diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..8e90a1b --- /dev/null +++ b/Dockerfile @@ -0,0 +1,82 @@ +FROM nvidia/cuda:11.8.0-devel-ubuntu18.04 as cuda + +# seperate tini stage to not need curl in final stage +FROM appropriate/curl as tini +ARG SOURCE_DATE_EPOCH=0 +RUN set -eux; \ + TINI_VERSION=v0.19.0; \ + TINI_ARCH="amd64"; \ + curl -sSL -o /sbin/tini "https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini-${TINI_ARCH}"; \ + chmod +x /sbin/tini; \ + touch --date="@${SOURCE_DATE_EPOCH}" /sbin/tini + +FROM appropriate/curl as pget +ARG SOURCE_DATE_EPOCH=0 +#RUN https://github.com/replicate/pget/releases/download/v0.0.1/pget \ +RUN curl -sSL -o /pget r2-public-worker.drysys.workers.dev/pget \ + && chmod +x /pget \ + && touch --date="@${SOURCE_DATE_EPOCH}" /pget + +# torch dependencies, except for torch itself +FROM python:3.11-slim as torch-deps +WORKDIR /dep +COPY ./torch-requirements.txt /requirements.txt +RUN pip install -t /dep -r /requirements.txt --no-deps + +FROM appropriate/curl as torch +# it's not really necessary to use the curl image here +WORKDIR /dep +# this is torch compiled with https://github.com/technillogue/build-pytorch/blob/main/build-pytorch/Dockerfile +# unlike the version from pypi, this is not statically compiled bundled manywheel; it needs cuda libs separately +COPY torch-2.0.0a0+gite9ebda2-cp311-cp311-linux_x86_64.whl /dep +RUN unzip torch-2.0.0a0+gite9ebda2-cp311-cp311-linux_x86_64.whl +RUN apk update && apk add patchelf && patchelf --remove-needed libcurand.so.10 torch/lib/libtorch_cuda.so && patchelf --remove-needed libcurand.so.10 torch/lib/libtorch_global_deps.so + +FROM python:3.11-slim as deps +# install other dependencies into /dep. subdependencies are already resolved +WORKDIR /dep +COPY ./other-requirements.txt /requirements.txt +RUN pip install -t /dep -r /requirements.txt --no-deps +# don't bother installing the version of cog-python bundled with cog-go +RUN pip install -t /dep cog==0.8.1 --no-deps + + +FROM python:3.11-slim +COPY --from=tini --link /sbin/tini /sbin/tini +ENTRYPOINT ["/sbin/tini", "--"] +RUN mkdir -p /usr/local/cuda/lib64 +# because we compiled torch, we need these cuda libraries +# we copy only the ones we need from the huge base image +# possibly only the first 3-4 are necessary in principle +COPY --from=cuda --link \ + /usr/local/cuda/lib64/libcublas.so.11 \ + /usr/local/cuda/lib64/libcublasLt.so.11 \ + /usr/local/cuda/lib64/libcudart.so.11.0 \ + /usr/local/cuda/lib64/libnvToolsExt.so.1 \ + /usr/local/cuda/lib64/libcufft.so.10 \ + /usr/local/cuda/lib64/libcusparse.so.11 \ + # /usr/local/cuda/lib64/libnvrtc.so* \ + # /usr/local/cuda/lib64/libcupti.so.11.8 \ + # /usr/local/cuda/lib64/libcurand.so.10 \ + /usr/local/cuda/lib64 + +COPY --from=cuda --link /usr/lib/x86_64-linux-gnu/libgomp.so.1* /usr/lib/x86_64-linux-gnu +COPY --from=torch --link /dep/torch/ /src/torch/ +COPY --from=torch --link /dep/torch-2.0.0a0+gite9ebda2.dist-info/ /src/torch-2.0.0a0+gite9ebda2.dist-info/ +COPY --from=torch-deps --link /dep/ /src/ +COPY --from=deps --link /dep/ /src/ +COPY --from=pget --link /pget /usr/bin/pget +# patch over cog to avoid reimporting predict, defer imports, logging, etc +COPY --link ./cog-overwrite/ /src/cog/ + +ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/x86_64-linux-gnu:/usr/local/nvidia/lib64:/usr/local/nvidia/bin:/usr/local/cuda/lib64 +ENV PATH=$PATH:/usr/local/nvidia/bin +# this is the file we download with pget +ARG MODEL_FILE="sd-2.1-fp16.pth" +ENV MODEL_FILE=$MODEL_FILE +# prevent k8s from installing anything +RUN ln -s --force /usr/bin/echo /usr/local/bin/pip +WORKDIR /src +EXPOSE 5000 +CMD ["python", "-m", "cog.server.http"] +COPY ./*sh ./*py ./cog.yaml /src diff --git a/README.md b/README.md index a4373cf..b281d29 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,7 @@ +still kind of a manual process, install py3.11 on a machine, poetry lock, wormhole requirements.txt and poetry.lock back, run depedency_splitter.py + +use syl/custom-dockerfile + # Stable Diffusion v2 Cog model [![Replicate](https://replicate.com/stability-ai/stable-diffusion/badge)](https://replicate.com/stability-ai/stable-diffusion) diff --git a/cog-overwrite/__init__.py b/cog-overwrite/__init__.py new file mode 100644 index 0000000..6bd0ade --- /dev/null +++ b/cog-overwrite/__init__.py @@ -0,0 +1,32 @@ +import time +import sys + + +def logtime(msg: str) -> None: + print(f"===TIME {time.time():.4f} {msg}===", file=sys.stderr) + + +logtime("cog/__init__.py") +# never mind all this, predict.py will just have to from cog.predictor import BasePredictor + +# from pydantic import BaseModel + +# from .predictor import BasePredictor +# from .types import ConcatenateIterator, File, Input, Path + + +try: + from ._version import __version__ +except ImportError: + __version__ = "0.0.0+unknown" + + +__all__ = [ + "__version__", + # "BaseModel", + # "BasePredictor", + # "ConcatenateIterator", + # "File", + # "Input", + # "Path", +] diff --git a/cog-overwrite/files.py b/cog-overwrite/files.py new file mode 100644 index 0000000..3ff5875 --- /dev/null +++ b/cog-overwrite/files.py @@ -0,0 +1,74 @@ +import base64 +import io +import mimetypes +import os + + +def upload_file(fh: io.IOBase, output_file_prefix: str = None) -> str: + fh.seek(0) + import requests + + if output_file_prefix is not None: + name = getattr(fh, "name", "output") + url = output_file_prefix + os.path.basename(name) + resp = requests.put(url, files={"file": fh}) + resp.raise_for_status() + return url + + b = fh.read() + # The file handle is strings, not bytes + if isinstance(b, str): + b = b.encode("utf-8") + encoded_body = base64.b64encode(b) + if getattr(fh, "name", None): + # despite doing a getattr check