616 lines
20 KiB
Python
616 lines
20 KiB
Python
import asyncio
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import contextlib
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import json
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import math
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import os
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import random
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import shutil
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import subprocess
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import sys
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import tempfile
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import time
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from typing import Dict, List, Optional
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import docker
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import pytest
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from aiohttp import ClientConnectorError, ClientOSError, ServerDisconnectedError
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from docker.errors import NotFound
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from syrupy.extensions.json import JSONSnapshotExtension
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from text_generation import AsyncClient
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from text_generation.types import (
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BestOfSequence,
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Message,
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ChatComplete,
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ChatCompletionChunk,
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ChatCompletionComplete,
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Completion,
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Details,
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Grammar,
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InputToken,
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Response,
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Token,
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)
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DOCKER_IMAGE = os.getenv("DOCKER_IMAGE", None)
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HF_TOKEN = os.getenv("HF_TOKEN", None)
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DOCKER_VOLUME = os.getenv("DOCKER_VOLUME", "/data")
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DOCKER_DEVICES = os.getenv("DOCKER_DEVICES")
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def pytest_addoption(parser):
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parser.addoption(
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"--release", action="store_true", default=False, help="run release tests"
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)
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def pytest_configure(config):
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config.addinivalue_line("markers", "release: mark test as a release-only test")
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def pytest_collection_modifyitems(config, items):
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if config.getoption("--release"):
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# --release given in cli: do not skip release tests
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return
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skip_release = pytest.mark.skip(reason="need --release option to run")
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for item in items:
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if "release" in item.keywords:
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item.add_marker(skip_release)
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class ResponseComparator(JSONSnapshotExtension):
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rtol = 0.2
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ignore_logprob = False
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def serialize(
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self,
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data,
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*,
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include=None,
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exclude=None,
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matcher=None,
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):
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if (
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isinstance(data, Response)
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or isinstance(data, ChatComplete)
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or isinstance(data, ChatCompletionChunk)
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or isinstance(data, ChatCompletionComplete)
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):
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data = data.model_dump()
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if isinstance(data, List):
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data = [d.model_dump() for d in data]
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data = self._filter(
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data=data,
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depth=0,
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path=(),
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exclude=exclude,
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include=include,
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matcher=matcher,
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)
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return json.dumps(data, indent=2, ensure_ascii=False, sort_keys=False) + "\n"
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def matches(
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self,
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*,
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serialized_data,
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snapshot_data,
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) -> bool:
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def convert_data(data):
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data = json.loads(data)
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return _convert_data(data)
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def _convert_data(data):
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if isinstance(data, Dict):
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if "choices" in data:
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data["choices"] = list(
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sorted(data["choices"], key=lambda x: x["index"])
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)
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choices = data["choices"]
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if isinstance(choices, List) and len(choices) >= 1:
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if "delta" in choices[0]:
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return ChatCompletionChunk(**data)
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if "text" in choices[0]:
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return Completion(**data)
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return ChatComplete(**data)
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else:
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return Response(**data)
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if isinstance(data, List):
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return [_convert_data(d) for d in data]
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raise NotImplementedError(f"Type {type(data)} is not supported")
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def eq_token(token: Token, other: Token) -> bool:
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return (
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token.id == other.id
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and token.text == other.text
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and (
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self.ignore_logprob
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or (token.logprob == other.logprob and token.logprob is None)
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or math.isclose(token.logprob, other.logprob, rel_tol=self.rtol)
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)
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and token.special == other.special
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)
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def eq_prefill_token(prefill_token: InputToken, other: InputToken) -> bool:
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try:
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return (
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prefill_token.id == other.id
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and prefill_token.text == other.text
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and (
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self.ignore_logprob
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or math.isclose(
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prefill_token.logprob,
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other.logprob,
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rel_tol=self.rtol,
