best effort tito

tests/test_openai_chat_completions_token_client.py

@@ -0,0 +1,270 @@
+from typing import Any, cast
+
+import pytest
+
+from verifiers.clients.openai_chat_completions_client import OpenAIChatCompletionsClient
+from verifiers.clients.openai_chat_completions_token_client import (
+    OpenAIChatCompletionsTokenClient,
+)
+from verifiers.types import State
+
+
+class _NoopClient:
+    base_url = "http://localhost:8000/v1"
+
+    def with_options(self, **kwargs):  # noqa: ANN003
+        return self
+
+
+class _RecordingClient(_NoopClient):
+    def __init__(self) -> None:
+        self.calls: list[dict[str, Any]] = []
+
+    async def post(self, path: str, body: dict[str, Any], cast_to: type) -> Any:
+        self.calls.append({"path": path, "body": body, "cast_to": cast_to})
+        return {"ok": True, "path": path, "body": body}
+
+
+class _PromptIdTestClient(OpenAIChatCompletionsTokenClient):
+    def __init__(self, full_prompt_ids: list[int]) -> None:
+        super().__init__(_NoopClient())
+        self._full_prompt_ids = full_prompt_ids
+
+    async def to_native_prompt(self, messages):  # type: ignore[override]
+        return cast(Any, messages), {}
+
+    async def tokenize(  # type: ignore[override]
+        self,
+        messages,
+        tools,
+        model,
+        extra_kwargs: dict = {},
+        **kwargs,
+    ) -> list[int]:
+        if isinstance(messages, str):
+            assert messages == "World!"
+            return [777]
+
+        if messages == [
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "World!"},
+        ]:
+            assert extra_kwargs == {"add_generation_prompt": False}
+            return [1, 777, 999]
+
+        return self._full_prompt_ids
+
+
+class _NoTokenizeClient(OpenAIChatCompletionsTokenClient):
+    def __init__(self) -> None:
+        super().__init__(_NoopClient())
+
+    async def to_native_prompt(self, messages):  # type: ignore[override]
+        return cast(Any, messages), {}
+
+    async def tokenize(  # type: ignore[override]
+        self,
+        messages,
+        tools,
+        model,
+        extra_kwargs: dict = {},
+        **kwargs,
+    ) -> list[int]:
+        raise AssertionError("tokenize should not be called without a prefix match")
+
+
+def _make_step(
+    prompt: list[dict[str, str]],
+    completion: list[dict[str, str]],
+    prompt_ids: list[int],
+    completion_ids: list[int],
+) -> dict[str, Any]:
+    return {
+        "prompt": prompt,
+        "completion": completion,
+        "tokens": {
+            "prompt_ids": prompt_ids,
+            "completion_ids": completion_ids,
+        },
+    }
+
+
+@pytest.mark.asyncio
+async def test_get_prompt_ids_uses_largest_message_prefix_match():
+    client = _PromptIdTestClient(full_prompt_ids=[1, 2, 3, 4, 999, 5])
+    state = cast(
+        State,
+        {
+            "model": "test-model",
+            "trajectory": [
+                _make_step(
+                    prompt=[{"role": "user", "content": "u1"}],
+                    completion=[{"role": "assistant", "content": "a1"}],
+                    prompt_ids=[1],
+                    completion_ids=[2],
+                ),
+                _make_step(
+                    prompt=[
+                        {"role": "user", "content": "u1"},
+                        {"role": "assistant", "content": "a1"},
+                        {"role": "user", "content": "u2"},
+                    ],
+                    completion=[{"role": "assistant", "content": "a2"}],
+                    prompt_ids=[1, 2, 3],
+                    completion_ids=[4],
+                ),
+            ],
+        },
+    )
+    prompt_messages = cast(
+        Any,
+        [
+            {"role": "user", "content": "u1"},
+            {"role": "assistant", "content": "a1"},
+            {"role": "user", "content": "u2"},
+            {"role": "assistant", "content": "a2"},
+            {"role": "user", "content": "u3"},
+        ],
+    )
+
+    prompt_ids = await client.get_prompt_ids(state, prompt_messages, oai_tools=None)
