireworks/models/llama4-maverick-instruct-basic",
"case_id": "case0"
},
"setup": {
"duration": 0.01656208303757012,
"outcome": "passed"
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"call": {
"duration": 22.76337137504015,
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"crash": {
"path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py",
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"message": "TypeError: object of type 'NoneType' has no len()"
},
"traceback": [
{
"path": "tests/verifications/openai_api/test_chat_completion.py",
"lineno": 223,
"message": "TypeError"
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],
"longrepr": "request = <FixtureRequest for <Function test_chat_non_streaming_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]>>\nopenai_client = <openai.OpenAI object at 0x1231f0460>\nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_non_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n response = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=False,\n )\n \n assert response.choices[0].message.role == \"assistant\"\n> assert len(response.choices[0].message.tool_calls) > 0\nE TypeError: object of type 'NoneType' has no len()\n\ntests/verifications/openai_api/test_chat_completion.py:223: TypeError"
},
"teardown": {
"duration": 0.00038704206235706806,
"outcome": "passed"
}
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{
"nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]",
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],
"metadata": {
"model": "accounts/fireworks/models/llama-v3p3-70b-instruct",
"case_id": "case0"
},
"setup": {
"duration": 0.015727541991509497,
"outcome": "passed"
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"call": {
"duration": 0.5719050420448184,
"outcome": "failed",
"crash": {
"path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py",
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"message": "assert 0 == 1\n + where 0 = len({})"
},
"traceback": [
{
"path": "tests/verifications/openai_api/test_chat_completion.py",
"lineno": 274,
"message": "AssertionError"
}
],
"longrepr": "request = <FixtureRequest for <Function test_chat_streaming_tool_calling[accounts/fireworks/models/llama-v3p3-70b-instruct-case0]>>\nopenai_client = <openai.OpenAI object at 0x122ef2bc0>\nmodel = 'accounts/fireworks/models/llama-v3p3-70b-instruct'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=True,\n )\n \n # Accumulate partial tool_calls here\n tool_calls_buffer = {}\n current_id = None\n # Process streaming chunks\n for chunk in stream:\n choice = chunk.choices[0]\n delta = choice.delta\n \n if delta.tool_calls is None:\n continue\n \n for tool_call_delta in delta.tool_calls:\n if tool_call_delta.id:\n current_id = tool_call_delta.id\n call_id = current_id\n func_delta = tool_call_delta.function\n \n if call_id not in tool_calls_buffer:\n tool_calls_buffer[call_id] = {\n \"id\": call_id,\n \"type\": tool_call_delta.type,\n \"name\": func_delta.name,\n \"arguments\": \"\",\n }\n \n if func_delta.arguments:\n tool_calls_buffer[call_id][\"arguments\"] += func_delta.arguments\n \n> assert len(tool_calls_buffer) == 1\nE assert 0 == 1\nE + where 0 = len({})\n\ntests/verifications/openai_api/test_chat_completion.py:274: AssertionError"
},
"teardown": {
"duration": 0.0003532909322530031,
"outcome": "passed"
}
},
{
"nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-case0]",
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"outcome": "failed",
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"test_chat_completion.py",
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],
"metadata": {
"model": "accounts/fireworks/models/llama4-scout-instruct-basic",
"case_id": "case0"
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"setup": {
"duration": 0.011914041941054165,
"outcome": "passed"
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"call": {
"duration": 5.403063916950487,
"outcome": "failed",
"crash": {
"path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py",
"lineno": 274,
"message": "assert 0 == 1\n + where 0 = len({})"
},
"traceback": [
{
"path": "tests/verifications/openai_api/test_chat_completion.py",
"lineno": 274,
"message": "AssertionError"
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],