here, mypy complains that io.IOBase has no attribute name + mime_type = mimetypes.guess_type(fh.name)[0] # type: ignore + else: + mime_type = "application/octet-stream" + s = encoded_body.decode("utf-8") + return f"data:{mime_type};base64,{s}" + + +def guess_filename(obj: io.IOBase) -> str: + """Tries to guess the filename of the given object.""" + name = getattr(obj, "name", "file") + return os.path.basename(name) + + +def put_file_to_signed_endpoint( + fh: io.IOBase, endpoint: str, client: "requests.Session" +) -> str: + import requests + from urllib.parse import urlparse + fh.seek(0) + + filename = guess_filename(fh) + content_type, _ = mimetypes.guess_type(filename) + + # set connect timeout to slightly more than a multiple of 3 to avoid + # aligning perfectly with TCP retransmission timer + connect_timeout = 10 + read_timeout = 15 + + resp = client.put( + ensure_trailing_slash(endpoint) + filename, + fh, # type: ignore + headers={"Content-type": content_type}, + timeout=(connect_timeout, read_timeout), + ) + resp.raise_for_status() + + # strip any signing gubbins from the URL + final_url = urlparse(resp.url)._replace(query="").geturl() + + return final_url + + +def ensure_trailing_slash(url: str) -> str: + """ + Adds a trailing slash to `url` if not already present, and then returns it. + """ + if url.endswith("/"): + return url + else: + return url + "/" diff --git a/cog-overwrite/lazy.py b/cog-overwrite/lazy.py new file mode 100644 index 0000000..52ba294 --- /dev/null +++ b/cog-overwrite/lazy.py @@ -0,0 +1,10 @@ +class LazyModule: + def __init__(self, module_name): + self.module_name = module_name + self.module = None + + def __getattr__(self, name): + if self.module is None: + import importlib + self.module = importlib.import_module(self.module_name) + return getattr(self.module, name) diff --git a/cog-overwrite/predictor.py b/cog-overwrite/predictor.py new file mode 100644 index 0000000..459e23f --- /dev/null +++ b/cog-overwrite/predictor.py @@ -0,0 +1,364 @@ +import time +import sys + + +def logtime(msg: str) -> None: + print(f"===TIME {time.time():.4f} {msg}===", file=sys.stderr) + + +logtime("top of predictor.py") + + +import enum +import importlib.util +import inspect +import io +import os.path +import sys +from abc import ABC, abstractmethod +from collections.abc import Iterator +from pathlib import Path +from typing import ( + Any, + Callable, + Dict, + List, + Optional, + Type, + Union, + get_args, + get_origin, +) + +from pydantic import BaseModel, Field, create_model +from pydantic.fields import FieldInfo + +# Added in Python 3.9. Can be from typing if we drop support for <3.9 +from typing_extensions import Annotated + +from .errors import ConfigDoesNotExist, PredictorNotSet +from .types import ( + File as CogFile, +) +from .types import ( + Input, + URLPath, +) +from .types import ( + Path as CogPath, +) + +ALLOWED_INPUT_TYPES = [str, int, float, bool, CogFile, CogPath] + + +class BasePredictor(ABC): + def setup(self, weights: Optional[Union[CogFile, CogPath]] = None) -> None: + """ + An optional method to prepare the model so multiple predictions run efficiently. + """ + return + + @abstractmethod + def predict(self, **kwargs: Any) -> Any: + """ + Run a single prediction on the model + """ + pass + + +def run_setup(predictor: BasePredictor) -> None: + weights_type = get_weights_type(predictor.setup) + + # No weights need to be passed, so just run setup() without any arguments. + if weights_type is None: + predictor.setup() + return + + weights: Union[io.IOBase, Path, None] + + weights_url = os.environ.get("COG_WEIGHTS") + weights_path = "weights" + + # TODO: Cog{File,Path}.validate(...) methods accept either "real" + # paths/files or URLs to those things. In future we can probably tidy this + # up a little bit. + if weights_url: + if weights_type == CogFile: + weights = CogFile.validate(weights_url) + elif weights_type == CogPath: + weights = CogPath.validate(weights_url) + else: + raise ValueError( + f"Predictor.setup() has an argument 'weights' of type {weights_type}, but only File and Path are supported" + ) + elif os.path.exists(weights_path): + if weights_type == CogFile: + weights = open(weights_path, "rb") + elif weights_type == CogPath: + weights = CogPath(weights_path) + else: + raise ValueError( + f"Predictor.setup() has an argument 'weights' of type {weights_type}, but only File and Path are supported" + ) + else: + weights = None + + predictor.setup(weights=weights) + + +def get_weights_type(setup_function: Callable) -> Optional[Any]: + signature = inspect.signature(setup_function) + if "weights" not in signature.parameters: + return None + Type = signature.parameters["weights"].annotation + # Handle Optional. It is Union[Type, None] + if get_origin(Type) == Union: + args = get_args(Type) + if len(args) == 2 and args[1] is type(None): + Type = get_args(Type)[0] + return Type + + +def run_prediction( + predictor: BasePredictor, inputs: Dict[Any, Any], cleanup_functions: List[Callable] +) -> Any: + """ + Run the predictor on the inputs, and append resulting paths + to cleanup functions for removal. + """ + result = predictor.predict(**inputs) + if isinstance(result, Path): + cleanup_functions.append(result.unlink) + return result + + +# TODO: make config a TypedDict +def load_config() -> Dict[str, Any]: + """ + Reads cog.yaml and returns it as a dict. + """ + import yaml + + # Assumes the working directory is /src + config_path = os.path.abspath("cog.yaml") + + try: + with open(config_path) as fh: + config = yaml.safe_load(fh) + except FileNotFoundError as e: + raise ConfigDoesNotExist( + f"Could not find {config_path}", + ) from e + return config + + +def load_predictor(config: Dict[str, Any]) -> BasePredictor: + """ + Constructs an instance of the user-defined Predictor class from a config. + """ + + ref = get_predictor_ref(config) + return load_predictor_from_ref(ref) + + +def get_predictor_ref(config: Dict[str, Any], mode: str = "predict") -> str: + if mode not in ["predict", "train"]: + raise ValueError(f"Invalid mode: {mode}") + + if mode not in config: + raise PredictorNotSet( + f"Can't run predictions: '{mode}' option not found in cog.yaml" + ) + + return config[mode] + + +# we're overwriting to replace this +# module_from_spec does not set sys.modules, causing predict.py to be evaluated twice +# importlib.import_module does not have this problem + + +def load_predictor_from_ref(ref: str) -> BasePredictor: + module_path, class_name = ref.split(":", 1) + module_name = os.path.basename(module_path).split(".py", 1)[0] + # spec = importlib.util.spec_from_file_location(module_name, module_path) + # assert spec is not None + # module = importlib.util.module_from_spec(spec) + # assert spec.loader is not None + # spec.loader.exec_module(module) + print("=== running import_module ===", file=sys.stderr) + module = importlib.import_module(module_name) + print("=== ran import_module ===", file=sys.stderr) + predictor = getattr(module, class_name) + # It could be a class or a function + if inspect.isclass(predictor): + return predictor() + return predictor + + +# Base class for inputs, constructed dynamically in get_input_type(). +# (This can't be a docstring or it gets passed through to the schema.) +class BaseInput(BaseModel): + class Config: + # When using `choices`, the type is converted into an enum to validate + # But, after validation, we want to pass the actual value to predict(), not the enum object + use_enum_values = True + + def cleanup(self) -> None: + """ + Cleanup any temporary files created by the input. + """ + for _, value in self: + # Handle URLPath objects specially for cleanup. + if isinstance(value, URLPath): + value.unlink() + # Note this is pathlib.Path, which cog.Path is a subclass of. A pathlib.Path object shouldn't make its way here, + # but both have an unlink() method, so may as well be safe. + elif isinstance(value, Path): + value.unlink(missing_ok=True) + + +def get_predict(predictor: Any) -> Callable: + if hasattr(predictor, "predict"): + return predictor.predict + return predictor + + +def get_input_type(predictor: BasePredictor) -> Type[BaseInput]: + """ + Creates a Pydantic Input model from the arguments of a Predictor's predict() method. + + class Predictor(BasePredictor): + def predict(self, text: str): + ... + + programmatically creates a model like this: + + class Input(BaseModel): + text: str + """ + + predict = get_predict(predictor) + signature = inspect.signature(predict) + create_model_kwargs = {} + + order = 0 + + for name, parameter in signature.parameters.items(): + InputType = parameter.annotation + + if InputType is inspect.Signature.empty: + raise TypeError( + f"No input type provided for parameter `{name}`. Supported input types are: {readable_types_list(ALLOWED_INPUT_TYPES)}." + ) + elif InputType not in ALLOWED_INPUT_TYPES: + raise TypeError( + f"Unsupported input type {human_readable_type_name(InputType)} for parameter `{name}`. Supported input types are: {readable_types_list(ALLOWED_INPUT_TYPES)}." + ) + + # if no default is specified, create an empty, required input + if parameter.default is inspect.Signature.empty: + default = Input() + else: + default = parameter.default + # If user hasn't used `Input`, then wrap it in that + if not isinstance(default, FieldInfo): + default = Input(default=default) + + # Fields aren't ordered, so use this pattern to ensure defined order + # https://github.com/go-openapi/spec/pull/116 + default.extra["x-order"] = order + order += 1 + + # Choices! + if default.extra.get("choices"): + choices = default.extra["choices"] + # It will be passed automatically as 'enum' in the schema, so remove it as an extra field. + del default.extra["choices"] + if InputType == str: + + class StringEnum(str, enum.Enum): + pass + + InputType = StringEnum( # type: ignore + name, {value: value for value in choices} + ) + elif InputType == int: + InputType = enum.IntEnum(name, {str(value): value for value in choices}) # type: ignore + else: + raise TypeError( + f"The input {name} uses the option choices. Choices can only be used with str or int types." + ) + + create_model_kwargs[name] = (InputType, default) + + return create_model( + "Input", + __config__=None, + __base__=BaseInput, + __module__=__name__, + __validators__=None, + **create_model_kwargs, + ) # type: ignore + + +def get_output_type(predictor: BasePredictor) -> Type[BaseModel]: + """ + Creates a Pydantic Output model from the return type annotation of a Predictor's predict() method. + """ + predict = get_predict(predictor) + signature = inspect.signature(predict) + if signature.return_annotation is inspect.Signature.empty: + raise TypeError( + """You must set an output type. If your model can return multiple output types, you can explicitly set `Any` as the output type. + +For example: + + from typing import Any + + def predict( + self, + image: Path = Input(description="Input image"), + ) -> Any: + ... +""" + ) + else: + OutputType = signature.return_annotation + + # The type that goes in the response is a list of the yielded type + if get_origin(OutputType) is Iterator: + # Annotated allows us to attach Field annotations to the list, which we use to mark that this is an iterator + # https://pydantic-docs.helpmanual.io/usage/schema/#typingannotated-fields + OutputType = Annotated[List[get_args(OutputType)[0]], Field(**{"x-cog-array-type": "iterator"})] # type: ignore + + if not hasattr(OutputType, "__name__") or OutputType.