diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..b70babc --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,53 @@ +name: Continuous Integration + +on: + push: + branches: + - master + pull_request: + branches: + - master + +jobs: + build-and-test: + name: Build and Test + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-latest, windows-latest, macos-latest] + python-version: ['3.8', '3.9', '3.10', '3.11'] + + steps: + - name: Check out code + uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + + - name: Setup Python, install dependencies, and run tests + run: | + python -m pip install --upgrade pip + python -m llama_api.server.app_settings --install-pkgs + python -m unittest discover tests + + build-release: + if: github.event_name == 'push' && success() + needs: build-and-test + name: Create Release + runs-on: ubuntu-latest + steps: + - name: Shorten SHA + id: shorten_sha + run: echo "::set-output name=sha::$(echo ${GITHUB_SHA:0:7})" + - name: Create Release + id: create_release + uses: actions/create-release@v1 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + tag_name: ${{ steps.shorten_sha.outputs.sha }} + release_name: master-${{ steps.shorten_sha.outputs.sha }} + draft: false + prerelease: false \ No newline at end of file diff --git a/.gitignore b/.gitignore index c6c4b43..2b48ff4 100644 --- a/.gitignore +++ b/.gitignore @@ -2,9 +2,10 @@ models/ggml/* models/gptq/* !models/ggml/llama_cpp_models_here.txt !models/gptq/exllama_models_here.txt +repositories/ *.log -.venv -.vscode *.pyc -PRIVATE_* -repositories \ No newline at end of file +.venv/ +.vscode/ +.test-venv/ +PRIVATE_* \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index b7d020d..85ba958 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,7 +1,7 @@ ### Dockerfile for Python 3.11.4 & CUDA 12.1.1 & Ubuntu 22.04 -### Approximately 7 ~ 10 minutes to build +### Approximately 5 ~ 10 minutes to build -# 필요한 CUDA 버전을 선택합니다. +# Select the required CUDA version. ARG CUDA_IMAGE="12.1.1-devel-ubuntu22.04" FROM nvidia/cuda:${CUDA_IMAGE} ENV PYTHON_VERSION="3.11.4" @@ -9,10 +9,13 @@ ENV PYTHON_VERSION_SHORT="3.11" ENV HOST 0.0.0.0 ENV PORT=8000 -# 필요한 파일들을 복사합니다. -COPY requirements.txt /tmp/requirements.txt +# Copy the necessary files. +COPY requirements.txt /app/requirements.txt +COPY pyproject.toml /app/pyproject.toml +COPY llama_api /app/llama_api -# Python 설치를 위한 종속성들을 설치하고, Python을 설치하고 설정합니다. +# Install the necessary applications, and then install Python. +# Then, install the necessary Python packages(Dependencies). RUN apt-get update && apt-get install -y --no-install-recommends \ build-essential \ zlib1g-dev \ @@ -34,11 +37,12 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ && update-alternatives --install /usr/bin/python python /usr/local/bin/python${PYTHON_VERSION_SHORT} 1 \ && update-alternatives --install /usr/bin/python3 python3 /usr/local/bin/python${PYTHON_VERSION_SHORT} 1 \ && python3 -m pip install --upgrade pip \ - && pip install --no-cache-dir -r /tmp/requirements.txt \ && rm -rf /var/lib/apt/lists/* \ && apt-get clean \ - && rm -rf /tmp/* + && rm -rf /tmp/* \ + && cd /app \ + && python3 -m llama_api.server.app_settings --force-cuda --install-pkgs -# 작업 디렉토리를 설정하고, 서버를 실행합니다. +# Set the working directory and start the server. WORKDIR /app -ENTRYPOINT [ "python3", "-m", "main" "--port", "${PORT}" ] +ENTRYPOINT [ "python3", "-m", "main", "--port", "${PORT}" ] \ No newline at end of file diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..3a1d718 --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,9 @@ +MIT License + +Copyright (c) 2023 Andrei Betlen + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml index 03111e3..c87a432 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -2,9 +2,7 @@ version: '3' services: llama-api: - build: - context: . - dockerfile: Dockerfile + image: cosogi/llama-api:230730 entrypoint: ["python3", "-m", "main", "--port", "8000"] environment: - MAX_WORKERS=1 @@ -14,6 +12,7 @@ services: - ./model_definitions.py:/app/model_definitions.py - ./main.py:/app/main.py - ./requirements.txt:/app/requirements.txt + - ./pyproject.toml:/app/pyproject.toml ports: - 8000:8000 deploy: @@ -21,4 +20,27 @@ services: reservations: devices: - driver: nvidia - capabilities: [gpu] \ No newline at end of file + capabilities: [gpu] + +# services: + # llama-api: + # build: + # context: . + # dockerfile: Dockerfile + # entrypoint: ["python3", "-m", "main", "--port", "8000"] + # environment: + # - MAX_WORKERS=1 + # volumes: + # - ./models:/app/models + # - ./llama_api:/app/llama_api + # - ./model_definitions.py:/app/model_definitions.py + # - ./main.py:/app/main.py + # - ./requirements.txt:/app/requirements.txt + # ports: + # - 8000:8000 + # deploy: + # resources: + # reservations: + # devices: + # - driver: nvidia + # capabilities: [gpu] \ No newline at end of file diff --git a/llama_api/mixins/prompt_utils.py b/llama_api/mixins/prompt_utils.py index bec69e8..adc6194 100644 --- a/llama_api/mixins/prompt_utils.py +++ b/llama_api/mixins/prompt_utils.py @@ -1,3 +1,4 @@ +from typing import List from ..schemas.api import APIChatMessage, TextGenerationSettings @@ -9,7 +10,7 @@ class PromptUtilsMixin: ai_fallback_input_role: str = "Assistant" @staticmethod - def get_stop_strings(*roles: str) -> list[str]: + def get_stop_strings(*roles: str) -> List[str]: """A helper method to generate stop strings for a given set of roles. Stop strings are required to stop text completion API from generating text that does not belong to the current chat turn. @@ -33,7 +34,7 @@ def get_stop_strings(*roles: str) -> list[str]: @classmethod def convert_messages_into_prompt( - cls, messages: list[APIChatMessage], settings: TextGenerationSettings + cls, messages: List[APIChatMessage], settings: TextGenerationSettings ) -> str: """A helper method to convert list of messages into one text prompt.""" @@ -48,7 +49,7 @@ def convert_messages_into_prompt( input_role = ai_input_role = message.role chat_history += f"### {input_role}:{message.content}" - prompt_stop: list[str] = cls.get_stop_strings( + prompt_stop: List[str] = cls.get_stop_strings( cls.user_input_role, cls.system_input_role, ai_input_role ) if isinstance(settings.stop, str): @@ -61,7 +62,7 @@ def convert_messages_into_prompt( @staticmethod def is_possible_to_generate_stops( - decoded_text: str, stops: list[str] + decoded_text: str, stops: List[str] ) -> bool: """A helper method to check if the decoded text contains any of the stop tokens.""" diff --git a/llama_api/modules/base.py b/llama_api/modules/base.py index 0310160..6428137 100644 --- a/llama_api/modules/base.py +++ b/llama_api/modules/base.py @@ -1,9 +1,8 @@ from abc import ABC, abstractmethod from dataclasses import dataclass -from typing import Any, Iterator, TypeVar +from typing import Any, Iterator, List, TypeVar from ..mixins.prompt_utils import PromptUtilsMixin -from ..mixins.waiter import WaiterMixin from ..schemas.api import ( APIChatMessage, ChatCompletion, @@ -24,7 +23,7 @@ class BaseLLMModel: max_total_tokens: int = 2048 -class BaseCompletionGenerator(ABC, PromptUtilsMixin, WaiterMixin): +class BaseCompletionGenerator(ABC, PromptUtilsMixin): """Base class for all completion generators.""" @abstractmethod @@ -57,14 +56,14 @@ def generate_completion_with_streaming( @abstractmethod def generate_chat_completion( - self, messages: list[APIChatMessage], settings: TextGenerationSettings + self, messages: List[APIChatMessage], settings: TextGenerationSettings ) -> ChatCompletion: """Generate a completion for a given prompt.""" ... @abstractmethod def generate_chat_completion_with_streaming( - self, messages: list[APIChatMessage], settings: TextGenerationSettings + self, messages: List[APIChatMessage], settings: TextGenerationSettings ) -> Iterator[ChatCompletionChunk]: """Generate a completion for a given prompt, yielding chunks of text as they are generated.""" @@ -92,9 +91,9 @@ def from_pretrained(cls, model_name: str) -> "BaseEmbeddingGenerator": @abstractmethod def generate_embeddings( self, - texts: list[str], + texts: List[str], **kwargs: Any, - ) -> list[list[float]]: + ) -> List[List[float]]: """Generate embeddings for a list of texts.""" ... diff --git a/llama_api/modules/exllama.py b/llama_api/modules/exllama.py index 2c9c192..e3fc0b5 100644 --- a/llama_api/modules/exllama.py +++ b/llama_api/modules/exllama.py @@ -1,12 +1,9 @@ """Wrapper for exllama to generate text completions.""" - -# flake8: noqa - from contextlib import contextmanager from pathlib import Path -from typing import TYPE_CHECKING, Iterator, Optional +from typing import TYPE_CHECKING, Dict, Iterator, List, Optional -from torch import IntTensor, cuda +from torch import IntTensor, Tensor, cuda from ..schemas.models import ExllamaModel from ..utils.completions import ( @@ -15,8 +12,9 @@ make_completion, make_completion_chunk, ) +from ..utils.dependency import import_repository from ..utils.logger import ApiLogger -from ..utils.path import import_repository, resolve_model_path_to_posix +from ..utils.path import resolve_model_path_to_posix from .base import BaseCompletionGenerator with import_repository( @@ -41,6 +39,15 @@ assert cuda.is_available(), "CUDA must be available to use ExLlama." +def _encode(tokenizer: ExLlamaTokenizer, text: str) -> Tensor: + """Encode a text string into a tensor.""" + result = tokenizer.encode(text) + if isinstance(result, tuple): + return result[0] + else: + return result + + def _make_config(llm_model: "ExllamaModel") -> ExLlamaConfig: """Create a config object for the ExLlama model.""" model_folder_path = Path( @@ -52,7 +59,7 @@ def _make_config(llm_model: "ExllamaModel") -> ExLlamaConfig: config = ExLlamaConfig((model_folder_path / "config.json").as_posix()) # Find the model checkpoint - model_file_found: list[Path] = [] + model_file_found: List[Path] = [] for ext in (".safetensors", ".pt", ".bin"): model_file_found.extend(model_folder_path.glob(f"*{ext}")) if model_file_found: @@ -107,7 +114,7 @@ class ExllamaCompletionGenerator(BaseCompletionGenerator): tokenizer: Optional[ExLlamaTokenizer] = None generator: Optional[ExLlamaGenerator] = None _llm_model: Optional["ExllamaModel"] = None - _completion_status: dict[ + _completion_status: Dict[ str, int ] = {} # key: completion_id, value: number of completion tokens @@ -272,7 +279,7 @@ def generate_completion_with_streaming( text=last_token if last_token is not None else "", finish_reason="length" if self._completion_status.get( - completion_id, self.tokenizer.encode(generated_text).shape[1] + completion_id, _encode(self.tokenizer, generated_text).shape[1] ) >= settings.max_tokens else "stop", @@ -284,9 +291,9 @@ def generate_completion( assert self.tokenizer is not None and self.config is not None completion_id: str = settings.completion_id generated_text: str = self._generate_text(prompt, settings=settings) - n_prompt_tokens: int = self.tokenizer.encode(prompt).shape[1] + n_prompt_tokens: int = _encode(self.tokenizer, prompt).shape[1] n_completion_tokens: int = self._completion_status.get( - completion_id, self.tokenizer.encode(generated_text).shape[1] + completion_id, _encode(self.tokenizer, generated_text).shape[1] ) return make_completion( id=completion_id, @@ -301,7 +308,7 @@ def generate_completion( def generate_chat_completion_with_streaming( self, - messages: list["APIChatMessage"], + messages: List["APIChatMessage"], settings: "TextGenerationSettings", ) -> Iterator["ChatCompletionChunk"]: assert self.config is not None and self.tokenizer is not None @@ -328,23 +335,23 @@ def generate_chat_completion_with_streaming( content=last_token if last_token is not None else "", finish_reason="length" if self._completion_status.get( - completion_id, self.tokenizer.encode(generated_text).shape[1] + completion_id, _encode(self.tokenizer, generated_text).shape[1] ) else "stop", ) def generate_chat_completion( self, - messages: list["APIChatMessage"], + messages: List["APIChatMessage"], settings: "TextGenerationSettings", ) -> "ChatCompletion": assert self.tokenizer is not None and self.config is not None completion_id: str = settings.completion_id prompt = self.convert_messages_into_prompt(messages, settings=settings) generated_text: str = self._generate_text(prompt, settings=settings) - prompt_tokens: int = self.tokenizer.encode(prompt).shape[1] + prompt_tokens: int = _encode(self.tokenizer, prompt).shape[1] completion_tokens: int = self._completion_status.get( - completion_id, self.tokenizer.encode(generated_text).shape[1] + completion_id, _encode(self.tokenizer, generated_text).shape[1] ) return make_chat_completion( id=completion_id, @@ -357,10 +364,10 @@ def generate_chat_completion( else "stop", ) - def encode(self, message: str, /) -> list[int]: + def encode(self, message: str, /) -> List[int]: assert self.tokenizer is not None, "Tokenizer is not initialized" - return self.tokenizer.encode(message).flatten().tolist() + return _encode(self.tokenizer, message).flatten().tolist() - def decode(self, tokens: list[int], /) -> str: + def decode(self, tokens: List[int], /) -> str: assert self.tokenizer is not None, "Tokenizer is not initialized" return str(self.tokenizer.decode(IntTensor(tokens))) diff --git a/llama_api/modules/llama_cpp.py b/llama_api/modules/llama_cpp.py index d786160..afd1adb 100644 --- a/llama_api/modules/llama_cpp.py +++ b/llama_api/modules/llama_cpp.py @@ -1,6 +1,6 @@ """Wrapper for llama_cpp to generate text completions.""" from inspect import signature -from typing import Iterator, Literal, Optional +from typing import