+
+    assert prompt_ids == [1, 2, 3, 4, 999, 5]
+
+
+@pytest.mark.asyncio
+async def test_get_prompt_ids_returns_none_when_no_prefix_match():
+    client = _NoTokenizeClient()
+    state = cast(
+        State,
+        {
+            "model": "test-model",
+            "trajectory": [
+                _make_step(
+                    prompt=[{"role": "user", "content": "old"}],
+                    completion=[{"role": "assistant", "content": "reply"}],
+                    prompt_ids=[1],
+                    completion_ids=[2],
+                )
+            ],
+        },
+    )
+
+    prompt_ids = await client.get_prompt_ids(
+        state,
+        cast(Any, [{"role": "user", "content": "new"}]),
+        oai_tools=None,
+    )
+
+    assert prompt_ids is None
+
+
+@pytest.mark.asyncio
+async def test_get_native_response_falls_back_to_super_when_no_prefix_match(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    client = OpenAIChatCompletionsTokenClient(_NoopClient())
+    sentinel = {"source": "super"}
+    calls: list[dict[str, Any]] = []
+
+    async def fake_get_prompt_ids(self, state, prompt_messages, oai_tools):  # noqa: ANN001
+        return None
+
+    async def fake_super_get_native_response(  # noqa: ANN001
+        self,
+        prompt,
+        model,
+        sampling_args,
+        tools=None,
+        **kwargs,
+    ):
+        calls.append(
+            {
+                "prompt": prompt,
+                "model": model,
+                "sampling_args": sampling_args,
+                "tools": tools,
+            }
+        )
+        return sentinel
+
+    monkeypatch.setattr(
+        OpenAIChatCompletionsTokenClient, "get_prompt_ids", fake_get_prompt_ids
+    )
+    monkeypatch.setattr(
+        OpenAIChatCompletionsClient,
+        "get_native_response",
+        fake_super_get_native_response,
+    )
+
+    state = cast(
+        State,
+        {
+            "model": "test-model",
+            "trajectory": [
+                _make_step(
+                    prompt=[{"role": "user", "content": "u1"}],
+                    completion=[{"role": "assistant", "content": "a1"}],
+                    prompt_ids=[1],
+                    completion_ids=[2],
+                )
+            ],
+        },
+    )
+    prompt = cast(Any, [{"role": "user", "content": "u2"}])
+
+    response = await client.get_native_response(
+        prompt=prompt,
+        model="test-model",
+        sampling_args={},
+        tools=None,
+        state=state,
+    )
+
+    assert response is sentinel
+    assert len(calls) == 1
+    assert calls[0]["prompt"] == prompt
+
+
+@pytest.mark.asyncio
+async def test_get_native_response_uses_token_route_when_prompt_ids_available(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    recording_client = _RecordingClient()
+    client = OpenAIChatCompletionsTokenClient(recording_client)
+
+    async def fake_get_prompt_ids(self, state, prompt_messages, oai_tools):  # noqa: ANN001
+        return [10, 20]
+
+    monkeypatch.setattr(
+        OpenAIChatCompletionsTokenClient, "get_prompt_ids", fake_get_prompt_ids
+    )
+
+    state = cast(
+        State,
+        {
+            "model": "test-model",
+            "trajectory": [
+                _make_step(
+                    prompt=[{"role": "user", "content": "u1"}],
+                    completion=[{"role": "assistant", "content": "a1"}],
+                    prompt_ids=[1],
+                    completion_ids=[2],
+                )
+            ],
+        },
+    )
+    prompt = cast(Any, [{"role": "user", "content": "u2"}])
+
+    response = await client.get_native_response(
+        prompt=prompt,
+        model="test-model",
+        sampling_args={},
+        tools=None,
+        state=state,
+    )
+
+    assert response["ok"] is True
+    assert len(recording_client.calls) == 1
+    assert recording_client.calls[0]["path"] == "/chat/completions/tokens"
+    assert recording_client.calls[0]["body"]["tokens"] == [10, 20]