"longrepr": "request = <FixtureRequest for <Function test_chat_streaming_tool_calling[accounts/fireworks/models/llama4-scout-instruct-basic-case0]>>\nopenai_client = <openai.OpenAI object at 0x1231f19c0>\nmodel = 'accounts/fireworks/models/llama4-scout-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=True,\n )\n \n # Accumulate partial tool_calls here\n tool_calls_buffer = {}\n current_id = None\n # Process streaming chunks\n for chunk in stream:\n choice = chunk.choices[0]\n delta = choice.delta\n \n if delta.tool_calls is None:\n continue\n \n for tool_call_delta in delta.tool_calls:\n if tool_call_delta.id:\n current_id = tool_call_delta.id\n call_id = current_id\n func_delta = tool_call_delta.function\n \n if call_id not in tool_calls_buffer:\n tool_calls_buffer[call_id] = {\n \"id\": call_id,\n \"type\": tool_call_delta.type,\n \"name\": func_delta.name,\n \"arguments\": \"\",\n }\n \n if func_delta.arguments:\n tool_calls_buffer[call_id][\"arguments\"] += func_delta.arguments\n \n> assert len(tool_calls_buffer) == 1\nE assert 0 == 1\nE + where 0 = len({})\n\ntests/verifications/openai_api/test_chat_completion.py:274: AssertionError"
},
"teardown": {
"duration": 0.0005193749675527215,
"outcome": "passed"
}
},
{
"nodeid": "tests/verifications/openai_api/test_chat_completion.py::test_chat_streaming_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]",
"lineno": 228,
"outcome": "failed",
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"test_chat_streaming_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]",
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"metadata": {
"model": "accounts/fireworks/models/llama4-maverick-instruct-basic",
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"setup": {
"duration": 0.012608832912519574,
"outcome": "passed"
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"call": {
"duration": 7.587262416025624,
"outcome": "failed",
"crash": {
"path": "/Users/erichuang/projects/llama-stack/tests/verifications/openai_api/test_chat_completion.py",
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"message": "assert 0 == 1\n + where 0 = len({})"
},
"traceback": [
{
"path": "tests/verifications/openai_api/test_chat_completion.py",
"lineno": 274,
"message": "AssertionError"
}
],
"longrepr": "request = <FixtureRequest for <Function test_chat_streaming_tool_calling[accounts/fireworks/models/llama4-maverick-instruct-basic-case0]>>\nopenai_client = <openai.OpenAI object at 0x123a56d70>\nmodel = 'accounts/fireworks/models/llama4-maverick-instruct-basic'\nprovider = 'fireworks'\nverification_config = {'providers': {'cerebras': {'api_key_var': 'CEREBRAS_API_KEY', 'base_url': 'https://api.cerebras.ai/v1', 'model_displa...el_display_names': {'gpt-4o': 'gpt-4o', 'gpt-4o-mini': 'gpt-4o-mini'}, 'models': ['gpt-4o', 'gpt-4o-mini'], ...}, ...}}\ncase = {'input': {'messages': [{'content': 'You are a helpful assistant that can use tools to get information.', 'role': 'sys..., 'properties': {...}, 'required': [...], 'type': 'object'}}, 'type': 'function'}]}, 'output': 'get_weather_tool_call'}\n\n @pytest.mark.parametrize(\n \"case\",\n chat_completion_test_cases[\"test_tool_calling\"][\"test_params\"][\"case\"],\n ids=case_id_generator,\n )\n def test_chat_streaming_tool_calling(request, openai_client, model, provider, verification_config, case):\n test_name_base = get_base_test_name(request)\n if should_skip_test(verification_config, provider, model, test_name_base):\n pytest.skip(f\"Skipping {test_name_base} for model {model} on provider {provider} based on config.\")\n \n stream = openai_client.chat.completions.create(\n model=model,\n messages=case[\"input\"][\"messages\"],\n tools=case[\"input\"][\"tools\"],\n stream=True,\n )\n \n # Accumulate partial tool_calls here\n tool_calls_buffer = {}\n current_id = None\n # Process streaming chunks\n for chunk in stream:\n choice = chunk.choices[0]\n delta = choice.delta\n \n if delta.tool_calls is None:\n continue\n \n for tool_call_delta in delta.tool_calls:\n if tool_call_delta.id:\n current_id = tool_call_delta.id\n call_id = current_id\n func_delta = tool_call_delta.function\n \n if call_id not in tool_calls_buffer:\n tool_calls_buffer[call_id] = {\n \"id\": call_id,\n \"type\": tool_call_delta.type,\n \"name\": func_delta.name,\n \"arguments\": \"\",\n }\n \n if func_delta.arguments:\n tool_calls_buffer[call_id][\"arguments\"] += func_delta.arguments\n \n> assert len(tool_calls_buffer) == 1\nE assert 0 == 1\nE + where 0 = len({})\n\ntests/verifications/openai_api/test_chat_completion.py:274: AssertionError"
},
"teardown": {
"duration": 0.0008685829816386104,
"outcome": "passed"
}
}
],
"run_timestamp": 1744328684
}
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