Dict, Iterator, List, Literal, Optional, Union from ..schemas.api import ( APIChatMessage, @@ -15,41 +15,30 @@ convert_text_completion_chunks_to_chat, convert_text_completion_to_chat, ) -from ..utils.llama_cpp import build_shared_lib +from ..utils.dependency import import_repository from ..utils.logger import ApiLogger -from ..utils.path import import_repository, resolve_model_path_to_posix +from ..utils.path import resolve_model_path_to_posix from .base import BaseCompletionGenerator logger = ApiLogger(__name__) logger.info("🦙 llama-cpp-python repository found!") -build_shared_lib(logger=logger) -try: - with import_repository( - git_path="https://github.com/abetlen/llama-cpp-python", - disk_path="repositories/llama_cpp", - ): - from repositories.llama_cpp import llama_cpp - from repositories.llama_cpp.llama_cpp.llama_cpp import GGML_USE_CUBLAS -except ImportError: - logger.warning( - "🦙 llama-cpp-python repository not found. " - "Falling back to llama-cpp-python submodule." - ) - - import llama_cpp - from llama_cpp import GGML_USE_CUBLAS as GGML_USE_CUBLAS +with import_repository( + git_path="https://github.com/abetlen/llama-cpp-python", + disk_path="repositories/llama_cpp", +): + from repositories.llama_cpp import llama_cpp def _make_logit_bias_processor( llama: llama_cpp.Llama, - logit_bias: dict[str, float], + logit_bias: Dict[str, float], logit_bias_type: Optional[Literal["input_ids", "tokens"]], ): """Create a logit bias processor to bias the logit scores.""" if logit_bias_type is None: logit_bias_type = "input_ids" - to_bias: dict[int, float] = {} + to_bias: Dict[int, float] = {} if logit_bias_type == "input_ids": for input_id_string, score in logit_bias.items(): to_bias[int(input_id_string)] = score @@ -62,10 +51,10 @@ def _make_logit_bias_processor( to_bias[input_id] = score def logit_bias_processor( - input_ids: list[int], - scores: list[float], - ) -> list[float]: - new_scores: list[float] = [0.0] * len(scores) + input_ids: List[int], + scores: List[float], + ) -> List[float]: + new_scores: List[float] = [0.0] * len(scores) for input_id, score in enumerate(scores): new_scores[input_id] = score + to_bias.get(input_id, 0.0) @@ -79,7 +68,7 @@ def _create_completion( prompt: str, stream: bool, settings: TextGenerationSettings, -) -> Completion | Iterator[CompletionChunk]: +) -> Union[Completion, Iterator[CompletionChunk]]: return client.create_completion( # type: ignore stream=stream, prompt=prompt, @@ -96,7 +85,7 @@ def _create_completion( mirostat_mode=settings.mirostat_mode, mirostat_tau=settings.mirostat_tau, mirostat_eta=settings.mirostat_eta, - logits_processor=llama_cpp.LogitsProcessorList( # type: ignore + logits_processor=llama_cpp.LogitsProcessorList( [ _make_logit_bias_processor( client, @@ -113,10 +102,10 @@ def _create_completion( def _create_chat_completion( client: llama_cpp.Llama, - messages: list[APIChatMessage], + messages: List[APIChatMessage], stream: bool, settings: TextGenerationSettings, -) -> ChatCompletion | Iterator[ChatCompletionChunk]: +) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: prompt: str = LlamaCppCompletionGenerator.convert_messages_into_prompt( messages, settings=settings ) @@ -134,7 +123,7 @@ def _create_chat_completion( mirostat_mode=settings.mirostat_mode, mirostat_tau=settings.mirostat_tau, mirostat_eta=settings.mirostat_eta, - logits_processor=llama_cpp.LogitsProcessorList( # type: ignore + logits_processor=llama_cpp.LogitsProcessorList( [ _make_logit_bias_processor( client, @@ -158,7 +147,9 @@ def _create_chat_completion( class LlamaCppCompletionGenerator(BaseCompletionGenerator): - generator: Optional[Iterator[CompletionChunk | ChatCompletionChunk]] = None + generator: Optional[ + Iterator[Union[CompletionChunk, ChatCompletionChunk]] + ] = None client: Optional[llama_cpp.Llama] = None _llm_model: Optional["LlamaCppModel"] = None @@ -172,11 +163,6 @@ def __del__(self) -> None: del self.client self.client = None logger.info("🗑️ LlamaCppCompletionGenerator deleted!") - if GGML_USE_CUBLAS: - logger.warning( - "🗑️ Since you are using cuBLAS, unloading llama.cpp model" - "will cause VRAM leak." - ) @property def llm_model(self) -> "LlamaCppModel": @@ -274,7 +260,7 @@ def generate_completion_with_streaming( yield from completion_chunk_generator def generate_chat_completion( - self, messages: list[APIChatMessage], settings: TextGenerationSettings + self, messages: List[APIChatMessage], settings: TextGenerationSettings ) -> ChatCompletion: assert self.client is not None chat_completion = _create_chat_completion( @@ -287,7 +273,7 @@ def generate_chat_completion( return chat_completion def generate_chat_completion_with_streaming( - self, messages: list[APIChatMessage], settings: TextGenerationSettings + self, messages: List[APIChatMessage], settings: TextGenerationSettings ) -> Iterator[ChatCompletionChunk]: assert self.client is not None chat_completion_chunk_generator = _create_chat_completion( @@ -300,12 +286,12 @@ def generate_chat_completion_with_streaming( self.generator = chat_completion_chunk_generator yield from chat_completion_chunk_generator - def encode(self, text: str, add_bos: bool = True) -> list[int]: + def encode(self, text: str, add_bos: bool = True) -> List[int]: assert self.client is not None, "Client is not initialized" return self.client.tokenize( text.encode("utf-8", errors="ignore"), add_bos=add_bos ) - def decode(self, tokens: list[int]) -> str: + def decode(self, tokens: List[int]) -> str: assert self.client is not None, "Client is not initialized" return self.client.detokenize(tokens).decode("utf-8", errors="ignore") diff --git a/llama_api/modules/sentence_encoder.py b/llama_api/modules/sentence_encoder.py index ac17431..e9f8694 100644 --- a/llama_api/modules/sentence_encoder.py +++ b/llama_api/modules/sentence_encoder.py @@ -1,4 +1,5 @@ -from typing import TYPE_CHECKING, Callable, Optional +"""Wrapper for sentence_encoder to generate text embeddings.""" +from typing import TYPE_CHECKING, Callable, List, Optional import numpy as np import tensorflow_hub as hub @@ -17,7 +18,7 @@ class SentenceEncoderEmbeddingGenerator(BaseEmbeddingGenerator): automatically downloading the model from https://tfhub.dev/""" base_url: str = "https://tfhub.dev/google/" - model: Optional[Callable[[list[str]], "Tensor"]] = None + model: Optional[Callable[[List[str]], "Tensor"]] = None _model_name: Optional[str] = None def __del__(self) -> None: @@ -40,12 +41,12 @@ def from_pretrained( def generate_embeddings( self, - texts: list[str], + texts: List[str], batch_size: int = 100, **kwargs, - ) -> list[list[float]]: + ) -> List[List[float]]: assert self.model is not None, "Please load the model first." - embeddings: list["Tensor"] = [] + embeddings: List["Tensor"] = [] for batch_idx_start in range(0, len(texts), batch_size): batch_idx_end = batch_idx_start + batch_size batch_texts = texts[batch_idx_start:batch_idx_end] diff --git a/llama_api/modules/transformer.py b/llama_api/modules/transformer.py index 8b67cc9..c11fea0 100644 --- a/llama_api/modules/transformer.py +++ b/llama_api/modules/transformer.py @@ -1,6 +1,6 @@ +"""Wrapper for transformer to generate text embeddings.""" from gc import collect -from typing import Optional - +from typing import List, Optional, Tuple, Union from torch import Tensor, cuda from transformers.modeling_outputs import ( BaseModelOutputWithPoolingAndCrossAttentions, @@ -25,7 +25,9 @@ class TransformerEmbeddingGenerator(BaseEmbeddingGenerator): automatically downloading the model from https://huggingface.co/""" model: Optional[PreTrainedModel] = None - tokenizer: Optional[PreTrainedTokenizer | PreTrainedTokenizerFast] = None + tokenizer: Optional[ + Union[PreTrainedTokenizer, PreTrainedTokenizerFast] + ] = None encoder: Optional[PreTrainedModel] = None _model_name: Optional[str] = None @@ -58,12 +60,12 @@ def from_pretrained( def generate_embeddings( self, - texts: list[str], + texts: List[str], context_length: int = 512, batch_size: int = 3, **kwargs, - ) -> list[list[float]]: - embeddings: list[list[float]] = [] + ) -> List[List[float]]: + embeddings: List[List[float]] = [] for batch_idx_start in range(0, len(texts), batch_size): batch_idx_end = batch_idx_start + batch_size batch_texts = texts[batch_idx_start:batch_idx_end] @@ -75,9 +77,9 @@ def generate_embeddings( def _generate_embeddings_and_n_tokens( self, - texts: list[str], + texts: List[str], context_length: int = 512, - ) -> tuple[list[list[float]], int]: + ) -> Tuple[List[List[float]], int]: assert self.model is not None and self.tokenizer is not None def average_pool( diff --git a/llama_api/schemas/api.py b/llama_api/schemas/api.py index f6f2514..45a08e3 100644 --- a/llama_api/schemas/api.py +++ b/llama_api/schemas/api.py @@ -101,7 +101,7 @@ class TextGenerationSettings(BaseModel): default=False, description="If True, the input is echoed back in the output.", ) - stop: Optional[str | list[str]] = Field( + stop: Optional[Union[str, List[str]]] = Field( default=None, description="A list of tokens at which to stop generation. If None, no stop tokens are used.", ) @@ -171,7 +171,7 @@ class TextGenerationSettings(BaseModel): mirostat_eta: float = Field( default=0.1, ge=0.001, le=1.0, description="Mirostat learning rate" ) - logit_bias: Optional[dict[str, float]] = Field( + logit_bias: Optional[Dict[str, float]] = Field( default=None, description=( "A dictionary of logit bias values to use for each token. The keys are the tokens" @@ -199,7 +199,7 @@ class TextGenerationSettings(BaseModel): class CreateEmbeddingRequest(BaseModel): model: str = Field(description="The model to use for embedding.") - input: Union[str, list[str]] = Field(description="The input to embed.") + input: Union[str, List[str]] = Field(description="The input to embed.") user: Optional[str] = Field(default=None, description="Not in use.") class Config: @@ -236,7 +236,7 @@ class CreateChatCompletionRequest(TextGenerationSettings): model: str = Field( default=..., description="The model to use for completion." ) - messages: list[APIChatMessage] = Field( + messages: List[APIChatMessage] = Field( default=[], description="A list of messages to generate completions for.", ) diff --git a/llama_api/schemas/function_calling.py b/llama_api/schemas/function_calling.py index d4ffee3..c45bcf2 100644 --- a/llama_api/schemas/function_calling.py +++ b/llama_api/schemas/function_calling.py @@ -1,8 +1,17 @@ """Helper classes for wrapping functions in OpenAI's API""" from dataclasses import dataclass -from types import NoneType -from typing import Any, Generic, Literal, Optional, Type, TypeVar, Union +from typing import ( + Any, + Dict, + Generic, + List, + Literal, + Optional, + Type, + TypeVar, + Union, +) from typing_extensions import NotRequired, TypedDict @@ -16,13 +25,13 @@ class ParameterProperty(TypedDict): type: str description: NotRequired[str] - enum: NotRequired[list[JsonTypes]] + enum: NotRequired[List[JsonTypes]] class ParameterDefinition(TypedDict): type: Literal["object"] - properties: dict[str, ParameterProperty] - required: NotRequired[list[str]] + properties: Dict[str, ParameterProperty] + required: NotRequired[List[str]] class FunctionProperty(TypedDict): @@ -38,9 +47,9 @@ class FunctionCallParameter(Generic[ParamType]): name: str type: Type[ParamType] description: Optional[str] = None - enum: Optional[list[ParamType]] = None + enum: Optional[List[ParamType]] = None - def to_dict(self) -> dict[str, ParameterProperty]: + def to_dict(self) -> Dict[str, ParameterProperty]: """Returns a dictionary representation of the parameter""" parameter_property: ParameterProperty = { "type": self._get_json_type(self.type) @@ -66,7 +75,7 @@ def _get_json_type(python_type: Type[JsonTypes]) -> str: return "object" elif python_type is list: return "array" - elif python_type is NoneType or python_type is None: + elif python_type is type(None) or python_type is None: return "null" else: raise ValueError( @@ -81,8 +90,8 @@ class FunctionCall: name: str description: Optional[str] = None - parameters: Optional[list[FunctionCallParameter[Any]]] = None - required: Optional[list[str]] = None + parameters: Optional[List[FunctionCallParameter[Any]]] = None + required: Optional[List[str]] = None def to_dict(self) -> FunctionProperty: """Returns a dictionary representation of the function""" diff --git a/llama_api/schemas/models.py b/llama_api/schemas/models.py index c44b6b8..cb326b8 100644 --- a/llama_api/schemas/models.py +++ b/llama_api/schemas/models.py @@ -1,5 +1,5 @@ from dataclasses import dataclass, field -from typing import Literal, Optional +from typing import List, Literal, Optional from ..modules.base import BaseLLMModel @@ -118,7 +118,7 @@ class ExllamaModel(BaseLLMModel): "RAM as a buffer for GPU to GPU transfers." }, ) - auto_map: Optional[list[float]] = field( + auto_map: Optional[List[float]] = field( default=None, metadata={ "description": "List of floats with memory allocation in GB, " diff --git a/llama_api/server/app_settings.py b/llama_api/server/app_settings.py index b80bb9c..deb39ca 100644 --- a/llama_api/server/app_settings.py +++ b/llama_api/server/app_settings.py @@ -1,25 +1,24 @@ +import argparse import platform from contextlib import asynccontextmanager from os import environ -import sys -from typing import Optional -import subprocess - - -def ensure_packages_installed(): - subprocess.run( - [ - sys.executable, - "-m", - "pip", - "install", - "--trusted-host", - "pypi.python.org", - "-r", - "requirements.txt", - ], - env=environ.copy(), - ) +from pathlib import Path +from typing import