diff --git a/.github/actions/run-and-record-tests/action.yml b/.github/actions/run-and-record-tests/action.yml index 60550cfdc4..b2f64dcf16 100644 --- a/.github/actions/run-and-record-tests/action.yml +++ b/.github/actions/run-and-record-tests/action.yml @@ -58,9 +58,9 @@ runs: git add tests/integration/recordings/ if [ "${{ inputs.run-vision-tests }}" == "true" ]; then - git commit -m "Recordings update from CI (vision)" + git commit -m "Recordings update from CI (vision) (${{ inputs.provider }})" else - git commit -m "Recordings update from CI" + git commit -m "Recordings update from CI (${{ inputs.provider }})" fi git fetch origin ${{ github.ref_name }} @@ -76,7 +76,8 @@ runs: if: ${{ always() }} shell: bash run: | - sudo docker logs ollama > ollama-${{ inputs.inference-mode }}.log || true + sudo docker logs ollama > ollama-${{ inputs.inference-mode }}.log 2>&1 || true + sudo docker logs vllm > vllm-${{ inputs.inference-mode }}.log 2>&1 || true - name: Upload logs if: ${{ always() }} diff --git a/.github/workflows/integration-tests.yml b/.github/workflows/integration-tests.yml index 57e582b202..13222e6550 100644 --- a/.github/workflows/integration-tests.yml +++ b/.github/workflows/integration-tests.yml @@ -21,7 +21,6 @@ on: schedule: # If changing the cron schedule, update the provider in the test-matrix job - cron: '0 0 * * *' # (test latest client) Daily at 12 AM UTC - - cron: '1 0 * * 0' # (test vllm) Weekly on Sunday at 1 AM UTC workflow_dispatch: inputs: test-all-client-versions: @@ -47,7 +46,6 @@ concurrency: cancel-in-progress: true jobs: - run-replay-mode-tests: runs-on: ubuntu-latest name: ${{ format('Integration Tests ({0}, {1}, {2}, client={3}, vision={4})', matrix.client-type, matrix.provider, matrix.python-version, matrix.client-version, matrix.run-vision-tests) }} @@ -57,11 +55,14 @@ jobs: matrix: client-type: [library, server] # Use vllm on weekly schedule, otherwise use test-provider input (defaults to ollama) - provider: ${{ (github.event.schedule == '1 0 * * 0') && fromJSON('["vllm"]') || fromJSON(format('["{0}"]', github.event.inputs.test-provider || 'ollama')) }} + provider: [ollama, vllm] # Use Python 3.13 only on nightly schedule (daily latest client test), otherwise use 3.12 python-version: ${{ github.event.schedule == '0 0 * * *' && fromJSON('["3.12", "3.13"]') || fromJSON('["3.12"]') }} client-version: ${{ (github.event.schedule == '0 0 * * *' || github.event.inputs.test-all-client-versions == 'true') && fromJSON('["published", "latest"]') || fromJSON('["latest"]') }} run-vision-tests: [true, false] + exclude: + - provider: vllm + run-vision-tests: true steps: - name: Checkout repository diff --git a/llama_stack/testing/inference_recorder.py b/llama_stack/testing/inference_recorder.py index 4a69583999..195487b1b0 100644 --- a/llama_stack/testing/inference_recorder.py +++ b/llama_stack/testing/inference_recorder.py @@ -10,12 +10,16 @@ import json import os import sqlite3 +import uuid from collections.abc import Generator from contextlib import contextmanager from enum import StrEnum from pathlib import Path from typing import Any, Literal, cast +from openai.pagination import AsyncPage +from openai.types.chat import ChatCompletion, ChatCompletionChunk + from llama_stack.log import get_logger logger = get_logger(__name__, category="testing") @@ -248,6 +252,20 @@ async def _patched_inference_method(original_method, self, client_type, endpoint recording = _current_storage.find_recording(request_hash) if recording: response_body = recording["response"]["body"] + if ( + isinstance(response_body, list) + and len(response_body) > 0 + and isinstance(response_body[0], ChatCompletionChunk) + ): + # We can't replay chatcompletions with the same id and we store them in a sqlite database with a unique constraint on the id. + # So we generate a new id and replace the old one. + newid = uuid.uuid4().hex + response_body[0].id = "chatcmpl-" + newid + elif isinstance(response_body, ChatCompletion): + # We