verifiers/clients/openai_chat_completions_token_client.py

@@ -1,5 +1,5 @@
 from collections.abc import Mapping
-from typing import Optional, cast
+from typing import Any, Optional, cast
 
 from openai import AsyncOpenAI, BaseModel
 from openai.types.chat import ChatCompletion
@@ -85,6 +85,10 @@ def normalize_sampling_args(sampling_args: SamplingArgs):
                 prompt, model, sampling_args, tools
             )
         prompt_ids = await self.get_prompt_ids(state, prompt, tools)
+        if prompt_ids is None:
+            return await super().get_native_response(
+                prompt, model, sampling_args, tools
+            )
         extra_body = sampling_args.pop("extra_body", {})
         body = dict(
             model=model,
@@ -106,18 +110,65 @@ async def get_prompt_ids(
         state: State,
         prompt_messages: OpenAIChatMessages,
         oai_tools: list[OpenAITool] | None,
-    ) -> list[int]:
+    ) -> list[int] | None:
         """
         Build prompt_ids (token prompt) corresponding to prompt_messages. We assume
         that this method is called *before* making the model response from
         prompt_messages, i.e. the previous turn's prompt and completion do not yet
         include the environment response and next turn's model response.
+
+        Returns None when no trajectory step has a message-level prefix match with
+        prompt_messages.
         """
-        prev_turn_tokens = state["trajectory"][-1]["tokens"]
-        assert prev_turn_tokens is not None
-        prev_turn_prompt_ids = prev_turn_tokens["prompt_ids"]
-        prev_turn_completion_ids = prev_turn_tokens["completion_ids"]
-        prev_turn_ids = prev_turn_prompt_ids + prev_turn_completion_ids
+
+        def normalize_for_comparison(value: Any) -> Any:
+            if hasattr(value, "model_dump"):
+                return normalize_for_comparison(value.model_dump())
+            if isinstance(value, Mapping):
+                return {
+                    str(key): normalize_for_comparison(val)
+                    for key, val in value.items()
+                }
+            if isinstance(value, list):
+                return [normalize_for_comparison(item) for item in value]
+            return value
+
+        async def find_largest_prefix_match_tokens() -> list[int] | None:
+            """Scan trajectory backwards for the step whose messages form the longest
+            prefix of prompt_messages. Returns that step's token IDs, or None."""
+            normalized_prompt_messages = normalize_for_comparison(prompt_messages)
+            best_prefix_len = -1
+            best_step_tokens = None
+            for step in reversed(state["trajectory"]):
+                step_tokens = step["tokens"]
+                if step_tokens is None:
+                    continue
+                step_messages = cast(Any, [*step["prompt"], *step["completion"]])
+                step_prompt_messages, _ = await self.to_native_prompt(step_messages)
+                normalized_step_messages = normalize_for_comparison(
+                    step_prompt_messages
+                )
+                prefix_len = len(normalized_step_messages)
+                if prefix_len <= 0:
+                    continue
+                if prefix_len <= best_prefix_len:
+                    continue
+                if prefix_len > len(normalized_prompt_messages):
+                    continue
+                if normalized_prompt_messages[:prefix_len] != normalized_step_messages:
+                    continue
+                best_prefix_len = prefix_len
+                best_step_tokens = step_tokens
+                if best_prefix_len == len(normalized_prompt_messages):
+                    break
+
+            if best_step_tokens is None:
+                return None
+            return best_step_tokens["prompt_ids"] + best_step_tokens["completion_ids"]
+
+        prev_turn_ids = await find_largest_prefix_match_tokens()
+        if prev_turn_ids is None:
+            return None
 
         def compute_suffix_ids(lst: list[int], value: int) -> list[int]:
             """Returns all tokens after the last occurrence of `value` in `lst`, if any."""