remove stress test

tests/integration/responses/test_conversation_stress.py

@@ -1,128 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-"""
-Opt-in concurrency stress tests for the Responses↔Conversations sync path.
-
-By default this module is skipped to avoid lengthening CI. Set the environment
-variable LLAMA_STACK_ENABLE_CONCURRENCY_TESTS=1 to run it locally. Optional
-knobs:
-
-    LLAMA_STACK_CONCURRENCY_WORKERS   (default: 4)
-    LLAMA_STACK_CONCURRENCY_TURNS     (default: 3)
-"""
-
-from __future__ import annotations
-
-import os
-from concurrent.futures import ThreadPoolExecutor, as_completed
-
-import pytest
-
-from llama_stack.core.testing_context import reset_test_context, set_test_context
-
-if not os.getenv("LLAMA_STACK_ENABLE_CONCURRENCY_TESTS"):
-    pytest.skip("Set LLAMA_STACK_ENABLE_CONCURRENCY_TESTS=1 to run stress tests", allow_module_level=True)
-
-
-CONCURRENCY_WORKERS = int(os.getenv("LLAMA_STACK_CONCURRENCY_WORKERS", "4"))
-TURNS_PER_WORKER = int(os.getenv("LLAMA_STACK_CONCURRENCY_TURNS", "1"))
-
-# Reuse the exact prompts from test_conversation_responses to hit existing recordings.
-FIRST_TURN_PROMPT = "Say hello"
-STREAMING_PROMPT = "Say goodbye"
-RECORDED_TEST_NODE_ID = (
-    "tests/integration/responses/test_conversation_responses.py::TestConversationResponses::"
-    "test_conversation_multi_turn_and_streaming[txt=openai/gpt-4o]"
-)
-
-
-@pytest.mark.integration
-def test_conversation_streaming_multi_client(openai_client, text_model_id):
-    """Run many copies of the conversation streaming flow concurrently (matching recorded prompts)."""
-
-    if CONCURRENCY_WORKERS < 2:
-        pytest.skip("Need at least 2 workers for concurrency stress")
-
-    def stream_single_turn(conversation_id: str, worker_idx: int, turn_idx: int):
-        """Send the streaming turn and ensure completion."""
-        response_stream = openai_client.responses.create(
-            model=text_model_id,
-            input=[{"role": "user", "content": STREAMING_PROMPT}],
-            conversation=conversation_id,
-            stream=True,
-        )
-
-        final_response = None
-        failed_error: str | None = None
-        for chunk in response_stream:
-            chunk_type = getattr(chunk, "type", None)
-            if chunk_type is None and isinstance(chunk, dict):
-                chunk_type = chunk.get("type")
-
-            chunk_response = getattr(chunk, "response", None)
-            if chunk_response is None and isinstance(chunk, dict):
-                chunk_response = chunk.get("response")
-
-            if chunk_type in {"response.completed", "response.incomplete"}:
-                final_response = chunk_response
-                break
-            if chunk_type == "response.failed":
-                error_obj = None
-                if chunk_response is not None:
-                    error_obj = getattr(chunk_response, "error", None)
-                    if error_obj is None and isinstance(chunk_response, dict):
-                        error_obj = chunk_response.get("error")
-                if isinstance(error_obj, dict):
-                    failed_error = error_obj.get("message", "response.failed")
-                elif hasattr(error_obj, "message"):
-                    failed_error = error_obj.message  # type: ignore[assignment]
-                else:
-                    failed_error = "response.failed event without error"
-                break
-
-        if failed_error:
-            raise RuntimeError(f"Worker {worker_idx} turn {turn_idx} failed: {failed_error}")
-        if final_response is None:
-            raise RuntimeError(f"Worker {worker_idx} turn {turn_idx} did not complete")
-
-    def worker(worker_idx: int) -> list[str]:
-        token = set_test_context(RECORDED_TEST_NODE_ID)
-        conversation_ids: list[str] = []
-        try:
-            for turn_idx in range(TURNS_PER_WORKER):
-                conversation = openai_client.conversations.create()
-                assert conversation.id.startswith("conv_")
-                conversation_ids.append(conversation.id)
-
-                openai_client.responses.create(
-                    model=text_model_id,
-                    input=[{"role": "user", "content": FIRST_TURN_PROMPT}],
-                    conversation=conversation.id,
-                )
-
-                stream_single_turn(conversation.id, worker_idx, turn_idx)
-
-            return conversation_ids
-        finally:
-            reset_test_context(token)
-
-    all_conversation_ids: list[str] = []
-    futures = []
-    with ThreadPoolExecutor(max_workers=CONCURRENCY_WORKERS) as executor:
-        for worker_idx in range(CONCURRENCY_WORKERS):
-            futures.append(executor.submit(worker, worker_idx))
-
-        for future in as_completed(futures):
-            all_conversation_ids.extend(future.result())
-
-    expected_conversations = CONCURRENCY_WORKERS * TURNS_PER_WORKER
-    assert len(all_conversation_ids) == expected_conversations
-
-    for conversation_id in all_conversation_ids:
-        conversation_items = openai_client.conversations.items.list(conversation_id)
-        assert len(conversation_items.data) >= 4, (
-            f"Conversation {conversation_id} missing expected items: {len(conversation_items.data)}"
-        )