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)
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if prefill_token.logprob is not None
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else prefill_token.logprob == other.logprob
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)
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)
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except TypeError:
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return False
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def eq_best_of(details: BestOfSequence, other: BestOfSequence) -> bool:
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return (
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details.finish_reason == other.finish_reason
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and details.generated_tokens == other.generated_tokens
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and details.seed == other.seed
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and len(details.prefill) == len(other.prefill)
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and all(
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[
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eq_prefill_token(d, o)
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for d, o in zip(details.prefill, other.prefill)
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]
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)
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and len(details.tokens) == len(other.tokens)
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and all([eq_token(d, o) for d, o in zip(details.tokens, other.tokens)])
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)
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def eq_details(details: Details, other: Details) -> bool:
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return (
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details.finish_reason == other.finish_reason
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and details.generated_tokens == other.generated_tokens
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and details.seed == other.seed
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and len(details.prefill) == len(other.prefill)
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and all(
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[
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eq_prefill_token(d, o)
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for d, o in zip(details.prefill, other.prefill)
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]
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)
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and len(details.tokens) == len(other.tokens)
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and all([eq_token(d, o) for d, o in zip(details.tokens, other.tokens)])
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and (
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len(details.best_of_sequences)
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if details.best_of_sequences is not None
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else 0
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)
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== (
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len(other.best_of_sequences)
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if other.best_of_sequences is not None
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else 0
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)
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and (
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all(
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[
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eq_best_of(d, o)
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for d, o in zip(
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details.best_of_sequences, other.best_of_sequences
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)
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]
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)
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if details.best_of_sequences is not None
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else details.best_of_sequences == other.best_of_sequences
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)
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)
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def eq_completion(response: Completion, other: Completion) -> bool:
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return response.choices[0].text == other.choices[0].text
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def eq_chat_complete(response: ChatComplete, other: ChatComplete) -> bool:
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return (
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response.choices[0].message.content == other.choices[0].message.content
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)
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def eq_chat_complete_chunk(
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response: ChatCompletionChunk, other: ChatCompletionChunk
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) -> bool:
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return response.choices[0].delta.content == other.choices[0].delta.content
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def eq_response(response: Response, other: Response) -> bool:
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return response.generated_text == other.generated_text and eq_details(
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response.details, other.details
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)
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serialized_data = convert_data(serialized_data)
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snapshot_data = convert_data(snapshot_data)
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if not isinstance(serialized_data, List):
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serialized_data = [serialized_data]
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if not isinstance(snapshot_data, List):
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snapshot_data = [snapshot_data]
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if isinstance(serialized_data[0], Completion):
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return len(snapshot_data) == len(serialized_data) and all(
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[eq_completion(r, o) for r, o in zip(serialized_data, snapshot_data)]
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)
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if isinstance(serialized_data[0], ChatComplete):
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return len(snapshot_data) == len(serialized_data) and all(
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[eq_chat_complete(r, o) for r, o in zip(serialized_data, snapshot_data)]
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)
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if isinstance(serialized_data[0], ChatCompletionChunk):
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return len(snapshot_data) == len(serialized_data) and all(
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[
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eq_chat_complete_chunk(r, o)
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for r, o in zip(serialized_data, snapshot_data)
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]
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)
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return len(snapshot_data) == len(serialized_data) and all(
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[eq_response(r, o) for r, o in zip(serialized_data, snapshot_data)]
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)
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class GenerousResponseComparator(ResponseComparator):
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# Needed for GPTQ with exllama which has serious numerical fluctuations.
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rtol = 0.75
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class IgnoreLogProbResponseComparator(ResponseComparator):
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ignore_logprob = True
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class LauncherHandle:
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def __init__(self, port: int):
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self.client = AsyncClient(f"http://localhost:{port}", timeout=30)
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def _inner_health(self):
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raise NotImplementedError
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async def health(self, timeout: int = 60):
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assert timeout > 0
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for _ in range(timeout):
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if not self._inner_health():
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raise RuntimeError("Launcher crashed")
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try:
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await self.client.generate("test")
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return
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except (ClientConnectorError, ClientOSError, ServerDisconnectedError):
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time.sleep(1)