can't replay chatcompletions with the same id and we store them in a sqlite database with a unique constraint on the id. + # So we generate a new id and replace the old one. + newid = uuid.uuid4().hex + response_body.id = "chatcmpl-" + newid if recording["response"].get("is_streaming", False): @@ -279,7 +297,8 @@ async def replay_stream(): } # Determine if this is a streaming request based on request parameters - is_streaming = body.get("stream", False) + # or if the response is an AsyncPage (like models.list returns) + is_streaming = body.get("stream", False) or isinstance(response, AsyncPage) if is_streaming: # For streaming responses, we need to collect all chunks immediately before yielding @@ -315,9 +334,11 @@ def patch_inference_clients(): from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions from openai.resources.completions import AsyncCompletions from openai.resources.embeddings import AsyncEmbeddings + from openai.resources.models import AsyncModels # Store original methods for both OpenAI and Ollama clients _original_methods = { + "models_list": AsyncModels.list, "chat_completions_create": AsyncChatCompletions.create, "completions_create": AsyncCompletions.create, "embeddings_create": AsyncEmbeddings.create, @@ -329,7 +350,38 @@ def patch_inference_clients(): "ollama_list": OllamaAsyncClient.list, } - # Create patched methods for OpenAI client + # Special handling for models.list which needs to return something directly async-iterable + # Direct iteration: async for m in client.models.list() + # Await then iterate: res = await client.models.list(); async for m in res + def patched_models_list(self, *args, **kwargs): + class AsyncIterableModelsWrapper: + def __init__(self, original_method, client_self, args, kwargs): + self.original_method = original_method + self.client_self = client_self + self.args = args + self.kwargs = kwargs + self._result = None + + def __aiter__(self): + return self._async_iter() + + async def _async_iter(self): + # Get the result from the patched method + result = await _patched_inference_method( + self.original_method, self.client_self, "openai", "/v1/models", *self.args, **self.kwargs + ) + async for item in result: + yield item + + def __await__(self): + # When awaited, return self (since we're already async-iterable) + async def _return_self(): + return self + + return _return_self().__await__() + + return AsyncIterableModelsWrapper(_original_methods["models_list"], self, args, kwargs) + async def patched_chat_completions_create(self, *args, **kwargs): return await _patched_inference_method( _original_methods["chat_completions_create"], self, "openai", "/v1/chat/completions", *args, **kwargs @@ -346,6 +398,7 @@ async def patched_embeddings_create(self, *args, **kwargs): ) # Apply OpenAI patches + AsyncModels.list = patched_models_list AsyncChatCompletions.create = patched_chat_completions_create AsyncCompletions.create = patched_completions_create AsyncEmbeddings.create = patched_embeddings_create @@ -402,8 +455,10 @@ def unpatch_inference_clients(): from openai.resources.chat.completions import AsyncCompletions as AsyncChatCompletions from openai.resources.completions import AsyncCompletions from openai.resources.embeddings import AsyncEmbeddings + from openai.resources.models import AsyncModels # Restore OpenAI client methods + AsyncModels.list = _original_methods["models_list"] AsyncChatCompletions.create = _original_methods["chat_completions_create"] AsyncCompletions.create = _original_methods["completions_create"] AsyncEmbeddings.create = _original_methods["embeddings_create"] diff --git a/scripts/integration-tests.sh b/scripts/integration-tests.sh index e152444e1f..a2bae9a87b 100755 --- a/scripts/integration-tests.sh +++ b/scripts/integration-tests.sh @@ -193,7 +193,7 @@ EXCLUDE_TESTS="builtin_tool or safety_with_image or code_interpreter or test_rag # Additional exclusions for vllm provider if 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