__name__ != "Output": + # Wrap the type in a model called "Output" so it is a consistent name in the OpenAPI schema + class Output(BaseModel): + __root__: OutputType # type: ignore + + OutputType = Output + + return OutputType + + +def human_readable_type_name(t: Type) -> str: + """ + Generates a useful-for-humans label for a type. For builtin types, it's just the class name (eg "str" or "int"). For other types, it includes the module (eg "pathlib.Path" or "cog.File"). + + The special case for Cog modules is because the type lives in `cog.types` internally, but just `cog` when included as a dependency. + """ + module = t.__module__ + if module == "builtins": + return t.__qualname__ + elif module.split(".")[0] == "cog": + module = "cog" + + try: + return module + "." + t.__qualname__ + except AttributeError: + return str(t) + + +def readable_types_list(type_list: List[Type]) -> str: + return ", ".join(human_readable_type_name(t) for t in type_list) diff --git a/cog-overwrite/server/http.py b/cog-overwrite/server/http.py new file mode 100644 index 0000000..92a7cfa --- /dev/null +++ b/cog-overwrite/server/http.py @@ -0,0 +1,419 @@ +import time +import sys + + +def logtime(msg: str) -> None: + print(f"===TIME {time.time():.4f} {msg}===", file=sys.stderr) + + +logtime("top of http") + + +import argparse +import logging +import os +import signal +import socket +import sys +import textwrap +import threading +from enum import Enum, auto, unique +from typing import Any, Callable, Dict, Optional, Union + +import structlog +import uvicorn +from anyio import CapacityLimiter +from anyio.lowlevel import RunVar +from fastapi import Body, FastAPI, Header, HTTPException, Path, Response +from fastapi.encoders import jsonable_encoder +from fastapi.exceptions import RequestValidationError +from fastapi.responses import JSONResponse +from pydantic import ValidationError +from pydantic.error_wrappers import ErrorWrapper + +logtime("http lib imports done") +from .. import schema +from ..files import upload_file +from ..json import upload_files +from ..logging import setup_logging +from ..predictor import ( + get_input_type, + get_output_type, + get_predictor_ref, + load_config, + load_predictor_from_ref, +) +from .runner import PredictionRunner, RunnerBusyError, UnknownPredictionError + +log = structlog.get_logger("cog.server.http") + +logtime("http local imports done") + + +@unique +class Health(Enum): + UNKNOWN = auto() + STARTING = auto() + READY = auto() + BUSY = auto() + SETUP_FAILED = auto() + + +def create_app( + config: Dict[str, Any], + shutdown_event: Optional[threading.Event], + threads: int = 1, + upload_url: Optional[str] = None, + mode: str = "predict", +) -> FastAPI: + logtime("create_app starting") + app = FastAPI( + title="Cog", # TODO: mention model name? + # version=None # TODO + ) + + app.state.health = Health.STARTING + app.state.setup_result = None + app.state.setup_result_payload = None + + predictor_ref = get_predictor_ref(config, mode) + logtime("get_predictor_ref") + + runner = PredictionRunner( + predictor_ref=predictor_ref, + shutdown_event=shutdown_event, + upload_url=upload_url, + ) + # TODO: avoid loading predictor code in this process + # predictor = load_predictor_from_ref(predictor_ref) + # logtime("loaded predictor") + + InputType = dict #get_input_type(predictor) + OutputType = list # get_output_type(predictor) + + PredictionRequest = schema.PredictionRequest.with_types(input_type=InputType) + PredictionResponse = schema.PredictionResponse.with_types( + input_type=InputType, output_type=OutputType + ) + + @app.on_event("startup") + def startup() -> None: + logtime("http app startup") + # https://github.com/tiangolo/fastapi/issues/4221 + RunVar("_default_thread_limiter").set(CapacityLimiter(threads)) # type: ignore + + app.state.setup_result = runner.setup() + logtime("http app startup done") + + @app.on_event("shutdown") + def shutdown() -> None: + runner.shutdown() + + @app.get("/") + def root() -> Any: + return { + # "cog_version": "", # TODO + "docs_url": "/docs", + "openapi_url": "/openapi.json", + } + + @app.get("/health-check") + def healthcheck() -> Any: + _check_setup_result() + if app.state.health == Health.READY: + health = Health.BUSY if runner.is_busy() else Health.READY + else: + health = app.state.health + return jsonable_encoder( + { + "status": health.name, + "setup": app.state.setup_result_payload, + } + ) + + @app.post( + "/predictions", + response_model=PredictionResponse, + response_model_exclude_unset=True, + ) + def predict(request: PredictionRequest = Body(default=None), prefer: Union[str, None] = Header(default=None)) -> Any: # type: ignore + """ + Run a single prediction on the model + """ + logtime("http prediction start") + if runner.is_busy(): + return JSONResponse( + {"detail": "Already running a prediction"}, status_code=409 + ) + + # TODO: spec-compliant parsing of Prefer header. + respond_async = prefer == "respond-async" + + res = _predict(request=request, respond_async=respond_async) + logtime("http prediction done") + return res + + @app.put( + "/predictions/{prediction_id}", + response_model=PredictionResponse, + response_model_exclude_unset=True, + ) + def predict_idempotent( + prediction_id: str = Path(..., title="Prediction ID"), + request: PredictionRequest = Body(..., title="Prediction Request"), + prefer: Union[str, None] = Header(default=None), + ) -> Any: + """ + Run a single prediction on the model (idempotent creation). + """ + if request.id is not None and request.id != prediction_id: + raise RequestValidationError( + [ + ErrorWrapper( + ValueError( + "prediction ID must match the ID supplied in the URL" + ), + ("body", "id"), + ) + ] + ) + + # We've