Dict, Optional, Union + +from ..shared.config import Config +from ..utils.dependency import ( + get_installed_packages, + get_poetry_executable, + git_clone, + install_all_dependencies, + install_package, + install_tensorflow, + install_pytorch, +) +from ..utils.llama_cpp import build_shared_lib +from ..utils.logger import ApiLogger + +logger = ApiLogger(__name__) def set_priority(pid: Optional[int] = None, priority: str = "high"): @@ -57,16 +56,54 @@ def set_priority(pid: Optional[int] = None, priority: str = "high"): p.nice(priorities[priority]) -def initialize_before_launch(install_packages: bool = False): +def initialize_before_launch( + git_and_disk_paths: Optional[Dict[str, Union[str, Path]]] = None, + install_packages: bool = False, + force_cuda: bool = False, + skip_pytorch_install: bool = False, + skip_tensorflow_install: bool = False, +) -> None: """Initialize the app""" - if install_packages: - ensure_packages_installed() + # Git clone the repositories + if git_and_disk_paths is not None: + for git_path, disk_path in git_and_disk_paths.items(): + git_clone(git_path=git_path, disk_path=disk_path) - if platform.system() == "Windows": - set_priority(priority="high") + if install_packages: + # Install all dependencies + poetry = get_poetry_executable() + if not poetry.exists(): + # Install poetry + logger.warning(f"⚠️ Poetry not found: {poetry}") + install_package("poetry", force=True) + if not skip_pytorch_install: + # Install pytorch + install_pytorch(force_cuda=force_cuda) + if not skip_tensorflow_install: + # Install tensorflow + install_tensorflow() + + # Install all dependencies of our project and other repositories + project_paths = [Path(".")] + list(Path("repositories").glob("*")) + install_all_dependencies(project_paths=project_paths) + + # Build the shared library of LLaMA C++ code + build_shared_lib(logger=logger) + + # Get current packages installed + logger.info(f"📦 Installed packages: {get_installed_packages()}") else: - set_priority(priority="normal") + logger.warning( + "🏃‍♂️ Skipping package installation..." + "If any packages are missing, " + "use `--install-pkgs` option to install them." + ) + try: + # Set the priority of the process + set_priority(priority="high") + except Exception: + pass @asynccontextmanager @@ -74,7 +111,7 @@ async def lifespan(app): from ..utils.concurrency import pool from ..utils.logger import ApiLogger - ApiLogger.ccritical("🦙 LLaMA API server is running") + ApiLogger.cinfo("🦙 LLaMA API server is running") yield ApiLogger.ccritical("🦙 Shutting down LLaMA API server...") pool().kill() @@ -106,18 +143,66 @@ async def health(): def run( port: int, max_workers: int = 1, + install_packages: bool = False, + force_cuda: bool = False, + skip_pytorch_install: bool = False, + skip_tensorflow_install: bool = False, ) -> None: - initialize_before_launch(install_packages=True) + initialize_before_launch( + git_and_disk_paths=Config.git_and_disk_paths, + install_packages=install_packages, + force_cuda=force_cuda, + skip_pytorch_install=skip_pytorch_install, + skip_tensorflow_install=skip_tensorflow_install, + ) - from uvicorn import Config, Server + from uvicorn import Config as UvicornConfig + from uvicorn import Server as UvicornServer environ["MAX_WORKERS"] = str(max_workers) - Server( - config=Config( + UvicornServer( + config=UvicornConfig( create_app_llama_cpp(), host="0.0.0.0", port=port, log_level="info", ) ).run() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--install-pkgs", + action="store_true", + help="Install all required packages before running the server", + ) + parser.add_argument( + "--force-cuda", + action="store_true", + help=( + "Force CUDA version of pytorch to be used" + "when installing pytorch. e.g. torch==2.0.1+cu118" + ), + ) + parser.add_argument( + "--skip-torch-install", + action="store_true", + help="Skip installing pytorch, if `install-pkgs` is set", + ) + parser.add_argument( + "--skip-tf-install", + action="store_true", + help="Skip installing tensorflow, if `install-pkgs` is set", + ) + + args = parser.parse_args() + + initialize_before_launch( + git_and_disk_paths=Config.git_and_disk_paths, + install_packages=args.install_pkgs, + force_cuda=args.force_cuda, + skip_pytorch_install=args.skip_torch_install, + skip_tensorflow_install=args.skip_tf_install, + ) diff --git a/llama_api/server/pools/llama.py b/llama_api/server/pools/llama.py index 9e72692..a7e3ca5 100644 --- a/llama_api/server/pools/llama.py +++ b/llama_api/server/pools/llama.py @@ -4,7 +4,7 @@ from os import getpid from queue import Queue from threading import Event -from typing import Iterator +from typing import Deque, Dict, Iterator, List, Union import model_definitions @@ -33,11 +33,11 @@ logger = ApiLogger(__name__) -logger.critical(f"{current_process()} is initiated with PID: {getpid()}") +logger.info(f"🔧 {current_process()} is initiated with PID: {getpid()}") lazy = LazyImports() # lazy-loader of modules -completion_generators: deque["BaseCompletionGenerator"] = deque(maxlen=1) -embedding_generators: deque["BaseEmbeddingGenerator"] = deque(maxlen=1) +completion_generators: Deque["BaseCompletionGenerator"] = deque(maxlen=1) +embedding_generators: Deque["BaseEmbeddingGenerator"] = deque(maxlen=1) def init() -> None: @@ -46,19 +46,18 @@ def init() -> None: @contextmanager def completion_generator_manager( - body: CreateCompletionRequest - | CreateChatCompletionRequest - | CreateEmbeddingRequest, + body: Union[ + CreateCompletionRequest, + CreateChatCompletionRequest, + CreateEmbeddingRequest, + ], ): """Context manager for completion generators.""" completion_generator = get_completion_generator(body) - completion_generator.wait_until_available() - completion_generator.set_availability(False) yield completion_generator - completion_generator.set_availability(True) -def get_model_names() -> list[str]: +def get_model_names() -> List[str]: return [ k + f"({v.model_path})" for k, v in model_definitions.__dict__.items() @@ -75,9 +74,11 @@ def get_model(model_name: str) -> "BaseLLMModel": def get_completion_generator( - body: CreateCompletionRequest - | CreateChatCompletionRequest - | CreateEmbeddingRequest, + body: Union[ + CreateCompletionRequest, + CreateChatCompletionRequest, + CreateEmbeddingRequest, + ], ) -> "BaseCompletionGenerator": """Get a completion generator for the given model. If the model is not cached, create a new one. @@ -85,7 +86,7 @@ def get_completion_generator( try: # Check if the model is an OpenAI model - openai_replacement_models: dict[str, str] = getattr( + openai_replacement_models: Dict[str, str] = getattr( model_definitions, "openai_replacement_models", {} ) if body.model in openai_replacement_models: @@ -114,7 +115,6 @@ def get_completion_generator( # Before creating a new completion generator, check memory usage if completion_generators.maxlen == len(completion_generators): - completion_generators[-1].wait_until_available() free_memory_of_first_item_from_container( completion_generators, min_free_memory_mb=256, @@ -204,7 +204,7 @@ def get_embedding_generator( def generate_completion_chunks( - body: CreateChatCompletionRequest | CreateCompletionRequest, + body: Union[CreateChatCompletionRequest, CreateCompletionRequest], queue: Queue, event: Event, ) -> None: @@ -212,7 +212,7 @@ def generate_completion_chunks( with completion_generator_manager(body=body) as cg: if isinstance(body, CreateChatCompletionRequest): _iterator: Iterator[ - ChatCompletionChunk | CompletionChunk + Union[ChatCompletionChunk, CompletionChunk] ] = cg.generate_chat_completion_with_streaming( messages=body.messages, settings=body, @@ -222,11 +222,13 @@ def generate_completion_chunks( prompt=body.prompt, settings=body, ) - first_response: ChatCompletionChunk | CompletionChunk = next( + first_response: Union[ChatCompletionChunk, CompletionChunk] = next( _iterator ) - def iterator() -> Iterator[ChatCompletionChunk | CompletionChunk]: + def iterator() -> ( + Iterator[Union[ChatCompletionChunk, CompletionChunk]] + ): yield first_response for chunk in _iterator: yield chunk @@ -239,18 +241,18 @@ def iterator() -> Iterator[ChatCompletionChunk | CompletionChunk]: def generate_completion( - body: CreateChatCompletionRequest | CreateCompletionRequest, + body: Union[CreateChatCompletionRequest, CreateCompletionRequest], queue: Queue, event: Event, ) -> None: with queue_event_manager(queue=queue, event=event): with completion_generator_manager(body=body) as cg: if isinstance(body, CreateChatCompletionRequest): - completion: ChatCompletion | Completion = ( - cg.generate_chat_completion( - messages=body.messages, - settings=body, - ) + completion: Union[ + ChatCompletion, Completion + ] = cg.generate_chat_completion( + messages=body.messages, + settings=body, ) elif isinstance(body, CreateCompletionRequest): completion = cg.generate_completion( @@ -279,8 +281,8 @@ def generate_embeddings( embedding_generator: "BaseEmbeddingGenerator" = ( get_embedding_generator(body) ) - embeddings: list[ - list[float] + embeddings: List[ + List[float] ] = embedding_generator.generate_embeddings( texts=body.input if isinstance(body.input, list) diff --git a/llama_api/server/routers/v1.py b/llama_api/server/routers/v1.py index ad9004e..53ea12d 100644 --- a/llama_api/server/routers/v1.py +++ b/llama_api/server/routers/v1.py @@ -15,8 +15,10 @@ Callable, Iterator, Optional, + Tuple, Type, TypeVar, + Union, ) from anyio import ( @@ -68,7 +70,7 @@ class WixMetadata: # Worker index (wix) is used to keep track of which worker is currently # processing a request. This is used to prevent multiple requests from # creating multiple completion generators at the same time. -wixs: tuple[WixMetadata] = tuple(WixMetadata() for _ in range(MAX_WORKERS)) +wixs: Tuple[WixMetadata] = tuple(WixMetadata() for _ in range(MAX_WORKERS)) def validate_item_type(item: Any, type: Type[T]) -> T: @@ -197,7 +199,7 @@ async def create_chat_completion( queue, event = get_queue_and_event() producer: Callable[ [ - CreateChatCompletionRequest | CreateCompletionRequest, + Union[CreateChatCompletionRequest, CreateCompletionRequest], Queue, Event, ], @@ -260,7 +262,7 @@ async def create_completion( queue, event = get_queue_and_event() producer: Callable[ [ - CreateChatCompletionRequest | CreateCompletionRequest, + Union[CreateChatCompletionRequest, CreateCompletionRequest], Queue, Event, ], diff --git a/llama_api/shared/config.py b/llama_api/shared/config.py new file mode 100644 index 0000000..d45e567 --- /dev/null +++ b/llama_api/shared/config.py @@ -0,0 +1,17 @@ +from pathlib import Path +from typing import Dict, Tuple, Union + + +class Config: + """Configuration for the project""" + + project_root: Path = Path(__file__).parent.parent.parent + env_for_venv: Tuple[str, ...] = ("SYSTEMROOT", "CUDA_HOME", "CUDA_PATH") + cuda_version: str = "11.8" + torch_version: str = "==2.0.1" + torch_source: str = "https://download.pytorch.org/whl/torch_stable.html" + tensorflow_version: str = "==2.13.0" + git_and_disk_paths: Dict[str, Union[Path, str]] = { + "https://github.com/abetlen/llama-cpp-python": "repositories/llama_cpp", # noqa: E501 + "https://github.com/turboderp/exllama": "repositories/exllama", + } diff --git a/llama_api/utils/concurrency.py b/llama_api/utils/concurrency.py index 0de3e4c..8fc517e 100644 --- a/llama_api/utils/concurrency.py +++ b/llama_api/utils/concurrency.py @@ -4,15 +4,21 @@ from multiprocessing.managers import SyncManager from os import environ from queue import Queue +from sys import version_info from threading import Event -from typing import Callable, Optional, ParamSpec, Tuple, TypeVar +from typing import Callable, Dict, Optional, Tuple, TypeVar from fastapi.concurrency import run_in_threadpool -from ..server.app_settings import initialize_before_launch +from ..server.app_settings import set_priority from ..utils.logger import ApiLogger from ..utils.process_pool import ProcessPool +if version_info >= (3, 10): + from typing import ParamSpec +else: + from typing_extensions import ParamSpec + T = TypeVar("T") P = ParamSpec("P") @@ -21,31 +27,39 @@ _manager: Optional[SyncManager] = None -def init_process_pool(env_vars: dict[str, str]) -> None: +def init_process_pool(env_vars: Dict[str, str]) -> None: """Initialize the process pool, and set the environment variables for the child processes""" try: - initialize_before_launch(install_packages=False) + # Set the priority of the process + set_priority(priority="high") except Exception: pass for key, value in env_vars.items(): environ[key] = value + cuda_home = environ.get("CUDA_HOME", None) + cuda_path = environ.get("CUDA_PATH", None) + if cuda_path is not None and cuda_home is None: + environ["CUDA_HOME"] = cuda_path + elif cuda_home is not None and cuda_path is None: + environ["CUDA_PATH"] = cuda_home + def pool() -> ProcessPool: """Get the process pool, and initialize it if it's not initialized yet""" global _pool if _pool is None: - logger.critical("Initializing process pool...") + logger.info("Initializing process pool...") _pool = ProcessPool( max_workers=int(environ.get("MAX_WORKERS", 1)), initializer=init_process_pool, initargs=(dict(environ),), ) elif not _pool.is_available: - logger.critical("Process pool died. Reinitializing process pool...") + logger.critical("🚨 Process pool died. Reinitializing process pool...") _pool = ProcessPool( max_workers=int(environ.get("MAX_WORKERS", 1)), initializer=init_process_pool, @@ -70,7 +84,7 @@ def run_in_executor( func: Callable[P, T], *args: P.args, **kwargs: P.kwargs, -) -> Future[T]: +) -> "Future[T]": """Run a function in an