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raise RuntimeError("Health check failed")
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class ContainerLauncherHandle(LauncherHandle):
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def __init__(self, docker_client, container_name, port: int):
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super(ContainerLauncherHandle, self).__init__(port)
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self.docker_client = docker_client
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self.container_name = container_name
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def _inner_health(self) -> bool:
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container = self.docker_client.containers.get(self.container_name)
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return container.status in ["running", "created"]
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class ProcessLauncherHandle(LauncherHandle):
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def __init__(self, process, port: int):
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super(ProcessLauncherHandle, self).__init__(port)
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self.process = process
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def _inner_health(self) -> bool:
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return self.process.poll() is None
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@pytest.fixture
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def response_snapshot(snapshot):
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return snapshot.use_extension(ResponseComparator)
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@pytest.fixture
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def generous_response_snapshot(snapshot):
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return snapshot.use_extension(GenerousResponseComparator)
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@pytest.fixture
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def ignore_logprob_response_snapshot(snapshot):
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return snapshot.use_extension(IgnoreLogProbResponseComparator)
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@pytest.fixture(scope="module")
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def event_loop():
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loop = asyncio.get_event_loop()
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yield loop
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loop.close()
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@pytest.fixture(scope="module")
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def launcher(event_loop):
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@contextlib.contextmanager
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def local_launcher(
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model_id: str,
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num_shard: Optional[int] = None,
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quantize: Optional[str] = None,
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trust_remote_code: bool = False,
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use_flash_attention: bool = True,
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disable_grammar_support: bool = False,
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dtype: Optional[str] = None,
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revision: Optional[str] = None,
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max_input_length: Optional[int] = None,
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max_batch_prefill_tokens: Optional[int] = None,
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max_total_tokens: Optional[int] = None,
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lora_adapters: Optional[List[str]] = None,
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cuda_graphs: Optional[List[int]] = None,
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attention: Optional[str] = None,
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):
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port = random.randint(8000, 10_000)
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master_port = random.randint(10_000, 20_000)
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shard_uds_path = (
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f"/tmp/tgi-tests-{model_id.split('/')[-1]}-{num_shard}-{quantize}-server"
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)
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args = [
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"text-generation-launcher",
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"--model-id",
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model_id,
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"--port",
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str(port),
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"--master-port",
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str(master_port),
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"--shard-uds-path",
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shard_uds_path,
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]
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env = os.environ
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if disable_grammar_support:
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args.append("--disable-grammar-support")
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if num_shard is not None:
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args.extend(["--num-shard", str(num_shard)])
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if quantize is not None:
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args.append("--quantize")
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args.append(quantize)
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if dtype is not None:
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args.append("--dtype")
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args.append(dtype)
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if revision is not None:
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args.append("--revision")
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args.append(revision)
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if trust_remote_code:
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args.append("--trust-remote-code")
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if max_input_length:
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args.append("--max-input-length")
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args.append(str(max_input_length))
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if max_batch_prefill_tokens:
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args.append("--max-batch-prefill-tokens")
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args.append(str(max_batch_prefill_tokens))
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if max_total_tokens:
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args.append("--max-total-tokens")
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args.append(str(max_total_tokens))
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if lora_adapters:
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args.append("--lora-adapters")
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args.append(",".join(lora_adapters))
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if cuda_graphs:
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args.append("--cuda-graphs")
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args.append(",".join(map(str, cuda_graphs)))
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print(" ".join(args), file=sys.stderr)
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env["LOG_LEVEL"] = "info,text_generation_router=debug"
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if not use_flash_attention:
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env["USE_FLASH_ATTENTION"] = "false"
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if attention is not None:
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env["ATTENTION"] = attention
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with tempfile.TemporaryFile("w+") as tmp:
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# We'll output stdout/stderr to a temporary file. Using a pipe
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# cause the process to block until stdout is read.
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with subprocess.Popen(
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args,
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stdout=tmp,
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stderr=subprocess.STDOUT,
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env=env,
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) as process:
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yield ProcessLauncherHandle(process, port)
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process.terminate()
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process.wait(60)
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tmp.seek(0)
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shutil.copyfileobj(tmp, sys.stderr)
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if not use_flash_attention:
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del env["USE_FLASH_ATTENTION"]
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@contextlib.contextmanager
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def docker_launcher(