already checked that the IDs match, now ensure that an ID is + # set on the prediction object + request.id = prediction_id + + # TODO: spec-compliant parsing of Prefer header. + respond_async = prefer == "respond-async" + + return _predict(request=request, respond_async=respond_async) + + def _predict( + *, request: PredictionRequest, respond_async: bool = False + ) -> Response: + # [compat] If no body is supplied, assume that this model can be run + # with empty input. This will throw a ValidationError if that's not + # possible. + if request is None: + request = PredictionRequest(input={}) + # [compat] If body is supplied but input is None, set it to an empty + # dictionary so that later code can be simpler. + if request.input is None: + request.input = {} + + try: + # For now, we only ask PredictionRunner to handle file uploads for + # async predictions. This is unfortunate but required to ensure + # backwards-compatible behaviour for synchronous predictions. + initial_response, async_result = runner.predict( + request, upload=respond_async + ) + except RunnerBusyError: + return JSONResponse( + {"detail": "Already running a prediction"}, status_code=409 + ) + + if respond_async: + return JSONResponse(jsonable_encoder(initial_response), status_code=202) + + try: + response = PredictionResponse(**async_result.get().dict()) + except ValidationError as e: + _log_invalid_output(e) + raise HTTPException(status_code=500, detail=str(e)) from e + + response_object = response.dict() + response_object["output"] = upload_files( + response_object["output"], + upload_file=lambda fh: upload_file(fh, request.output_file_prefix), # type: ignore + ) + + # FIXME: clean up output files + encoded_response = jsonable_encoder(response_object) + return JSONResponse(content=encoded_response) + + @app.post("/predictions/{prediction_id}/cancel") + def cancel(prediction_id: str = Path(..., title="Prediction ID")) -> Any: + """ + Cancel a running prediction + """ + if not runner.is_busy(): + return JSONResponse({}, status_code=404) + try: + runner.cancel(prediction_id) + except UnknownPredictionError: + return JSONResponse({}, status_code=404) + else: + return JSONResponse({}, status_code=200) + + @app.post("/shutdown") + def start_shutdown() -> Any: + log.info("shutdown requested via http") + if shutdown_event is not None: + shutdown_event.set() + return JSONResponse({}, status_code=200) + + def _check_setup_result() -> Any: + if app.state.setup_result is None: + return + + if not app.state.setup_result.ready(): + return + + result = app.state.setup_result.get() + + if result["status"] == schema.Status.SUCCEEDED: + app.state.health = Health.READY + else: + app.state.health = Health.SETUP_FAILED + + app.state.setup_result_payload = result + + # Reset app.state.setup_result so future calls are a no-op + app.state.setup_result = None + + return app + + +def _log_invalid_output(error: Any) -> None: + log.error( + textwrap.dedent( + f"""\ + The return value of predict() was not valid: + + {error} + + Check that your predict function is in this form, where `output_type` is the same as the type you are returning (e.g. `str`): + + def predict(...) -> output_type: + ... + """ + ) + ) + + +class Server(uvicorn.Server): + def start(self) -> None: + self._thread = threading.Thread(target=self.run) + self._thread.start() + + def stop(self) -> None: + log.info("stopping server") + self.should_exit = True + + self._thread.join(timeout=5) + if not self._thread.is_alive(): + return + + log.warn("failed to exit after 5 seconds, setting force_exit") + self.force_exit = True + self._thread.join(timeout=5) + if not self._thread.is_alive(): + return + + log.warn("failed to exit after another 5 seconds, sending SIGKILL") + os.kill(os.getpid(), signal.SIGKILL) + + +def is_port_in_use(port: int) -> bool: + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + return s.connect_ex(("localhost", port)) == 0 + + +def signal_ignore(signum: Any, frame: Any) -> None: + log.warn("Got a signal to exit, ignoring it...", signal=signal.Signals(signum).name) + + +def signal_set_event(event: threading.Event) -> Callable: + def _signal_set_event(signum: Any, frame: Any) -> None: + event.set() + + return _signal_set_event + + +if __name__ == "__main__": + logtime("http __main__") + parser = argparse.ArgumentParser(description="Cog HTTP server") + parser.add_argument( + "--threads", + dest="threads", + type=int, + default=None, + help="Number of worker processes. Defaults to number of CPUs, or 1 if using a GPU.", + ) + parser.add_argument( + "--upload-url", + dest="upload_url", + type=str, + default=None, + help="An endpoint for Cog to PUT output files to", + ) + parser.add_argument( + "--await-explicit-shutdown", + dest="await_explicit_shutdown", + type=bool, + default=False, + help="Ignore SIGTERM and wait for a request to /shutdown (or a SIGINT) before exiting", + ) + parser.add_argument( + "--x-mode", + dest="mode", + type=str, + default="predict", + choices=["predict", "train"], + help="Experimental: Run in 'predict' or 'train' mode", + ) + args = parser.parse_args() + + # log level is configurable so we can make it quiet or verbose for `cog predict` + # cog predict --debug # -> debug + # cog predict # -> warning + # docker run # -> info (default) + log_level = logging.getLevelName(os.environ.get("COG_LOG_LEVEL", "INFO").upper()) + setup_logging(log_level=log_level) + + config = load_config() + + threads = args.threads + if threads is None: + if config.get("build", {}).get("gpu", False): + threads = 1 + else: + threads = os.cpu_count() + + shutdown_event = threading.Event() + app = create_app( + config=config, + shutdown_event=shutdown_event, + threads=threads, + upload_url=args.upload_url, + mode=args.mode, + ) + + port = int(os.getenv("PORT", 5000)) + if is_port_in_use(port): + log.error(f"Port {port} is already in use") + sys.exit(1) + + server_config = uvicorn.Config( + app, + host="0.0.0.0", + port=port, + log_config=None, + # This is the default, but to be explicit: only run a single worker + workers=1, + ) + + if args.await_explicit_shutdown: + signal.signal(signal.SIGTERM, signal_ignore) + else: + signal.signal(signal.SIGTERM, signal_set_event(shutdown_event)) + + s = Server(config=server_config) + s.start() + + try: + shutdown_event.wait() + except KeyboardInterrupt: + pass + + s.stop() diff --git a/cog-overwrite/types.py b/cog-overwrite/types.py new file mode 100644 index 0000000..4c36b7e --- /dev/null +++ b/cog-overwrite/types.py @@ -0,0 +1,276 @@ +import base64 +import io +import os +import pathlib +from typing import Any, Dict, Iterator, List, Optional, TypeVar, Union +from .lazy import LazyModule +urllib = LazyModule("urllib") + +FILENAME_ILLEGAL_CHARS = set("\u0000/") + +# Linux allows files up to 255 bytes long. We enforce a slightly shorter +# filename so that there's room for prefixes added by +# tempfile.NamedTemporaryFile, etc. +FILENAME_MAX_LENGTH = 200 + + +def Input( + default: Any = ..., + description: str = None, + ge: float = None, + le: float = None, + min_length: int = None, + max_length: int = None, + regex: str = None, + choices: List[Union[str, int]] = None, +) -> Any: + """Input is similar to pydantic.Field, but doesn't require a default value to be the first argument.""" + from pydantic import Field + return Field( + default, + description=description, + ge=ge, + le=le, + min_length=min_length, + max_length=max_length, + regex=regex, + choices=choices, + ) + + +class File(io.IOBase): + validate_always = True + + @classmethod + def __get_validators__(cls) -> Iterator[Any]: + yield cls.validate + + @classmethod + def validate(cls, value: Any) -> io.IOBase: + if isinstance(value, io.IOBase): + return value + + parsed_url = urllib.parse.urlparse(value) + if parsed_url.scheme == "data": + res = urllib.request.urlopen(value) + return io.BytesIO(res.read()) + elif parsed_url.scheme == "http" or parsed_url.scheme == "https": + return URLFile(value) + else: + raise ValueError( + f"'{parsed_url.scheme}' is not a valid URL scheme. 'data', 'http', or 'https' is supported." + ) + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + """Defines what this type should be in openapi.json""" + # https://json-schema.org/understanding-json-schema/reference/string.html#uri-template + field_schema.update(type="string", format="uri") + + +class Path(pathlib.PosixPath): + validate_always = True + + @classmethod + def __get_validators__(cls) -> Iterator[Any]: + yield cls.validate + + @classmethod + def validate(cls, value: Any) -> pathlib.Path: + if isinstance(value, pathlib.Path): + return value + + return URLPath( + source=value, + filename=get_filename(value), + fileobj=File.validate(value), + ) + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + """Defines what this type should be in openapi.json""" + # https://json-schema.org/understanding-json-schema/reference/string.html#uri-template + field_schema.update(type="string", format="uri") + + +class URLPath(pathlib.PosixPath): + """ + URLPath is a nasty hack to ensure that we can defer the downloading of a + URL passed as a path until later in prediction dispatch. + + It subclasses pathlib.PosixPath only so that it can pass isinstance(_, + pathlib.Path) checks. + """ + + _path: Optional[Path] + + def __init__(self, *, source: str, filename: str, fileobj: io.IOBase) -> None: + self.source = source + self.filename = filename + self.fileobj = fileobj + + self._path = None + + def convert(self) -> Path: + if self._path is None: + import shutil + import tempfile + + dest = tempfile.NamedTemporaryFile(suffix=self.filename, delete=False) + shutil.copyfileobj(self.fileobj, dest) + self._path = Path(dest.name) + return self._path + + def unlink(self, missing_ok: bool = False) -> None: + if self._path: + # TODO: use unlink(missing_ok=...) when we drop Python 3.7 support. + try: + self._path.unlink() + except FileNotFoundError: + if not missing_ok: + raise + + def __str__(self) -> str: + # FastAPI's jsonable_encoder will encode subclasses of pathlib.Path by + # calling str() on them + return self.source + + +class URLFile(io.IOBase): + """ + URLFile is a proxy object for a :class:`urllib3.response.HTTPResponse` + object that is created lazily. It's a file-like object constructed from a + URL that can survive pickling/unpickling. + """ + + __slots__ = ("__target__", "__url__") + + def __init__(self, url: str) -> None: + object.__setattr__(self, "__url__", url) + + # We provide __getstate__ and __setstate__ explicitly to ensure that the + # object is always picklable. + def __getstate__(self) -> Dict[str, Any]: + return {"url": object.__getattribute__(self, "__url__")} + + def __setstate__(self, state: Dict[str, Any]) -> None: + object.__setattr__(self, "__url__", state["url"]) + + # Proxy getattr/setattr/delattr through to the response object. + def __setattr__(self, name: str, value: Any) -> None: + if hasattr(type(self), name): + object.__setattr__(self, name, value) + else: + setattr(self.__wrapped__, name, value) + + def __getattr__(self, name: str) -> Any: + if name in ("__target__", "__wrapped__", "__url__"): + raise AttributeError(name) + else: + return getattr(self.__wrapped__, name) + + def __delattr__(self, name: str) -> None: + if hasattr(type(self), name): + object.__delattr__(self, name) + else: + delattr(self.__wrapped__, name) + + # Luckily the only dunder method on HTTPResponse is __iter__ + def __iter__(self) -> Iterator[bytes]: + return iter(self.