executor, and return a future""" if loop.is_closed: diff --git a/llama_api/utils/dependency.py b/llama_api/utils/dependency.py new file mode 100644 index 0000000..a0cb6c3 --- /dev/null +++ b/llama_api/utils/dependency.py @@ -0,0 +1,387 @@ +import os +import sys +from contextlib import contextmanager +from importlib.util import find_spec +from pathlib import Path +from platform import mac_ver +from re import compile +from subprocess import PIPE, check_call, run +from tempfile import mkstemp +from typing import List, Optional, Union +from urllib.request import urlopen + +from ..shared.config import Config +from ..utils.logger import ApiLogger +from ..utils.system import get_cuda_version + +logger = ApiLogger(__name__) + + +def run_command( + command: List[str], + action: str, + name: str, + try_emoji: str = "📦", + success_emoji: str = "✅", + failure_emoji: str = "❌", + verbose: bool = True, + **kwargs, +) -> bool: + """Run a command and log the result. + Return True if the command was successful, False otherwise.""" + try: + if verbose: + logger.info( + f"{try_emoji} {action}ing {name} with command: " + f"{' '.join(command)}" + ) + result = run(command, text=True, stdout=PIPE, stderr=PIPE, **kwargs) + if result.returncode != 0: + if verbose: + logger.error( + f"{failure_emoji} Failed to {action} {name}:\n" + + (result.stdout or "") + + (result.stderr or "") + ) + return False + else: + if verbose: + logger.info(f"{success_emoji} Successfully {name} {action}ed.") + return True + except Exception as e: + if verbose: + logger.error( + f"{failure_emoji} Failed to {action} {name}:\n" + str(e) + ) + return False + + +def is_package_available(package: str) -> bool: + return True if find_spec(package) else False + + +def git_clone(git_path: str, disk_path: Union[Path, str]) -> Optional[bool]: + """Clone a git repository to a disk path.""" + if not Path(disk_path).exists(): + return run_command( + ["git", "clone", git_path, str(disk_path)], + action="clone", + name=f"{git_path} to {disk_path}", + try_emoji="📥", + ) + return None + + +def get_mac_major_version_string(): + # platform.mac_ver() returns a tuple ('10.16', ('', '', ''), 'x86_64') + # Split the version string on '.' and take the first two components + major = mac_ver()[0].split(".")[0] + + # Join the components with '_' and prepend 'macosx_' + return "macosx_" + major + + +def get_installed_packages() -> List[str]: + """Return a list of installed packages""" + return [ + package.split("==")[0] + for package in run( + [sys.executable, "-m", "pip", "freeze"], + text=True, + stdout=PIPE, + stderr=PIPE, + ) + .stdout.strip() + .split("\n") + ] + + +def get_poetry_executable() -> Path: + """Construct the path to the poetry executable + within the virtual environment. + Check the operating system to determine the correct location""" + if os.name == "nt": # Windows + return Path(sys.prefix) / "Scripts" / "poetry.exe" + else: # Linux or Mac + return Path(sys.prefix) / "bin" / "poetry" + + +def get_proper_torch_cuda_version( + cuda_version: str, + source: str = Config.torch_source, + fallback_cuda_version: str = "11.8", +) -> str: + """Helper function that returns the proper CUDA version of torch.""" + if cuda_version == fallback_cuda_version: + return fallback_cuda_version + elif check_if_torch_version_available( + version=f'cu{cuda_version.replace(".", "")}', source=source + ): + return cuda_version + else: + return fallback_cuda_version + + +def check_if_torch_version_available( + version: str = Config.torch_version, + source: str = Config.torch_source, +) -> bool: + """Helper function that checks if the version of torch is available""" + try: + # Determine the version of python, CUDA, and platform + canonical_version = compile(r"([0-9\.]+)").search(version) + if not canonical_version: + return False + package_ver = canonical_version.group().encode() + python_ver = ( + f"cp{sys.version_info.major}{sys.version_info.minor}" + ).encode() + if "win32" in sys.platform: + platform = "win_amd64".encode() + elif "linux" in sys.platform: + platform = "linux_x86_64".encode() + elif "darwin" in sys.platform: + platform = get_mac_major_version_string().encode() + else: + return False + + # Check if the CUDA version of torch is available + for line in urlopen(source).read().splitlines(): + if package_ver in line and python_ver in line and platform in line: + return True + return False + except Exception: + return False + + +def parse_requirements( + requirements: str, + excludes: Optional[List[str]] = None, + include_version: bool = True, +) -> List[str]: + """Parse requirements from a string. + Args: + requirements: The string of requirements.txt to parse. + excludes: A list of packages to exclude. + include_version: + Whether to include the version in the parsed requirements. + Returns: + A list of parsed requirements. + """ + # Define the regular expression pattern + pattern = compile(r"([a-zA-Z0-9_\-\+]+)(==|>=|<=|~=|>|<|!=|===)([0-9\.]+)") + + # Use finditer to get all matches in the string + return [ + match.group() if include_version else match.group(1) + for match in pattern.finditer(requirements) + if not excludes or match.group(1) not in excludes + ] + + +def convert_toml_to_requirements_with_poetry( + toml_path: Path, + *args, + format: str = "requirements.txt", + output: str = "requirements.txt", +) -> Optional[bool]: + """Convert dependencies from pyproject.toml to requirements.txt.""" + poetry = str(get_poetry_executable()) + command = [poetry, "export", "-f", format, "--output", output, *args] + try: + if toml_path.exists(): + # Convert dependencies from pyproject.toml to requirements.txt + return run_command( + command, + action="convert", + name="pyproject.toml to requirements.txt", + try_emoji="🔄", + cwd=toml_path.parent, + ) + except Exception: + return None + + +@contextmanager +def import_repository(git_path: str, disk_path: str): + """ + Import a repository from git. The repository will be cloned to disk_path. + The dependencies will be installed from pyproject.toml or requirements.txt. + """ + + # Clone the repository + git_clone(git_path=git_path, disk_path=disk_path) + + # Add the repository to the path so that it can be imported + sys.path.insert(0, str(disk_path)) + yield + sys.path.remove(str(disk_path)) + + +def install_package(package: str, force: bool = False) -> bool: + """Install a package with pip.""" + if not force and is_package_available(package): + return True + return run_command( + [sys.executable, "-m", "pip", "install", package], + action="install", + name=package, + ) + + +def install_poetry() -> bool: + """Install poetry.""" + logger.info("📦 Installing poetry...") + return run_command( + [sys.executable, "-m", "pip", "install", "poetry"], + action="install", + name="poetry", + ) + + +def install_pytorch( + torch_version: str = Config.torch_version, + cuda_version: Optional[str] = Config.cuda_version, + source: Optional[str] = Config.torch_source, + force_cuda: bool = False, +) -> bool: + """Try to install Pytorch. + If CUDA is available, install the CUDA version of torch. + Else, install the CPU version of torch. + Args: + torch_version (str): The version of torch. + Defaults to Config.torch_version. + cuda_version (str): The version of CUDA. + Defaults to Config.cuda_version. + source (Optional[str]): The source to install torch from. + Defaults to Config.torch_source. + force_cuda (bool): Whether to force install the CUDA version of torch. + Returns: + bool: True if Pytorch is installed successfully, else False.""" + pip_install = [sys.executable, "-m", "pip", "install"] + fallback_cuda_version = Config.cuda_version + # If a source is specified, and if CUDA is available, + # install the CUDA version of torch + if force_cuda or (source and get_cuda_version()): + # Check if the CUDA version of torch is available. + # If not, fallback to `Config.cuda_version`. + cuda_version = ( + get_proper_torch_cuda_version( + cuda_version=cuda_version, + fallback_cuda_version=fallback_cuda_version, + ) + if cuda_version is not None + else fallback_cuda_version + ) + pip_install.append( + f'torch{torch_version}+cu{cuda_version.replace(".", "")}' + ) + # If a source is specified, add it to the pip install command + pip_install += ["-f", source] + elif source: + # If a source is specified, but CUDA is not available, + + if check_if_torch_version_available( + version=f"{torch_version}+cpu", + source=source, + ): + # install the CPU version of torch if available + pip_install.append(f"torch{torch_version}+cpu") + else: + # else, install the canonical version of torch + pip_install.append(f"torch{torch_version}") + + # If a source is specified, add it to the pip install command + pip_install += ["-f", source] + else: + # If source is not specified, install the canonical version of torch + pip_install.append(f"torch{torch_version}") + + # Install torch + return run_command(pip_install, action="install", name="PyTorch") + + +def install_tensorflow( + tensorflow_version: str = Config.tensorflow_version, + source: Optional[str] = None, +) -> bool: + """Try to install TensorFlow. + + Args: + tensorflow_version (str): The version of TensorFlow. + Defaults to Config.tensorflow_version. + source (Optional[str]): The source to install TensorFlow from. + If not specified, TensorFlow will be installed from PyPi. + Returns: + bool: True if TensorFlow is installed successfully, else False. + """ + pip_install = [ + sys.executable, + "-m", + "pip", + "install", + f"tensorflow{tensorflow_version}", + ] + + # If a source is specified, add it to the pip install command + if source: + pip_install += ["-f", source] + + # Install TensorFlow + return run_command(pip_install, action="install", name="TensorFlow") + + +def install_all_dependencies( + project_paths: Optional[Union[List[Path], List[str]]] = None, +) -> Optional[bool]: + """Install every dependencies.""" + pip_install = [sys.executable, "-m", "pip", "install", "-r"] + result = True + for project_path in project_paths or []: + project_path = Path(project_path).resolve() + logger.info(f"📦 Installing dependencies for {project_path}...") + convert_toml_to_requirements_with_poetry( + project_path / "pyproject.toml", "--without-hashes" + ) + requirements_path = project_path / "requirements.txt" + if not requirements_path.exists(): + logger.warning( + f"⚠️ Could not find requirements.txt in {project_path}." + ) + continue + result &= run_command( + pip_install + [requirements_path.as_posix()], + action="install", + name="dependencies", + ) + return result + + +def remove_all_dependencies(): + """Remove all dependencies. + To be used when cleaning up the environment.""" + logger.critical("Removing all dependencies...") + # Create a temporary file + fd, temp_path = mkstemp() + + try: + # Step 1: List out all installed packages + with open(temp_path, "w") as temp_file: + check_call( + [sys.executable, "-m", "pip", "freeze"], stdout=temp_file + ) + + # Step 2: Uninstall all packages listed in the temp file + with open(temp_path, "r") as temp_file: + packages = [line.strip() for line in temp_file if "-e" not in line] + + for package in packages: + # The "--yes" option automatically confirms the uninstallation + check_call( + [sys.executable, "-m", "pip", "uninstall", "--yes", package] + ) + finally: + # Close the file descriptor and remove the temporary file + os.close(fd) + os.remove(temp_path) + logger.critical("All dependencies removed.") diff --git a/llama_api/utils/errors.py b/llama_api/utils/errors.py index bbe540c..5086ce5 100644 --- a/llama_api/utils/errors.py +++ b/llama_api/utils/errors.py @@ -1,7 +1,7 @@ from os import getpid, kill from re import Match, compile from signal import SIGINT -from typing import Callable, Optional +from typing import Callable, Optional, Tuple, Union from fastapi import Request, Response from fastapi.responses import JSONResponse @@ -28,9 +28,9 @@ class ErrorResponse(TypedDict): class ErrorResponseCallbacks: @staticmethod def token_exceed_callback( - request: CreateCompletionRequest | CreateChatCompletionRequest, - match: Match[str], - ) -> tuple[int, ErrorResponse]: + request: Union[CreateCompletionRequest, CreateChatCompletionRequest], + match: "Match[str]", + ) -> Tuple[int, ErrorResponse]: context_window = int(match.group(2)) prompt_tokens = int(match.group(1)) completion_tokens = request.max_tokens @@ -69,11 +69,13 @@ def wrap_error_message_as_openai( self, error: Exception, body: Optional[ - CreateCompletionRequest - | CreateChatCompletionRequest - | CreateEmbeddingRequest + Union[ + CreateCompletionRequest, + CreateChatCompletionRequest, + CreateEmbeddingRequest, + ] ] = None, - ) -> tuple[int, ErrorResponse]: + ) -> Tuple[int, ErrorResponse]: if body is None or isinstance(body, CreateEmbeddingRequest): return 500, ErrorResponse( message=str(error), @@ -104,7 +106,7 @@ async def custom_route_handler(request: Request) -> Response: try: return await original_route_handler(request) except (OSError, MemoryError) as e: - logger.exception(f"Exception in llama-cpp: {e}") + logger.exception(f"Exception in llama api: {e}") if isinstance(e, MemoryError): error_msg = str(e) else: @@ -122,7 +124,7 @@ async def custom_route_handler(request: Request) -> Response: 500, ) except Exception as e: - logger.exception(f"Exception in llama-cpp: {e}") + logger.exception(f"Exception in llama api: {e}") json_body = await request.json() try: diff --git a/llama_api/utils/lazy_imports.py b/llama_api/utils/lazy_imports.py index 25981d1..19be2ea 100644 --- a/llama_api/utils/lazy_imports.py +++ b/llama_api/utils/lazy_imports.py @@ -4,13 +4,13 @@ from functools import wraps -from typing import Callable, TypeVar, Union -from .logger import ApiLogger +from typing import Callable, Set, TypeVar, Union +from .logger import ApiLogger T = TypeVar("T") logger = ApiLogger(__name__) -logged_modules: set[str] = set() +logged_modules: Set[str] = set() def try_import(module_name: str): @@ -34,7 +34,7 @@ def wrapper(*args, **kwargs) -> Union[T, Exception]: logger.info(f"🦙 Successfully imported {module_name}!") return result except Exception as e: - logger.error(f"🦙 Error importing {module_name}: {e}") + logger.exception(f"🦙 Error importing {module_name}: {e}") return e finally: # Add the module to the `logged_modules` set diff --git a/llama_api/utils/llama_cpp.py b/llama_api/utils/llama_cpp.py index 4a5a441..d49e416 100644 --- a/llama_api/utils/llama_cpp.py +++ b/llama_api/utils/llama_cpp.py @@ -1,16 +1,22 @@ -from contextlib import contextmanager -from os import chdir, environ, getcwd -from shutil import copy +import shutil import subprocess import sys +from contextlib import contextmanager from logging import Logger, getLogger +from os import chdir, environ, getcwd from pathlib import Path -from typing import Optional +from typing import List, Optional, Union + +from ..utils.dependency import install_package, run_command +from ..utils.system import get_cuda_version # You can set the CMAKE_ARGS environment variable to change the cmake args. -# cuBLAS is default to ON, -# but if it fails to build, fall back to the default settings (CPU only) -CMAKE_ARGS: str = "-DLLAMA_STATIC=Off -DBUILD_SHARED_LIBS=ON -DLLAMA_CUBLAS=ON" +# cuBLAS is default to ON if CUDA is installed. +# CPU inference is default if CUDA is not installed. +if get_cuda_version() is None: + CMAKE_ARGS: str = "-DBUILD_SHARED_LIBS=ON" +else: + CMAKE_ARGS = "-DBUILD_SHARED_LIBS=ON -DLLAMA_CUBLAS=ON" LIB_BASE_NAME: str = "llama" REPOSITORY_FOLDER: str = "repositories" @@ -63,9 +69,9 @@ def _git_clone() -> None: subprocess.run(clone_command, cwd=cwd) -def _get_libs() -> list[str]: +def _get_libs() -> List[str]: # Determine the libs based on the platform - if sys.platform.startswith("linux"): + if "linux" in sys.platform: return [ f"lib{LIB_BASE_NAME}.so", ] @@ -82,34 +88,114 @@ def _get_libs() -> list[str]: raise RuntimeError("Unsupported platform") -def _get_lib_paths(base_path: Path) -> list[Path]: +def _get_lib_paths(base_path: Path) -> List[Path]: # Determine the lib paths based on the platform return [base_path / lib for lib in _get_libs()] -def _copy_skbuild_libs_to_target( - cmake_dir: Path, target_dir: Path -) -> list[Path]: +def _copy_make_libs_to_target(make_dir: Path, target_dir: Path) -> None: # Copy the built libs to the target folder - source_libs: Optional[list[Path]] = None - for dir in (cmake_dir / "_skbuild").glob("*"): - if dir.is_dir(): - print(f"~~~ Checking {dir}") - source_libs = [ - source_lib - for source_lib in (dir / "cmake-install" / MODULE_NAME).glob( - "*" - ) - if source_lib.name in _get_libs() - ] - if source_libs: - print(f"~~~ Found {source_libs}") - break - assert source_libs is not None, "Could not find build libs" - - for source_lib in source_libs: - copy(source_lib, target_dir) - return source_libs + for lib_name in _get_libs(): + lib = make_dir / lib_name + if lib.exists(): + print(f"~~~ Found shared library: {lib}") + shutil.copy(lib, target_dir) + else: + print(f"~~~ Library {lib_name} not found") + + +def _copy_cmake_libs_to_target(cmake_dir: Path, target_dir: Path) -> None: + # Copy the built libs to the target folder + for lib_name in _get_libs(): + lib = cmake_dir / "build" / "bin" / "Release" / lib_name + if lib.exists(): + print(f"~~~ Found shared library: {lib}") + shutil.copy(lib, target_dir) + else: + print(f"~~~ Library {lib_name} not found") + + +def _get_cmake_args(cmake_args: Union[str, List[str]]) -> List[str]: + if isinstance(cmake_args, str): + cmake_args = cmake_args.split(" ") + if "-DBUILD_SHARED_LIBS=ON" not in cmake_args: + cmake_args.append("-DBUILD_SHARED_LIBS=ON") + return cmake_args + + +def _cmake_args_to_make_args(cmake_args: List[str]) -> List[str]: + # initialize an empty list to store the converted parts + result: List[str] = [] + # loop through each part + for cmake_arg in cmake_args: + # capitalize all letters + cmake_arg = cmake_arg.upper() + + # replace `ON` with `1` and `OFF` with `0` + cmake_arg = cmake_arg.replace("=ON", "=1").replace("=OFF", "=0") + + # remove the `-D` flag + if cmake_arg.startswith("-D"): + cmake_arg = cmake_arg[2:] + + # append the converted part to the result list + result.append(cmake_arg) + return result + + +def _make(make_dir: Path, make_args: List[str], target_dir: Path) -> None: + # Run make to build the shared lib + + # Build the shared lib + run_command( + ["make", *make_args], + action="build", + name="llama.cpp shared lib", + cwd=make_dir, + ) + for lib in _get_libs(): + run_command( + ["make", lib], + action="build", + name="llama.cpp shared lib", + cwd=make_dir, + ) + + # Copy the built libs to the target folder + _copy_make_libs_to_target(make_dir=make_dir, target_dir=target_dir) + + +def _cmake(cmake_dir: Path, cmake_args: List[str], target_dir: Path) -> None: + # Run cmake to build the shared lib + build_dir = cmake_dir / "build" + if build_dir.exists(): + # If the build folder exists, delete it + shutil.rmtree(build_dir) + + # Create the build folder + build_dir.mkdir(exist_ok=True) + + # Check if cmake is installed + if not run_command(["cmake"], action="check", name="cmake", verbose=False): + # If cmake is not installed, try to install it + install_package("cmake", force=True) + + # Build the shared lib + run_command( + ["cmake", *cmake_args, ".."], + action="build", + name="llama.cpp shared lib", + cwd=build_dir, + ) + run_command( + ["cmake", "--build", ".", "--config", "Release"], + action="build", + name="llama.cpp shared lib", + cwd=build_dir, + ) + + # Copy the built libs to the target folder + _copy_cmake_libs_to_target(cmake_dir=cmake_dir, target_dir=target_dir) def build_shared_lib( @@ -129,34 +215,22 @@ def build_shared_lib( if force_cmake or not any( lib_path.exists() for lib_path in _get_lib_paths(MODULE_PATH) ): - target_dir = MODULE_PATH - # Build the libs - with _temporary_change_cwd(PROJECT_PATH): - # Try to build the lib with cmake - environ["FORCE_CMAKE"] = "1" - if environ.get("CMAKE_ARGS") is None: - environ["CMAKE_ARGS"] = CMAKE_ARGS - - logger.critical( - f"🦙 Building llama.cpp libs with {environ['CMAKE_ARGS']}" + # Try to build the lib with cmake + cmake_dir = VENDOR_PATH + cmake_args_str = environ.get("CMAKE_ARGS", CMAKE_ARGS) + if sys.platform == "win32": + _cmake( + cmake_dir=cmake_dir, + cmake_args=_get_cmake_args(cmake_args_str), + target_dir=MODULE_PATH, + ) + else: + _make( + make_dir=cmake_dir, + make_args=_cmake_args_to_make_args( + _get_cmake_args(cmake_args_str) + ), + target_dir=MODULE_PATH, ) - subprocess.run([sys.executable, "-m", "pip", "install", "."]) - - # Move the built libs to the target folder - source_libs = _copy_skbuild_libs_to_target( - cmake_dir=PROJECT_PATH, target_dir=target_dir - ) - logger.info(f"🦙 llama.cpp libs built with `{environ['CMAKE_ARGS']}`") - for source_lib in source_libs: - logger.info(f"~~~ Moved {source_lib.name} to {target_dir}") return - - -if __name__ == "__main__": - root_path = Path(__file__).parent.parent.parent - sys.path.insert(0, root_path.as_posix()) - - from llama_api.utils.logger import ApiLogger - - build_shared_lib(force_cmake=True, logger=ApiLogger(__name__)) diff --git a/llama_api/utils/logger.py b/llama_api/utils/logger.py index 40dcf72..7cfe2bf 100644 --- a/llama_api/utils/logger.py +++ b/llama_api/utils/logger.py @@ -3,7 +3,7 @@ import logging import os from dataclasses import dataclass -from typing import Optional +from typing import Dict, Optional from .colorama import Fore, Style @@ -38,7 +38,7 @@ def format(self, record: logging.LogRecord): class ApiLogger(logging.Logger): - _instances: dict[str, "ApiLogger"] = {} + _instances: Dict[str, "ApiLogger"] = {} def __new__( cls, name: str, logging_config: LoggingConfig = LoggingConfig() diff --git a/llama_api/utils/path.py b/llama_api/utils/path.py index 1dba0b0..62cc0fd 100644 --- a/llama_api/utils/path.py +++ b/llama_api/utils/path.py @@ -1,51 +1,34 @@ -from os import getcwd -from subprocess import run -import sys -from contextlib import contextmanager from pathlib import Path from typing import Optional +from ..shared.config import Config from ..utils.logger import ApiLogger logger = ApiLogger(__name__) -@contextmanager -def import_repository(git_path: str, disk_path: str): - if not Path(disk_path).exists(): - run(["git", "clone", git_path, disk_path]) - sys.path.insert(0, str(disk_path)) - yield - sys.path.remove(str(disk_path)) - - -@contextmanager -def relative_import(path: str): - sys.path.insert(0, str(path)) - yield - sys.path.remove(str(path)) - - -@contextmanager -def suppress_import_error(): - try: - yield - except ImportError as e: - logger.warning(f"ImportError: {e}") - - def resolve_model_path_to_posix( model_path: str, default_relative_directory: Optional[str] = None ) -> str: - """Resolve a model path to a POSIX path, relative to the BASE_DIR.""" - cwd = getcwd() + """Resolve a model path to a POSIX path.""" path = Path(model_path) - parent_directory: Path = ( - Path(cwd) / Path(default_relative_directory) - if default_relative_directory is not None - else Path(cwd) - if Path.cwd() == path.parent.resolve() - else path.parent.resolve() - ) - filename: str = path.name - return (parent_directory / filename).as_posix() + cwd = Path.cwd() + + # Try to find the model in all possible scenarios + parent_dir_candidates = [ + cwd, + Config.project_root, + Config.project_root / "models", + Config.project_root / "models" / "ggml", + Config.project_root / "models" / "gptq", + Config.project_root / "llama_api", + ] + if default_relative_directory is not None: + parent_dir_candidates.insert(0, cwd / Path(default_relative_directory)) + for parent_dir in parent_dir_candidates: + if (parent_dir / model_path).exists(): + logger.info(f"`{path.name}` found in {parent_dir}") + return (parent_dir / model_path).resolve().as_posix() + logger.warning(f"`{path.name}` not found in {parent_dir}") + logger.error(f"`{path.name}` not found in any of the above directories") + return model_path diff --git a/llama_api/utils/process_pool.py b/llama_api/utils/process_pool.py index 66aaf88..ecaed7a 100644 --- a/llama_api/utils/process_pool.py +++ b/llama_api/utils/process_pool.py @@ -16,13 +16,17 @@ Iterable, List, Optional, - ParamSpec, Tuple, Type, TypeVar, Union, ) +if sys.version_info >= (3, 10): + from typing import ParamSpec +else: + from typing_extensions import ParamSpec + class _WrappedWorkerException(Exception): # type: ignore def __init__( @@ -94,7 +98,7 @@ def _chunked_fn(fn: Callable[..., T], *args: Tuple[Any, ...]) -> List[T]: def _process_chunk( fn: Callable[..., T], chunk: Iterable[Tuple[Any, ...]] -) -> List[partial[List[T]]]: +) -> "List[partial[List[T]]]": """Processes a chunk of an iterable passed to map. Runs the function passed to map() on a chunk of the @@ -217,6 +221,10 @@ def __init__( ) self.process.start() + @property + def is_alive(self) -> bool: + return self.process.is_alive() + def send(self, job: Job) -> None: partialed_func, _, future = job @@ -405,7 +413,7 @@ def _job_manager_thread(self) -> None: """Manages dispatching jobs to processes, checking results, sending them to futures and restarting if they die""" while True: - busy_procs = [] # type: list[int] + busy_procs = [] # type: List[int] for wix, worker in enumerate(self._pool): if worker is None: continue @@ -440,7 +448,7 @@ def _job_manager_thread(self) -> None: idle_procs = [ wix for wix in range(self.max_workers) if wix not in busy_procs - ] # type: list[int] + ] # type: List[int] if not idle_procs: # All workers are busy, let's wait for one to become idle sleep(SLEEP_TICK) @@ -542,9 +550,9 @@ def map( # Create multiple partial functions so that we can send # multiple arguments to map - chunked_funcs: List[partial[List]] = _process_chunk( + chunked_funcs = _process_chunk( fn, chunk=_get_chunks(*iterables, chunksize=chunksize) - ) + ) # type: List[partial[List]] # Submit all the partial functions to the pool chunked_futures: List[Future[List]] = [ diff --git a/llama_api/utils/system.py b/llama_api/utils/system.py index 05098d5..f2f4410 100644 --- a/llama_api/utils/system.py +++ b/llama_api/utils/system.py @@ -1,7 +1,9 @@ +from collections import deque from gc import collect from logging import INFO, getLogger -from typing import TYPE_CHECKING, Any, Optional, Union -from collections import deque +from re import compile +from subprocess import PIPE, check_output, run +from typing import TYPE_CHECKING, Any, List, Optional, Union if TYPE_CHECKING: from asyncio import Queue as AsyncQueue @@ -11,12 +13,24 @@ ContainerLike = Union["deque", "Queue", "AsyncQueue", list, dict] -def get_vram_usages() -> Optional[list[int]]: +def get_cuda_version() -> Optional[str]: + """Returns the current CUDA version as a string. + Returns None if nvidia-smi is not available or CUDA is not installed.""" + try: + result = compile(r"CUDA Version: (\d+\.