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model_id: str,
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num_shard: Optional[int] = None,
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quantize: Optional[str] = None,
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trust_remote_code: bool = False,
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use_flash_attention: bool = True,
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disable_grammar_support: bool = False,
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dtype: Optional[str] = None,
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revision: Optional[str] = None,
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max_input_length: Optional[int] = None,
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max_batch_prefill_tokens: Optional[int] = None,
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max_total_tokens: Optional[int] = None,
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lora_adapters: Optional[List[str]] = None,
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cuda_graphs: Optional[List[int]] = None,
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attention: Optional[str] = None,
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):
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port = random.randint(8000, 10_000)
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args = ["--model-id", model_id, "--env"]
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if disable_grammar_support:
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args.append("--disable-grammar-support")
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if num_shard is not None:
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args.extend(["--num-shard", str(num_shard)])
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if quantize is not None:
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args.append("--quantize")
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args.append(quantize)
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if dtype is not None:
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args.append("--dtype")
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args.append(dtype)
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if revision is not None:
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args.append("--revision")
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args.append(revision)
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if trust_remote_code:
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args.append("--trust-remote-code")
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if max_input_length:
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args.append("--max-input-length")
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args.append(str(max_input_length))
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if max_batch_prefill_tokens:
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args.append("--max-batch-prefill-tokens")
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args.append(str(max_batch_prefill_tokens))
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if max_total_tokens:
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args.append("--max-total-tokens")
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args.append(str(max_total_tokens))
|
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if lora_adapters:
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args.append("--lora-adapters")
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args.append(",".join(lora_adapters))
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if cuda_graphs:
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args.append("--cuda-graphs")
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args.append(",".join(map(str, cuda_graphs)))
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client = docker.from_env()
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container_name = f"tgi-tests-{model_id.split('/')[-1]}-{num_shard}-{quantize}"
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|
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try:
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container = client.containers.get(container_name)
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container.stop()
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container.wait()
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except NotFound:
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pass
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gpu_count = num_shard if num_shard is not None else 1
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|
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env = {
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"LOG_LEVEL": "info,text_generation_router=debug",
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}
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if not use_flash_attention:
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env["USE_FLASH_ATTENTION"] = "false"
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if attention is not None:
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env["ATTENTION"] = attention
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if HF_TOKEN is not None:
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env["HF_TOKEN"] = HF_TOKEN
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|
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volumes = []
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if DOCKER_VOLUME:
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volumes = [f"{DOCKER_VOLUME}:/data"]
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if DOCKER_DEVICES:
|
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devices = DOCKER_DEVICES.split(",")
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visible = os.getenv("ROCR_VISIBLE_DEVICES")
|
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if visible:
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env["ROCR_VISIBLE_DEVICES"] = visible
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device_requests = []
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else:
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devices = []
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device_requests = [
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docker.types.DeviceRequest(count=gpu_count, capabilities=[["gpu"]])
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]
|
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container = client.containers.run(
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DOCKER_IMAGE,
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command=args,
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name=container_name,
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environment=env,
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auto_remove=False,
|
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detach=True,
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device_requests=device_requests,
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devices=devices,
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volumes=volumes,
|
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ports={"80/tcp": port},
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healthcheck={"timeout": int(10 * 1e9)},
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shm_size="1G",
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)
|
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yield ContainerLauncherHandle(client, container.name, port)
|
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|
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if not use_flash_attention:
|
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del env["USE_FLASH_ATTENTION"]
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try:
|
|
container.stop()
|
|
container.wait()
|
|
except NotFound:
|
|
pass
|
|
|
|
container_output = container.logs().decode("utf-8")
|
|
print(container_output, file=sys.stderr)
|
|
|
|
container.remove()
|
|
|
|
if DOCKER_IMAGE is not None:
|
|
return docker_launcher
|
|
return local_launcher
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def generate_load():
|
|
async def generate_load_inner(
|
|
client: AsyncClient,
|
|
prompt: str,
|
|
max_new_tokens: int,
|
|
n: int,
|
|
seed: Optional[int] = None,
|
|
grammar: Optional[Grammar] = None,
|
|
stop_sequences: Optional[List[str]] = None,
|
|
) -> List[Response]:
|
|
futures = [
|
|
client.generate(
|
|
prompt,
|
|
max_new_tokens=max_new_tokens,
|
|
decoder_input_details=True,
|
|
seed=seed,
|
|
grammar=grammar,
|
|
stop_sequences=stop_sequences,
|
|
)
|
|
for _ in range(n)
|
|
]
|
|
|
|
return await asyncio.gather(*futures)
|
|
|
|
return generate_load_inner
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def generate_multi():
|
|
async def generate_load_inner(
|
|
client: AsyncClient,
|
|
prompts: List[str],
|
|
max_new_tokens: int,
|
|
seed: Optional[int] = None,
|
|
) -> List[Response]:
|
|
import numpy as np
|
|
|
|
arange = np.arange(len(prompts))
|
|
perm = np.random.permutation(arange)
|
|
rperm = [-1] * len(perm)
|
|
for i, p in enumerate(perm):
|
|
rperm[p] = i
|
|
|
|
shuffled_prompts = [prompts[p] for p in perm]
|
|
futures = [
|
|
client.chat(
|
|
messages=[Message(role="user", content=prompt)],
|
|
max_tokens=max_new_tokens,
|
|
temperature=0,
|
|
seed=seed,
|
|
)
|
|
for prompt in shuffled_prompts
|
|
]
|
|
|
|
shuffled_responses = await asyncio.gather(*futures)
|
|
responses = [shuffled_responses[p] for p in rperm]
|
|
return responses
|
|
|
|
return generate_load_inner
|