From a3661b9cc03be1c44bf82f6d91b3394dbf5163c0 Mon Sep 17 00:00:00 2001 From: Faiz Surani Date: Thu, 5 Sep 2024 12:40:09 -0700 Subject: [PATCH] Add logprobs to gpt 3.5 --- .gitignore | 1 - README.md | 2 +- pyproject.toml | 6 +- rl/llm/engines/client.py | 51 +- rl/llm/engines/core.py | 5 +- uv.lock | 4541 ++++++++++++++++++++++++++++++++++++++ 6 files changed, 4586 insertions(+), 20 deletions(-) create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore index a1d802d..b533190 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,3 @@ build/ dist/ *.spec -uv.lock diff --git a/README.md b/README.md index 536e5ff..ce08984 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ curl -LsSf https://astral.sh/uv/install.sh | sh Then, on both your local machine and Sherlock (or either one, if you just want some features): ``` -uv tool install "rl[sherlock] @ git+https://github.com/ProbablyFaiz/rl.git@v0.8.1" +uv tool install "rl[sherlock] @ git+https://github.com/ProbablyFaiz/rl.git@v0.9.0" ``` ## Setup diff --git a/pyproject.toml b/pyproject.toml index f79b33a..bd2e820 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ authors = [ { name = "Faiz Surani", email = "faiz@law.stanford.edu" }, { name = "Varun Magesh", email="vim@law.stanford.edu" } ] -description = "A CLI for various RegLab / Sherlock tasks" +description = "Utilities for various RegLab / Sherlock tasks" readme = "README.md" classifiers = [ "Development Status :: 3 - Alpha", @@ -25,8 +25,8 @@ dependencies = [ "python-dotenv", "unidecode", "StrEnum", - "watchdog", # TODO: Maybe move into an extra? + "watchdog", "pydantic>=2", "typing-extensions", "legal-segmenter @ git+https://github.com/lexeme-dev/legal-segmenter@main", @@ -42,9 +42,9 @@ sherlock = [ "paramiko", ] local_llm = [ + "rl[llm]", "transformers>=4.43", "torch", - "rl[llm]", "vllm~=0.5.3post1; platform_system == 'Linux'", "peft", "trl", diff --git a/rl/llm/engines/client.py b/rl/llm/engines/client.py index 97ca806..e2713b2 100644 --- a/rl/llm/engines/client.py +++ b/rl/llm/engines/client.py @@ -44,6 +44,9 @@ def batch_generate(self, prompts: list[ChatInput]) -> list[InferenceOutput]: ) +_NUM_LOGPROBS = 20 + + class _OAIClientEngine(ClientEngine, ABC): BASE_URL: str API_KEY_NAME: str @@ -82,16 +85,26 @@ def generate(self, prompt: ChatInput) -> InferenceOutput: completion_kwargs["max_tokens"] = self.llm_config.max_new_tokens if EngineFeature.JSON_OUTPUT in self.enabled_features: completion_kwargs["response_format"] = {"type": "json_object"} + if EngineFeature.RETURN_LOGPROBS in self.enabled_features: + completion_kwargs["logprobs"] = True + completion_kwargs["top_logprobs"] = _NUM_LOGPROBS response = self.client.chat.completions.create(**completion_kwargs) - return InferenceOutput( + choice = response.choices[0] + + output = InferenceOutput( prompt=prompt, # type: ignore - text=response.choices[0].message.content, + text=choice.message.content, metadata={ "model": self.llm_config.model_name_or_path, "base_url": self.BASE_URL, }, ) + if EngineFeature.RETURN_LOGPROBS in self.enabled_features: + output.logprobs = [ + {tkn.token: tkn.logprob for tkn in pos.top_logprobs} + for pos in choice.logprobs.content + ] @register_engine( @@ -107,7 +120,7 @@ class TogetherEngine(_OAIClientEngine): @register_engine( "openai", required_modules=("openai",), - supported_features=(EngineFeature.JSON_OUTPUT,), + supported_features=(EngineFeature.JSON_OUTPUT, EngineFeature.RETURN_LOGPROBS), ) class OpenAIEngine(_OAIClientEngine): BASE_URL = "https://api.openai.com/v1" @@ -322,7 +335,7 @@ def _get_modal_app_name(self, model_name: str) -> str: @register_engine( "batch_openai", required_modules=("openai",), - supported_features=(EngineFeature.JSON_OUTPUT,), + supported_features=(EngineFeature.JSON_OUTPUT, EngineFeature.RETURN_LOGPROBS), ) class BatchOpenAIEngine(InferenceEngine): def __init__(self, llm_config: LLMConfig): @@ -343,6 +356,8 @@ def generate(self, prompt: ChatInput) -> InferenceOutput: return self.batch_generate([prompt])[0] def batch_generate(self, prompts: list[ChatInput]) -> list[InferenceOutput]: + from openai.types.chat import ChatCompletion + with tempfile.NamedTemporaryFile( mode="w+", suffix=".jsonl", delete=False ) as temp_file: @@ -357,6 +372,9 @@ def batch_generate(self, prompts: list[ChatInput]) -> list[InferenceOutput]: body_kwargs["temperature"] = self.llm_config.temperature if EngineFeature.JSON_OUTPUT in self.enabled_features: body_kwargs["response_format"] = {"type": "json_object"} + if EngineFeature.RETURN_LOGPROBS in self.enabled_features: + body_kwargs["logprobs"] = True + body_kwargs["top_logprobs"] = _NUM_LOGPROBS request = { "custom_id": f"request-{i}", "method": "POST", @@ -396,17 +414,22 @@ def batch_generate(self, prompts: list[ChatInput]) -> list[InferenceOutput]: outputs = [] for result in results: - response = result["response"]["body"] - outputs.append( - InferenceOutput( - prompt=prompts[int(result["custom_id"].split("-")[1])], - text=response["choices"][0]["message"]["content"], - metadata={ - "model": self.llm_config.model_name_or_path, - "base_url": "https://api.openai.com/v1", - }, - ) + parsed_result = ChatCompletion.model_validate(result["response"]["body"]) + choice = parsed_result.choices[0] + output = InferenceOutput( + prompt=prompts[int(result["custom_id"].split("-")[1])], + text=choice.message.content, + metadata={ + "model": self.llm_config.model_name_or_path, + "base_url": "https://api.openai.com/v1", + }, ) + if EngineFeature.RETURN_LOGPROBS in self.enabled_features: + output.logprobs = [ + {tkn.token: tkn.logprob for tkn in pos.top_logprobs} + for pos in choice.logprobs.content + ] + outputs.append(output) Path(temp_file_path).unlink() diff --git a/rl/llm/engines/core.py b/rl/llm/engines/core.py index 065099a..2e8e3e5 100644 --- a/rl/llm/engines/core.py +++ b/rl/llm/engines/core.py @@ -29,7 +29,10 @@ class InferenceOutput(BaseModel): prompt: InferenceInput text: str - logprobs: list[dict[int, float]] | None = None + # TODO: OpenAI gives us logprobs in terms of the string representation of the token + # but vLLM gives us logprobs in terms of the token itself. We should probably + # standardize on one representation. + logprobs: list[dict[int | str, float]] | None = None metadata: dict[str, Any] = Field(default_factory=dict) diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..b2a57d1 --- /dev/null +++ b/uv.lock @@ -0,0 +1,4541 @@ +version = 1 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version < '3.11' and sys_platform == 'linux'", + "python_full_version < '3.11' and sys_platform != 'linux'", + "python_full_version < '3.11' and sys_platform == 'linux'", + "python_full_version == '3.11.*' and sys_platform == 'linux'", + "python_full_version < '3.11' and sys_platform != 'linux'", + "python_full_version == '3.11.*' and sys_platform != 'linux'", + "python_full_version == '3.12.*' and sys_platform == 'linux'", + "python_full_version == '3.12.*' and sys_platform != 'linux'", + "python_full_version >= '3.13' and sys_platform == 'linux'", + "python_full_version >= '3.13' and sys_platform != 'linux'", +] + 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