\d+)").search( + check_output(["nvidia-smi"]).decode("utf-8") + ) + if result is None: + return + return result.group(1) + except Exception: + return + + +def get_vram_usages() -> Optional[List[int]]: """Returns a list of memory usage in MB for each GPU. Returns None if nvidia-smi is not available.""" try: - from subprocess import PIPE, run - result = run( [ "nvidia-smi", diff --git a/llama_api/utils/venv.py b/llama_api/utils/venv.py new file mode 100644 index 0000000..2ff8d1e --- /dev/null +++ b/llama_api/utils/venv.py @@ -0,0 +1,145 @@ +import os +import shutil +import subprocess +import sys +from pathlib import Path +from typing import Dict, Optional, Tuple, Union + +from ..shared.config import Config + + +class VirtualEnvironment: + def __init__(self, venv_path: Union[Path, str]) -> None: + self.venv_path = Path(venv_path).resolve() + self.env_for_venv = Config.env_for_venv + self._executable: Optional[Path] = None + self._env: Optional[Dict[str, str]] = None + + def remove(self) -> int: + """Remove the venv if it exists. + If successful, return 0. Otherwise, return 1.""" + try: + if self.venv_path.exists(): + shutil.rmtree(self.venv_path) + return 0 + except OSError: + return 1 + + def create(self) -> int: + """Create a virtual environment. + If successful, return 0. Otherwise, return non-zero exit code""" + assert ( + subprocess.check_call( + [ + sys.executable, + "-m", + "pip", + "install", + "--upgrade", + "pip", + "virtualenv", + ], + stdout=subprocess.DEVNULL, + ) + == 0 + ), "Failed to install virtualenv." + return subprocess.check_call( + [sys.executable, "-m", "virtualenv", self.venv_path.as_posix()], + stdout=subprocess.DEVNULL, + ) + + def recreate(self) -> int: + """Remove and create a virtual environment""" + self.remove() + return self.create() + + def get_settings(self) -> Tuple[Path, Dict[str, str]]: + """Return the path and the environment variables. + These will be used to run commands in the virtual environment.""" + + # Create the virtual environment if it does not exist. + if not self.venv_path.exists(): + self.create() + + # The name of the Python executable may vary across platforms. + python_executable = ( + "python.exe" if sys.platform == "win32" else "python" + ) + + # The name of the Python executable and the directory + # it resides in may vary across platforms. + if sys.platform == "win32": + python_executable = "python.exe" + executable_directory = "Scripts" + else: + python_executable = "python" + executable_directory = "bin" + + venv_python_path = ( + self.venv_path / executable_directory / python_executable + ) + + # Verify if the path is correct. + if not venv_python_path.exists(): + raise FileNotFoundError(f"{venv_python_path} does not exist.") + + # Create the environment variables. + # Copy only the environment variables that are needed. + # This is for security reasons. + env = { + "PATH": venv_python_path.parent.as_posix(), + "VIRTUAL_ENV": venv_python_path.parent.parent.as_posix(), + } + for var in self.env_for_venv: + if var in os.environ: + env[var] = os.environ[var] + + # Check if the virtual environment is correct. + check_command = [venv_python_path, "-c", "import sys; sys.executable"] + exit_code = subprocess.check_call( + check_command, env=env, stdout=subprocess.DEVNULL + ) + assert ( + exit_code == 0 + ), "The virtual environment is not configured correctly." + + # Return the path and the environment variables. + return venv_python_path, env + + def pip(self, *commands: str, stdout: Optional[int] = None) -> int: + """Run a pip command in the virtual environment. + Return the exit code.""" + original_env = os.environ.copy() + executable, env = self.get_settings() + original_env.update(env) + return subprocess.check_call( + [executable.as_posix(), "-m", "pip", *commands], + env=original_env, + stdout=stdout, + ) + + def run_script( + self, script_path: Union[Path, str] + ) -> subprocess.CompletedProcess[str]: + """Run a python script in the virtual environment. + Return the completed process object. + This contains the returncode, stdout, and stderr.""" + executable, env = self.get_settings() + return subprocess.run( + [executable.as_posix(), Path(script_path).as_posix()], + env=env, + text=True, + stdout=subprocess.PIPE, + ) + + @property + def executable(self) -> Path: + if self._executable is None: + self._executable = self.get_settings()[0] + return self._executable + + @property + def env(self) -> Dict[str, str]: + if self._env is None: + self._env = self.get_settings()[1] + return self._env diff --git a/main.py b/main.py index 8456c5d..c6c34e7 100644 --- a/main.py +++ b/main.py @@ -8,17 +8,44 @@ "--port", type=int, default=8000, - help="Port to run the server on", + help="Port to run the server on; default is 8000", ) parser.add_argument( "--max-workers", type=int, default=1, - help="Maximum number of process workers to run", + help="Maximum number of process workers to run; default is 1", + ) + parser.add_argument( + "--install-pkgs", + action="store_true", + help="Install all required packages before running the server", + ) + parser.add_argument( + "--force-cuda", + action="store_true", + help=( + "Force CUDA version of pytorch to be used" + "when installing pytorch. e.g. torch==2.0.1+cu118" + ), + ) + parser.add_argument( + "--skip-torch-install", + action="store_true", + help="Skip installing pytorch, if `install-pkgs` is set", + ) + parser.add_argument( + "--skip-tf-install", + action="store_true", + help="Skip installing tensorflow, if `install-pkgs` is set", ) args = parser.parse_args() run( port=args.port, max_workers=args.max_workers, + install_packages=args.install_pkgs, + force_cuda=args.force_cuda, + skip_pytorch_install=args.skip_torch_install, + skip_tensorflow_install=args.skip_tf_install, ) diff --git a/model_definitions.py b/model_definitions.py index 3b2fc1b..c14c8d2 100644 --- a/model_definitions.py +++ b/model_definitions.py @@ -1,3 +1,4 @@ +from typing import Dict from llama_api.schemas.models import ExllamaModel, LlamaCppModel # ================== LLaMA.cpp models ================== # @@ -12,6 +13,10 @@ model_path="llama-2-13b-chat.ggmlv3.q4_K_M.bin", ) +test_vocab = LlamaCppModel( + max_total_tokens=4096, + model_path="repositories/llama_cpp/vendor/llama.cpp/models/ggml-vocab.bin", +) # ================== ExLLaMa models ================== # orca_mini_7b = ExllamaModel( @@ -19,12 +24,21 @@ max_total_tokens=4096, compress_pos_emb=2.0, ) +stable_beluga_13b = ExllamaModel( + model_path="stable_beluga_13b", # model_path here + max_total_tokens=4096, +) +nous_hermes_llama_2_13b = ExllamaModel( + model_path="nous_hermes_llama_2_13b", # model_path here + max_total_tokens=8192, + compress_pos_emb=2.0, +) # Define a mapping from OpenAI model names to LLaMA models. # e.g. If you request API model "gpt-3.5-turbo", # the API will load the LLaMA model "orca_mini_3b" -openai_replacement_models: dict[str, str] = { - "gpt-3.5-turbo": "orca_mini_3b", - "gpt-4": "orca_mini_7b", +openai_replacement_models: Dict[str, str] = { + "gpt-3.5-turbo": "stable_beluga_13b", + "gpt-4": "nous_hermes_llama_2_13b", } diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..22f6c60 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,3293 @@ +# This file is automatically @generated by Poetry 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+sse-starlette = "^1.6" +psutil = "^5.9" +cmake = ">=3.18.0" +filelock = "^3.12" +transformers = "^4.31.0" +tensorflow-hub = "^0.14" +numpy = "^1.24.3" +safetensors = "^0.3.1" +ninja = "^1.11.1" +diskcache = "^5.6.1" +pydantic = "^2.0.0" +pydantic-settings = "^2.0.0" +sentencepiece = ">=0.1.97" +typing-extensions = ">=4.0.0" +# torch: 2.0.1+cu118 for GPU, 2.0.1+cpu for CPU + +[tool.poetry.group.dev.dependencies] +black = "^23.7.0" +twine = "^4.0.2" +flake8 = "^6.0.0" +mkdocs = "^1.4.3" +mkdocstrings = { extras = ["python"], version = "^0.22.0" } +mkdocs-material = "^9.1.19" +pytest = "^7.4.0" +pytest-asyncio = "^0.21.1" +httpx = "^0.24.1" + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/readme.md b/readme.md index 5c86312..fb86b39 100644 --- a/readme.md +++ b/readme.md @@ -13,14 +13,16 @@ You can use this server to run the models in your own application, or use it as 2. **Parallelism and Concurrency Enabled** - Due to the internal operation of the process pool, both parallelism and concurrency are secured. The `--max-workers $NUM_WORKERS` option needs to be provided when starting the server. This, however, only applies when requests are made simultaneously for different models. If requests are made for the same model, they will wait until a slot becomes available due to the semaphore. +3. **Auto Dependency Installation** + - The project automatically do git clones and installs the required dependencies, including **pytorch** and **tensorflow**, when the server is started. This is done by checking the `pyproject.toml` or `requirements.txt` file in the root directory of this project or other repositories. `pyproject.toml` will be parsed into `requirements.txt` with `poetry`. If you want to add more dependencies, simply add them to the file. ## Before you start -1. **Python 3.11** is required to run the server. You can download it from https://www.python.org/downloads/ +1. **Python 3.8 / 3.9 / 3.10 / 3.11** is required to run the server. You can download it from https://www.python.org/downloads/ -2. **CMake** is required to build llama.cpp shared library. You can download it from https://cmake.org/download/ +2. **llama.cpp**: To use llama.cpp, and if you are **Windows** user, download [CMake](https://cmake.org/download/) to compile library. -3. **CUDA 11.8** is required to build cuBLAS build of llama.cpp. You can download it from https://developer.nvidia.com/cuda-11-8-0-download-archive +3. **ExLlama**: To use ExLlama, install the prerequisites of this [repository](https://github.com/turboderp/exllama). Maybe **Windows** user needs to install both [MSVC 2022](https://visualstudio.microsoft.com/downloads/) and [CUDA Toolkit 11.8](https://developer.nvidia.com/cuda-11-8-0-download-archive). ## How to download the models @@ -62,11 +64,26 @@ Define llama.cpp & exllama models in `model_definitions.py`. You can define all ## How to run server -All required packages will be installed automatically. -Simply run the following command: +All required packages will be installed automatically with this command. ```bash -python -m main --port 8000 +python -m main --install-pkgs +``` + +If you already have all required packages installed, you can skip the installation with this command. +```bash +python -m main +``` +Options: +```b + -h, --help show this help message and exit + --port PORT Port to run the server on; default is 8000 + --max-workers MAX_WORKERS + Maximum number of process workers to run; default is 1 + --install-pkgs Install all required packages before running the server + --force-cuda Force CUDA version of pytorch to be used when installing pytorch. e.g. torch==2.0.1+cu118 + --skip-torch-install Skip installing pytorch, if `install-pkgs` is set + --skip-tf-install Skip installing tensorflow, if `install-pkgs` is set ``` ## Usage: Text Completion diff --git a/requirements-all.txt b/requirements-all.txt new file mode 100644 index 0000000..8e1425d --- /dev/null +++ b/requirements-all.txt @@ -0,0 +1,26 @@ +psutil +fastapi +uvicorn[standard] +transformers +orjson +llama_cpp_python[server] +safetensors==0.3.1 +sentencepiece>=0.1.97 +ninja==1.11.1 +--find-links https://download.pytorch.org/whl/torch_stable.html +torch==2.0.1+cu118 +numpy +scikit-learn +tensorflow>=2.0.0 +tensorflow-hub +scikit-build + + +# Dev +black +twine +flake8 +mkdocs +mkdocstrings +mkdocs-material +httpx \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 456cc75..2c76573 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,16 +1,90 @@ -psutil -fastapi -uvicorn[standard] -transformers -orjson -llama_cpp_python[server] -safetensors==0.3.1 -sentencepiece>=0.1.97 -ninja==1.11.1 ---find-links https://download.pytorch.org/whl/torch_stable.html -torch==2.0.1+cu118 -numpy -scikit-learn -tensorflow>=2.0.0 -tensorflow-hub -scikit-build \ No newline at end of file +annotated-types==0.5.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +anyio==3.7.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +attrs==23.1.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +build==0.10.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +cachecontrol[filecache]==0.12.14 ; python_full_version >= "3.8.1" and python_version < "3.12" +certifi==2023.7.22 ; python_full_version >= "3.8.1" and python_version < "3.12" +cffi==1.15.1 ; python_full_version >= "3.8.1" and python_version < "3.12" and (sys_platform == "darwin" or sys_platform == "linux") +charset-normalizer==3.2.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +cleo==2.0.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +click==8.1.6 ; python_full_version >= "3.8.1" and python_version < "3.12" +cmake==3.27.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +colorama==0.4.6 ; python_full_version >= "3.8.1" and python_version < "3.12" and (os_name == "nt" or platform_system == "Windows") +crashtest==0.4.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +cryptography==41.0.2 ; python_full_version >= "3.8.1" and python_version < "3.12" and sys_platform == "linux" +diskcache==5.6.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +distlib==0.3.7 ; python_full_version >= "3.8.1" and python_version < "3.12" +dulwich==0.21.5 ; python_full_version >= "3.8.1" and python_version < "3.12" +exceptiongroup==1.1.2 ; python_full_version >= "3.8.1" and python_version < "3.11" +fastapi==0.100.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +filelock==3.12.2 ; python_full_version >= "3.8.1" and python_version < "3.12" +fsspec==2023.6.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +h11==0.14.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +html5lib==1.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +httptools==0.6.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +huggingface-hub==0.16.4 ; python_full_version >= "3.8.1" and python_version < "3.12" +idna==3.4 ; python_full_version >= "3.8.1" and python_version < "3.12" +importlib-metadata==6.8.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +importlib-resources==6.0.0 ; python_full_version >= "3.8.1" and python_version < "3.9" +installer==0.7.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +jaraco-classes==3.3.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +jeepney==0.8.0 ; python_full_version >= "3.8.1" and python_version < "3.12" and sys_platform == "linux" +jsonschema-specifications==2023.7.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +jsonschema==4.18.4 ; python_full_version >= "3.8.1" and python_version < "3.12" +keyring==23.13.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +lockfile==0.12.2 ; python_full_version >= "3.8.1" and python_version < "3.12" +more-itertools==10.0.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +msgpack==1.0.5 ; python_full_version >= "3.8.1" and python_version < "3.12" +ninja==1.11.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +numpy==1.24.4 ; python_full_version >= "3.8.1" and python_version < "3.12" +orjson==3.9.2 ; python_full_version >= "3.8.1" and python_version < "3.12" +packaging==23.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +pexpect==4.8.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +pkginfo==1.9.6 ; python_full_version >= "3.8.1" and python_version < "3.12" +pkgutil-resolve-name==1.3.10 ; python_full_version >= "3.8.1" and python_version < "3.9" +platformdirs==3.10.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +poetry-core==1.6.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +poetry-plugin-export==1.4.0 ; python_full_version >= "3.8.1" and python_version < "3.12" 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python_full_version >= "3.8.1" and python_version < "3.12" +rapidfuzz==2.15.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +referencing==0.30.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +regex==2023.6.3 ; python_full_version >= "3.8.1" and python_version < "3.12" +requests-toolbelt==1.0.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +requests==2.31.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +rpds-py==0.9.2 ; python_full_version >= "3.8.1" and python_version < "3.12" +safetensors==0.3.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +secretstorage==3.3.3 ; python_full_version >= "3.8.1" and python_version < "3.12" and sys_platform == "linux" +sentencepiece==0.1.99 ; python_full_version >= "3.8.1" and python_version < "3.12" +shellingham==1.5.0.post1 ; python_full_version >= "3.8.1" and python_version < "3.12" +six==1.16.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +sniffio==1.3.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +sse-starlette==1.6.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +starlette==0.27.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +tensorflow-hub==0.14.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +tokenizers==0.13.3 ; python_full_version >= "3.8.1" and python_version < "3.12" +tomli==2.0.1 ; python_full_version >= "3.8.1" and python_version < "3.11" +tomlkit==0.12.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +tqdm==4.65.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +transformers==4.31.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +trove-classifiers==2023.7.6 ; python_full_version >= "3.8.1" and python_version < "3.12" +typing-extensions==4.7.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +urllib3==1.26.16 ; python_full_version >= "3.8.1" and python_version < "3.12" +uvicorn[standard]==0.23.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +uvloop==0.17.0 ; (sys_platform != "win32" and sys_platform != "cygwin") and platform_python_implementation != "PyPy" and python_full_version >= "3.8.1" and python_version < "3.12" +virtualenv==20.24.2 ; python_full_version >= "3.8.1" and python_version < "3.12" +watchfiles==0.19.0 ; python_full_version >= "3.8.1" and python_version < "3.12" +webencodings==0.5.1 ; python_full_version >= "3.8.1" and python_version < "3.12" +websockets==11.0.3 ; python_full_version >= "3.8.1" and python_version < "3.12" +xattr==0.10.1 ; python_full_version >= "3.8.1" and python_version < "3.12" and sys_platform == "darwin" +zipp==3.16.2 ; python_full_version >= "3.8.1" and python_version < "3.12" diff --git a/tests/conftest.py b/tests/conftest.py index b18ead2..e276030 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,32 +1,45 @@ +import importlib +import unittest +from os import environ from pathlib import Path -import sys -import pytest - - -sys.path.insert(0, Path(__file__).parent.parent.as_posix()) -from llama_api.utils.concurrency import pool # noqa: E402 - - -@pytest.fixture(scope="session") -def app(): - from llama_api.server.app_settings import ( - create_app_llama_cpp, - ) - - try: - yield create_app_llama_cpp() - finally: - pool().shutdown(wait=False) - - -@pytest.fixture(scope="session") -def ppool(): - from llama_api.utils.process_pool import ( - ProcessPool, - ) - - with ProcessPool(max_workers=2) as pool: - for wix in range(pool.max_workers): - pool.worker_at_wix(wix) - - yield pool +from typing import TYPE_CHECKING, Dict, List, Type # noqa: F401 + +from llama_api.server.app_settings import create_app_llama_cpp +from llama_api.shared.config import Config +from llama_api.utils.dependency import install_package, is_package_available +from llama_api.utils.concurrency import _pool + +if TYPE_CHECKING: + from fastapi.testclient import TestClient # noqa: F401 + from httpx import AsyncClient # noqa: F401 + + +class TestLlamaAPI(unittest.TestCase): + ggml_model: str = "orca-mini-3b.ggmlv3.q4_1.bin" + ggml_path: Path = Config.project_root / Path(f"models/ggml/{ggml_model}") + + gptq_model: str = "orca_mini_7b" + gptq_path: Path = Config.project_root / Path(f"models/gptq/{gptq_model}") + + messages: List[Dict[str, str]] = [ + {"role": "user", "content": "Hello, there!"} + ] + prompt: str = "\n".join([f"{m['role']}: {m['content']}" for m in messages]) + + @classmethod + def setUpClass(cls): + if not is_package_available("httpx"): + install_package("httpx") + cls.AsyncClient = importlib.import_module( + "httpx" + ).AsyncClient # type: Type[AsyncClient] + cls.TestClient = importlib.import_module( + "fastapi.testclient" + ).TestClient # type: Type[TestClient] + cls.app = create_app_llama_cpp() + environ["MAX_WORKERS"] = "2" + + @classmethod + def tearDownClass(cls): + if _pool is not None: + _pool.shutdown(wait=True) diff --git a/tests/test_process_pool.py b/tests/test_process_pool.py index 470980a..41416b1 100644 --- a/tests/test_process_pool.py +++ b/tests/test_process_pool.py @@ -1,157 +1,74 @@ -from asyncio import gather -from os import environ, getpid -import pytest from concurrent.futures import Future +from contextlib import contextmanager from functools import partial -import sys -from pathlib import Path +from os import getpid from time import sleep, time - -sys.path.insert(0, Path(__file__).parents[1].as_posix()) -environ["MAX_WORKERS"] = "2" -from llama_api.utils.concurrency import ( # noqa: E402 - ProcessPool, - awake_all_pool_workers, - pool, - run_in_processpool, - run_in_processpool_with_wix, -) - - -def simple_job(sleep_time: float) -> float: - print("> Input sleep time:", sleep_time, "PID:", getpid()) - sleep(sleep_time) - print("> Job done at", time(), "PID:", getpid()) - return sleep_time - - -def test_process_pool(ppool) -> None: - # We're recording the start time - start_time = time() - - # Submitting two jobs which will sleep for 1 second each - f1: Future = ppool.submit(simple_job, 1) - f2: Future = ppool.submit(simple_job, 1) - print("Submitted jobs at", time()) - - # Waiting for both jobs to complete - result1 = f1.result() # This will block until f1 is done - result2 = f2.result() # This will block until f2 is done - print("Jobs done:", result1, result2) - - # Recording the end time - end_time = time() - print("Elapsed time:", end_time - start_time) - - # Total elapsed time should be little more than 1 second, - # not 2 seconds, because jobs are expected to run in parallel. - assert end_time - start_time <= 2.0, f"{end_time - start_time}" - assert result1 == result2 == 1 - - -def test_process_pool_with_wix(ppool) -> None: - # We're recording the start time +from typing import Tuple + +from llama_api.utils.process_pool import ProcessPool +from tests.conftest import TestLlamaAPI + + +@contextmanager +def process_pool(max_workers: int): + with ProcessPool(max_workers=max_workers) as executor: + alive_workers = [False] * max_workers + while not all(alive_workers): + for wix in range(executor.max_workers): + # This will run in the worker + # at the specified worker-index(wix). + # We're just checking if the worker is alive. + alive_workers[wix] = executor.worker_at_wix(wix).is_alive + print("- Waiting for workers to start...", alive_workers) + sleep(0.25) # Wait for the pool to start + print("- Workers started.") + yield executor + + +def simple_job(sleep_time: float) -> Tuple[float, float]: + """A simple job that sleeps for a given time + and returns the start and end times.""" start_time = time() - - # Submitting two jobs which will sleep for 1 second each - f1: Future = ppool.submit_with_wix(partial(simple_job, 1), wix=1) - f2: Future = ppool.submit_with_wix(partial(simple_job, 1), wix=1) - print("Submitted jobs") - - # Waiting for both jobs to complete - result1 = f1.result() # This will block until f1 is done - result2 = f2.result() # This will block until f2 is done - print("Jobs done:", result1, result2) - - # Recording the end time + print("> Starting at:", start_time, "PID:", getpid()) + sleep(sleep_time) end_time = time() - print("Elapsed time:", end_time - start_time) - - # Total elapsed time should be little more than 2 seconds, - # not 1 second, because jobs are expected to run in serial. - assert end_time - start_time >= 2.0, f"{end_time - start_time}" - assert result1 == result2 == 1 - - -@pytest.mark.asyncio -async def test_process_pool_async(): - try: - awake_all_pool_workers() - sleep(2) # Give the pool some time to start up - - start_time = time() - print("Submitting jobs...") - result_1, result_2 = await gather( - run_in_processpool(simple_job, 1), - run_in_processpool(simple_job, 1), # type: ignore - ) - end_time = time() - print("Elapsed time:", end_time - start_time) - - # Total elapsed time should be little more than 1 second, - # not 2 seconds, because jobs are expected to run in parallel. - assert end_time - start_time <= 2.0, f"{end_time - start_time}" - assert result_1 == result_2 == 1 - finally: - _pool = pool() - _pool.join() - - -@pytest.mark.asyncio -async def test_process_pool_with_wix_async(): - try: - awake_all_pool_workers() - sleep(2) # Give the pool some time to start up - - start_time = time() - print("Submitting jobs...") - result_1, result_2 = await gather( - run_in_processpool_with_wix(partial(simple_job, 1), wix=1), - run_in_processpool_with_wix(partial(simple_job, 1), wix=1), - ) - end_time = time() - print("Elapsed time:", end_time - start_time) - - # Total elapsed time should be little more than 2 seconds, - # not 1 second, because jobs are expected to run in serial. - assert end_time - start_time >= 2.0, f"{end_time - start_time}" - assert result_1 == result_2 == 1 - finally: - _pool = pool() - _pool.join() - - -def test_process_pool_graceful_shutdown(): - timeout = None - with ProcessPool(max_workers=2) as executor: - f1 = executor.submit(simple_job, 1) - f2 = executor.submit(simple_job, 1) - print("Submitted jobs") - - # Wait for the jobs to start - result1 = f1.result(timeout=timeout) - result2 = f2.result(timeout=timeout) - print("Jobs done:", result1, result2) - - -def test_process_pool_map(): - timeout = 3.0 - with ProcessPool(max_workers=4) as executor: - results = iter( - executor.map( - simple_job, - [1, 1, 1, 1], - chunksize=2, - timeout=timeout, - ) - ) - print("Submitted jobs") - - # Wait for the jobs to start - result1 = next(results) - result2 = next(results) - print("Jobs done:", result1, result2) - - -if __name__ == "__main__": - test_process_pool_map() + print("> Ending at", end_time, "PID:", getpid()) + return start_time, end_time + + +class TestProcessPool(TestLlamaAPI): + """Test that the process pool works as expected.""" + + def test_process_pool(self) -> None: + """Test the basic functionality of the process pool.""" + # We're recording the start time + with process_pool(max_workers=2) as executor: + # Submitting two jobs which will sleep for 1 second each + f1: Future = executor.submit(simple_job, 1) + f2: Future = executor.submit(simple_job, 1) + print("Submitted jobs at", time()) + + # Waiting for both jobs to complete + _, e1 = f1.result() # This will block until f1 is done + s2, _ = f2.result() # This will block until f2 is done + + # Assert that the second job started before the first job ended + self.assertLess(s2, e1) + + def test_process_pool_with_wix(self) -> None: + """Test the worker-index-based scheduling functionality + of the process pool.""" + # We're recording the start time + + with process_pool(max_workers=2) as executor: + # Submitting two jobs which will sleep for 1 second each + f1: Future = executor.submit_with_wix(partial(simple_job, 1), wix=0) + f2: Future = executor.submit_with_wix(partial(simple_job, 1), wix=0) + print("Submitted jobs at", time()) + + # Waiting for both jobs to complete + _, e1 = f1.result() # This will block until f1 is done + s2, _ = f2.result() # This will block until f2 is done + + # Assert that the second job started before the first job ended + self.assertGreater(s2, e1) diff --git a/tests/test_server.py b/tests/test_server.py index a649269..8255b4a 100644 --- a/tests/test_server.py +++ b/tests/test_server.py @@ -1,19 +1,18 @@ import re from asyncio import gather -from pathlib import Path from typing import ( - TYPE_CHECKING, Awaitable, + Dict, Iterable, List, Literal, Optional, Tuple, + Union, ) -from fastapi.testclient import TestClient - -import pytest -from httpx import AsyncClient, Response +import unittest +from llama_api.utils.system import get_cuda_version +from tests.conftest import TestLlamaAPI from llama_api.schemas.api import ( ModelList, @@ -21,190 +20,256 @@ CompletionChoice, ) -if TYPE_CHECKING: - from fastapi import FastAPI EndPoint = Literal["completions", "chat/completions"] -GGML_MODEL: str = "orca-mini-3b.ggmlv3.q4_1.bin" -GGML_PATH: Path = Path(__file__).parents[1] / Path(f"models/ggml/{GGML_MODEL}") -GPTQ_MODEL: str = "orca_mini_7b" -GPTQ_PATH: Path = Path(__file__).parents[1] / Path(f"models/gptq/{GPTQ_MODEL}") +class TestServer(TestLlamaAPI, unittest.IsolatedAsyncioTestCase): + """Test the FastAPI server + with basic health checks & LLM completion requests (with concurrency)""" -MESSAGES = [{"role": "user", "content": "Hello, there!"}] -PROMPT = "\n".join([f"{m['role']}: {m['content']}" for m in MESSAGES]) + def test_health(self): + """Test the health endpoint""" + with self.TestClient(app=self.app) as client: + response = client.get( + "/health", + headers={"Content-Type": "application/json"}, + ) + self.assertEqual(response.status_code, 200) + def test_import_llama_cpp(self): + try: + from llama_api.modules.llama_cpp import ( # noqa: F401 + LlamaCppCompletionGenerator, + ) + except ImportError as e: + self.fail(f"Failed to import module: {e}") -async def _arequest_completion( - app: "FastAPI", - model_names: List[str] | Tuple[str, ...], - endpoints: EndPoint | Iterable[EndPoint], -): - _endpoints: Iterable[str] = ( - [endpoints] * len(model_names) - if isinstance(endpoints, str) - else endpoints + @unittest.skipIf( + get_cuda_version() is None, + reason="No CUDA found on this system", ) - async with AsyncClient( - app=app, base_url="http://localhost", timeout=None - ) as client: - # Get models using the API - model_resp: ModelList = (await client.get("/v1/models")).json() - models: list[str] = [] - for model_name in model_names: - model: Optional[str] = None - for model_data in model_resp["data"]: - if model_name in model_data["id"]: - model = re.sub(r"\(.*\)", "", model_data["id"]).strip() - break - assert model, f"Model {model_name} not found" - models.append(model) - - # Submit requests to the API - tasks: list[Awaitable] = [] - for model, endpoint in zip(models, _endpoints): - request = {"model": model, "max_tokens": 50} - request_message = ( - {"messages": MESSAGES} - if endpoint.startswith("chat") - else {"prompt": PROMPT} + def test_import_exllama(self): + try: + from llama_api.modules.exllama import ( # noqa: F401 + ExllamaCompletionGenerator, ) - tasks.append( - client.post( - f"/v1/{endpoint}", - json=request | {"stream": False} | request_message, - headers={"Content-Type": "application/json"}, - timeout=None, - ) + except ImportError as e: + self.fail(f"Failed to import module: {e}") + + def test_import_sentence_encoder(self): + try: + from llama_api.modules.sentence_encoder import ( # noqa: F401 + SentenceEncoderEmbeddingGenerator, ) + except ImportError as e: + self.fail(f"Failed to import module: {e}") - # Wait for responses - cmpl_resps: list[Response] = await gather(*tasks) - results: list[str] = [] - for model, cmpl_resp in zip(models, cmpl_resps): - assert cmpl_resp.status_code == 200 - choice: CompletionChoice | ChatCompletionChoice = cmpl_resp.json()[ - "choices" - ][0] - if "message" in choice: - results.append(choice["message"]["content"]) # type: ignore - elif "text" in choice: - results.append(choice["text"]) - else: - raise ValueError(f"Unknown response: {cmpl_resp.json()}") - print(f"Result of {model}:", results[-1], end="\n\n", flush=True) - - assert len(results) == len(models) - - -def _request_completion( - app: "FastAPI", - model_names: List[str] | Tuple[str, ...], - endpoints: EndPoint | Iterable[EndPoint], -): - _endpoints: Iterable[str] = ( - [endpoints] * len(model_names) - if isinstance(endpoints, str) - else endpoints - ) - with TestClient(app=app) as client: - # Get models using the API - model_resp = (client.get("/v1/models")).json() - models: list[str] = [] - for model_name in model_names: - model: Optional[str] = None - for model_data in model_resp["data"]: - if model_name in model_data["id"]: - model = re.sub(r"\(.*\)", "", model_data["id"]).strip() - break - assert model, f"Model {model_name} not found" - models.append(model) - - # Submit requests to the API - cmpl_resps: list[Response] = [] - for model, endpoint in zip(models, _endpoints): - request = {"model": model, "max_tokens": 50} - request_message = ( - {"messages": MESSAGES} - if endpoint.startswith("chat") - else {"prompt": PROMPT} + def test_import_transformer(self): + try: + from llama_api.modules.transformer import ( # noqa: F401 + TransformerEmbeddingGenerator, ) - cmpl_resps.append( - client.post( - f"/v1/{endpoint}", - json=request | {"stream": False} | request_message, - headers={"Content-Type": "application/json"}, - timeout=None, - ) + except ImportError as e: + self.fail(f"Failed to import module: {e}") + + def test_v1_models(self): + """Test the v1/models endpoint""" + with self.TestClient(app=self.app) as client: + response = client.get( + "/v1/models", + headers={"Content-Type": "application/json"}, ) + self.assertEqual(response.status_code, 200) - # Wait for responses - results: list[str] = [] - for model, cmpl_resp in zip(models, cmpl_resps): - assert cmpl_resp.status_code == 200 - choice: CompletionChoice | ChatCompletionChoice = cmpl_resp.json()[ - "choices" - ][0] - if "message" in choice: - results.append(choice["message"]["content"]) # type: ignore - elif "text" in choice: - results.append(choice["text"]) - else: - raise ValueError(f"Unknown response: {cmpl_resp.json()}") - print(f"Result of {model}:", results[-1], end="\n\n", flush=True) - - assert len(results) == len(models) - - -def test_health(app): - with TestClient(app=app) as client: - response = client.get( - "/health", - headers={"Content-Type": "application/json"}, + @unittest.skipIf( + not TestLlamaAPI.ggml_path.exists(), + reason=f"No model in {TestLlamaAPI.ggml_path}", + ) + def test_llama_cpp(self): + """Test the Llama CPP model completion endpoints""" + self._request_completion( + model_names=(self.ggml_model,), endpoints="completions" + ) + self._request_completion( + model_names=(self.ggml_model,), endpoints="chat/completions" ) - assert response.status_code == 200 - -@pytest.mark.skipif(not GGML_PATH.exists(), reason=f"No model in {GGML_PATH}") -def test_llama_cpp(app): - _request_completion( - app, model_names=(GGML_MODEL,), endpoints="completions" - ) - _request_completion( - app, model_names=(GGML_MODEL,), endpoints="chat/completions" + @unittest.skipIf( + not TestLlamaAPI.gptq_path.exists(), + reason=f"No model in{TestLlamaAPI.gptq_path}", ) + def test_exllama(self): + """Test the ExLLama model completion endpoints""" + self._request_completion( + model_names=(self.gptq_model,), endpoints="completions" + ) + self._request_completion( + model_names=(self.gptq_model,), endpoints="chat/completions" + ) + @unittest.skipIf( + not TestLlamaAPI.ggml_path.exists(), + reason=f"No model in {TestLlamaAPI.ggml_path}", + ) + async def test_llama_cpp_concurrency(self): + """Test the Llama CPP model completion endpoints with concurrency""" + model_names: Tuple[str, ...] = (self.ggml_model, self.ggml_model) + await self._arequest_completion( + model_names=model_names, endpoints="completions" + ) -@pytest.mark.skipif(not GPTQ_PATH.exists(), reason=f"No model in{GPTQ_PATH}") -def test_exllama(app): - _request_completion( - app, model_names=(GPTQ_MODEL,), endpoints="completions" + @unittest.skipIf( + not TestLlamaAPI.gptq_path.exists(), + reason=f"No model in {TestLlamaAPI.gptq_path}", ) - _request_completion( - app, model_names=(GPTQ_MODEL,), endpoints="chat/completions" + async def test_exllama_concurrency(self): + """Test the ExLLama model completion endpoints with concurrency""" + model_names: Tuple[str, ...] = (self.gptq_model, self.gptq_model) + await self._arequest_completion( + model_names=model_names, endpoints="completions" + ) + + @unittest.skipIf( + (not TestLlamaAPI.ggml_path.exists()) + or (not TestLlamaAPI.gptq_path.exists()), + f"No model in {TestLlamaAPI.ggml_path} or {TestLlamaAPI.gptq_path}", ) + async def test_llama_mixed_concurrency(self): + """Test the Llama CPP & ExLLama model completion endpoints + with concurrency""" + model_names: Tuple[str, ...] = (self.ggml_model, self.gptq_model) + await self._arequest_completion( + model_names=model_names, endpoints="completions" + ) + async def _arequest_completion( + self, + model_names: Union[List[str], Tuple[str, ...]], + endpoints: Union[EndPoint, Iterable[EndPoint]], + ): + _endpoints: Iterable[str] = ( + [endpoints] * len(model_names) + if isinstance(endpoints, str) + else endpoints + ) + async with self.AsyncClient( + app=self.app, base_url="http://localhost", timeout=None + ) as client: + # Get models using the API + model_resp: ModelList = (await client.get("/v1/models")).json() + models: List[str] = [] + for model_name in model_names: + model: Optional[str] = None + for model_data in model_resp["data"]: + if model_name in model_data["id"]: + model = re.sub(r"\(.*\)", "", model_data["id"]).strip() + break + self.assertTrue(model, f"Model {model_name} not found") + models.append(str(model)) -@pytest.mark.asyncio -async def test_llama_cpp_concurrency(app): - model_names: Tuple[str, ...] = (GGML_MODEL, GGML_MODEL) - await _arequest_completion( - app, model_names=model_names, endpoints="completions" - ) + # Submit requests to the API + tasks: List[Awaitable] = [] + for model, endpoint in zip(models, _endpoints): + request = {"model": model, "max_tokens": 50} + request_message = ( + {"messages": self.messages} + if endpoint.startswith("chat") + else {"prompt": self.prompt} + ) + tasks.append( + client.post( + f"/v1/{endpoint}", + json=_union( + request, {"stream": False}, request_message + ), + headers={"Content-Type": "application/json"}, + timeout=None, + ) + ) + # Wait for responses + cmpl_resps: List = await gather(*tasks) + results: List[str] = [] + for model, cmpl_resp in zip(models, cmpl_resps): + self.assertEqual(cmpl_resp.status_code, 200) + choice: Union[ + CompletionChoice, ChatCompletionChoice + ] = cmpl_resp.json()["choices"][0] + if "message" in choice: + results.append(choice["message"]["content"]) + elif "text" in choice: + results.append(choice["text"]) + else: + raise ValueError(f"Unknown response: {cmpl_resp.json()}") + print( + f"Result of {model}:", results[-1], end="\n\n", flush=True + ) -@pytest.mark.asyncio -async def test_exllama_concurrency(app): - model_names: Tuple[str, ...] = (GPTQ_MODEL, GPTQ_MODEL) - await _arequest_completion( - app, model_names=model_names, endpoints="completions" - ) + self.assertEqual(len(results), len(models)) + def _request_completion( + self, + model_names: Union[List[str], Tuple[str, ...]], + endpoints: Union[EndPoint, Iterable[EndPoint]], + ): + _endpoints: Iterable[str] = ( + [endpoints] * len(model_names) + if isinstance(endpoints, str) + else endpoints + ) + with self.TestClient(app=self.app) as client: + # Get models using the API + model_resp = (client.get("/v1/models")).json() + models: List[str] = [] + for model_name in model_names: + model: Optional[str] = None + for model_data in model_resp["data"]: + if model_name in model_data["id"]: + model = re.sub(r"\(.*\)", "", model_data["id"]).strip() + break + self.assertTrue(model, f"Model {model_name} not found") + models.append(str(model)) -@pytest.mark.asyncio -async def test_llama_mixed_concurrency(app): - model_names: Tuple[str, ...] = (GGML_MODEL, GPTQ_MODEL) - await _arequest_completion( - app, model_names=model_names, endpoints="completions" - ) + # Submit requests to the API + cmpl_resps: List = [] + for model, endpoint in zip(models, _endpoints): + request = {"model": model, "max_tokens": 50} + request_message = ( + {"messages": self.messages} + if endpoint.startswith("chat") + else {"prompt": self.prompt} + ) + cmpl_resps.append( + client.post( + f"/v1/{endpoint}", + json=_union( + request, {"stream": False}, request_message + ), + headers={"Content-Type": "application/json"}, + timeout=None, + ) + ) + + # Wait for responses + results: List[str] = [] + for model, cmpl_resp in zip(models, cmpl_resps): + self.assertEqual(cmpl_resp.status_code, 200) + choice: Union[ + CompletionChoice, ChatCompletionChoice + ] = cmpl_resp.json()["choices"][0] + if "message" in choice: + results.append(choice["message"]["content"]) + elif "text" in choice: + results.append(choice["text"]) + else: + raise ValueError(f"Unknown response: {cmpl_resp.json()}") + print( + f"Result of {model}:", results[-1], end="\n\n", flush=True + ) + + self.assertEqual(len(results), len(models)) + + +def _union(*dicts: Dict) -> Dict: + return {k: v for d in dicts for k, v in d.items()}