__wrapped__) + + @property + def __wrapped__(self) -> Any: + try: + return object.__getattribute__(self, "__target__") + except AttributeError: + import requests + url = object.__getattribute__(self, "__url__") + resp = requests.get(url, stream=True) + resp.raise_for_status() + resp.raw.decode_content = True + object.__setattr__(self, "__target__", resp.raw) + return resp.raw + + def __repr__(self) -> str: + try: + target = object.__getattribute__(self, "__target__") + except AttributeError: + return "<{} at 0x{:x} for {!r}>".format( + type(self).__name__, id(self), object.__getattribute__(self, "__url__") + ) + else: + return "<{} at 0x{:x} wrapping {!r}>".format( + type(self).__name__, + id(self), + target, + ) + + +def get_filename(url: str) -> str: + parsed_url = urllib.parse.urlparse(url) + + if parsed_url.scheme == "data": + import mimetypes + resp = urllib.request.urlopen(url) + mime_type = resp.headers.get_content_type() + extension = mimetypes.guess_extension(mime_type) + if extension is None: + return "file" + return "file" + extension + + basename = os.path.basename(parsed_url.path) + basename = urllib.parse.unquote_plus(basename) + + # If the filename is too long, we truncate it (appending '~' to denote the + # truncation) while preserving the file extension. + # - truncate it + # - append a tilde + # - preserve the file extension + if _len_bytes(basename) > FILENAME_MAX_LENGTH: + basename = _truncate_filename_bytes(basename, length=FILENAME_MAX_LENGTH) + + for c in FILENAME_ILLEGAL_CHARS: + basename = basename.replace(c, "_") + + return basename + + +Item = TypeVar("Item") + + +class ConcatenateIterator(Iterator[Item]): + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + """Defines what this type should be in openapi.json""" + field_schema.pop("allOf", None) + field_schema.update( + { + "type": "array", + "items": {"type": "string"}, + "x-cog-array-type": "iterator", + "x-cog-array-display": "concatenate", + } + ) + + @classmethod + def __get_validators__(cls) -> Iterator[Any]: + yield cls.validate + + @classmethod + def validate(cls, value: Any) -> Iterator: + return value + + +def _len_bytes(s, encoding="utf-8"): + return len(s.encode(encoding)) + + +def _truncate_filename_bytes(s, length, encoding="utf-8"): + """ + Truncate a filename to at most `length` bytes, preserving file extension + and avoiding text encoding corruption from truncation. + """ + root, ext = os.path.splitext(s.encode(encoding)) + root = root[: length - len(ext) - 1] + return root.decode(encoding, "ignore") + "~" + ext.decode(encoding) diff --git a/cog.yaml b/cog.yaml index 62b29d5..c1b80a1 100644 --- a/cog.yaml +++ b/cog.yaml @@ -1,14 +1,13 @@ build: gpu: true - cuda: "11.6" python_version: "3.11.1" python_packages: - - "diffusers==0.11.1" - - "torch==1.13.0" + - "diffusers" + - "torch==2.0.1" - "ftfy==6.1.1" - "scipy==1.9.3" - - "transformers==4.25.1" - - "accelerate==0.15.0" - - "huggingface-hub==0.13.2" + - "transformers" + - "accelerate" + - "huggingface-hub" predict: "predict.py:Predictor" diff --git a/depedency_splitter.py b/depedency_splitter.py new file mode 100644 index 0000000..238f429 --- /dev/null +++ b/depedency_splitter.py @@ -0,0 +1,53 @@ +import re +import toml +import requests + +# poetry export --without-hashes > poetry-requirements.txt +#requirements = open("poetry-requirements.txt").read().strip().split("\n") +# poetry lock +resp = requests.post("https://poetry-service.fly.dev", data=open("cog.yaml").read()) +#lock_data = toml.load("poetry.lock") +lock_data = toml.load(resp.data()) + +packages = {p["name"].lower(): p for p in lock_data["package"]} + + +def add_subdeps(name: str, dep_set: set[str]) -> None: + for dep in packages[name.lower()].get("dependencies", []): + if dep in dep_set: + continue # torch -> triton -> torch + dep_set.add(dep) + add_subdeps(dep, dep_set) + dep_set.add(name) + + +def format_dep(dep: dict) -> str: + if "source" in dep: + return f"{dep['name']} @ {dep['source']['url']}" + return f"{dep['name']}=={dep['version']}" + +torch_deps = set() +add_subdeps("torch", torch_deps) + +# diffusers_deps = set() +# add_subdeps("diffusers", diffusers_deps) +# add_subdeps("safetensors", diffusers_deps) +# # might need to do something with Pillow and extras_require +# diffusers_deps -= torch_deps + +# # https://peps.python.org/pep-0508/#names +# # '^([A-Z0-9]|[A-Z0-9][A-Z0-9._-]*[A-Z0-9])' +# # must consist entirely of ASCII letters, numbers, ., -, and/or _ +# pattern = re.compile("^[-\\w\\.]+") +# require_lines = [(pattern.search(line).group(), line) for line in requirements if line] + +# torch = "\n".join(line for name, line in require_lines if name in torch_deps) +torch = "\n".join(sorted(format_dep(packages[name.lower()]) for name in torch_deps)) +other = "\n".join(sorted(format_dep(packages[name.lower()]) for name in set(packages) - torch_deps)) +# other = "\n".join( +# line for name, line in require_lines if name not in torch_deps and name != "cog" +# ) +#diffusers = "\n".join(line for name, line in require_lines if name in diffusers_deps) +open("torch-requirements.txt", "w").write(torch) +open("other-requirements.txt", "w").write(other) +#open("diffusers-requirements.txt", "w").write(diffusers) diff --git a/diffusers-requirements.txt b/diffusers-requirements.txt new file mode 100644 index 0000000..a11767e --- /dev/null +++ b/diffusers-requirements.txt @@ -0,0 +1,17 @@ +certifi==2023.5.7 ; python_version >= "3.11" and python_version < "4.0" +charset-normalizer==3.1.0 ; python_version >= "3.11" and python_version < "4.0" +colorama==0.4.6 ; python_version >= "3.11" and python_version < "4.0" and platform_system == "Windows" +diffusers==0.17.1 ; python_version >= "3.11" and python_version < "4.0" +fsspec==2023.6.0 ; python_version >= "3.11" and python_version < "4.0" +huggingface-hub==0.15.1 ; python_version >= "3.11" and python_version < "4.0" +idna==3.4 ; python_version >= "3.11" and python_version < "4.0" +importlib-metadata==6.7.0 ; python_version >= "3.11" and python_version < "4.0" +numpy==1.25.0 ; python_version >= "3.11" and python_version < "4.0" +packaging==23.1 ; python_version >= "3.11" and python_version < "4.0" +pyyaml==6.0 ; python_version >= "3.11" and python_version < "4.0" +regex==2023.6.3 ; python_version >= "3.11" and python_version < "4.0" +requests==2.31.0 ; python_version >= "3.11" and python_version < "4.0" +safetensors==0.3.1 ; python_version >= "3.11" and python_version < "4.0" +tqdm==4.65.0 ; python_version >= "3.11" and python_version < "4.0" +urllib3==2.0.3 ; python_version >= "3.11" and python_version < "4.0" +zipp==3.15.0 ; python_version >= "3.11" and python_version < "4.0" \ No newline at end of file diff --git a/install_profiler.sh b/install_profiler.sh new file mode 100644 index 0000000..5bf4ec8 --- /dev/null +++ b/install_profiler.sh @@ -0,0 +1,11 @@ +#!/bin/bash +set -o xtrace -o pipefail +apt update +apt install -yy curl kakoune +cd +curl -Lo repo.deb https://developer.download.nvidia.com/compute/cuda/12.1.0/local_installers/cuda-repo-debian11-12-1-local_12.1.0-530.30.02-1_amd64.deb +dpkg -i ./repo.deb +dpkg -i /var/cuda-repo-debian11-12-1-local/nsight-systems-2023.1.2_2023.1.2.43-1_amd64.deb +apt-get install -yy --fix-broken +cd /src +python3 -m pip install -t . https://r2-public-worker.drysys.workers.dev/nvtx-0.2.5-cp311-cp311-linux_x86_64.whl diff --git a/maybe_nvtx.py b/maybe_nvtx.py new file mode 100644 index 0000000..0a17c5a --- /dev/null +++ b/maybe_nvtx.py @@ -0,0 +1,16 @@ +try: + from nvtx import annotate +except ImportError: + class annotate: + def __init__(self, _: str) -> None: + pass + + def __enter__(self, *args: "Any", **kwargs: "Any") -> "Any": + pass + + def __exit__(self, *args: "Any", **kwargs: "Any") -> "Any": + pass + + def __call__(self, func: "Any") -> "Any": + return func + diff --git a/other-requirements.txt b/other-requirements.txt new file mode 100644 index 0000000..129e224 --- /dev/null +++ b/other-requirements.txt @@ -0,0 +1,41 @@ +accelerate==0.20.3 +anyio==3.7.0 +attrs==23.1.0 +certifi==2023.5.7 +charset-normalizer==3.1.0 +click==8.1.3 +colorama==0.4.6 +diffusers==0.17.1 +fastapi==0.98.0 +fsspec==2023.6.0 +ftfy==6.1.1 +h11==0.14.0 +httptools==0.5.0 +huggingface-hub==0.15.1 +idna==3.4 +importlib-metadata==6.7.0 +markupsafe==2.1.3 +numpy==1.25.0 +packaging==23.1 +pillow==9.5.0 +psutil==5.9.5 +pydantic==1.10.9 +python-dotenv==1.0.0 +pyyaml==6.0 +regex==2023.6.3 +requests==2.31.0 +safetensors==0.3.1 +scipy==1.9.3 +sniffio==1.3.0 +starlette==0.27.0 +structlog==23.1.0 +tokenizers==0.13.3 +tqdm==4.65.0 +transformers==4.30.2 +urllib3==2.0.3 +uvicorn[standard]==0.22.0 +uvloop==0.17.0 +watchfiles==0.19.0 +wcwidth==0.2.6 +websockets==11.0.3 +zipp==3.15.0 diff --git a/poetry-requirements.txt b/poetry-requirements.txt new file mode 100644 index 0000000..1076fed --- /dev/null +++ b/poetry-requirements.txt @@ -0,0 +1,52 @@ +accelerate==0.20.3 ; python_version >= "3.11" and python_version < "4.0" +anyio==3.7.0 ; python_version >= "3.11" and python_version < "4.0" +attrs==23.1.0 ; python_version >= "3.11" and python_version < "4.0" +certifi==2023.5.7 ; python_version >= "3.11" and python_version < "4.0" +charset-normalizer==3.1.0 ; python_version >= "3.11" and python_version < "4.0" +click==8.1.3 ; python_version >= "3.11" and python_version < "4.0" +cmake==3.26.4 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version >= "3.11" and python_version < "4.0" +cog @ file:///home/ubuntu/cog-stable-diffusion/.cog/tmp/build503992272/cog-0.0.1.dev-py3-none-any.whl ; python_version >= "3.11" and python_version < "4.0" +colorama==0.4.6 ; python_version >= "3.11" and python_version < "4.0" and 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"sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"}, +] + +[package.extras] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] + +[metadata] +lock-version = "2.0" +python-versions = "^3.11" +content-hash = "f1cf64478bf6e4af4ac8e4642cae2924eaa1c4c50937e03d96cafaf0dc4b44ca" diff --git a/predict.py b/predict.py index db3c20e..05779fc 100644 --- a/predict.py +++ b/predict.py @@ -1,8 +1,41 @@ import os +import time +import sys +import pathlib +import subprocess +from version import MODEL_CACHE, MODEL_ID, REVISION, SAFETY_MODEL_ID, SAFETY_REVISION +from maybe_nvtx import annotate + +MODEL_FILE = os.environ["MODEL_FILE"] + + +def logtime(msg: str) -> None: + print(f"===TIME {time.time():.4f} {msg}===", file=sys.stderr) + + +check = pathlib.Path("/tmp/predict-import") +if not check.exists(): + check.touch() +else: + print("===!!!!!!predict has been imported again!!!!!!===") +if os.getenv("PGET") or not pathlib.Path("/.dockerenv").exists(): + url = f"https://storage.googleapis.com/replicate-weights/{MODEL_FILE}" + pget_proc = subprocess.Popen(["/usr/bin/pget", url, MODEL_FILE], close_fds=True) + logtime("pget launched") +else: + pget_proc = None + + from typing import List +logtime("importing torch") import torch -from cog import BasePredictor, Input, Path + +logtime("imported torch, importing cog") +from cog.predictor import BasePredictor +from cog.types import Input, Path + +logtime("imported cog, importing diffusers") from diffusers import ( StableDiffusionPipeline, PNDMScheduler, @@ -16,28 +49,60 @@ StableDiffusionSafetyChecker, ) -# MODEL_ID refers to a diffusers-compatible model on HuggingFace -# e.g. prompthero/openjourney-v2, wavymulder/Analog-Diffusion, etc -MODEL_ID = "stabilityai/stable-diffusion-2-1" -MODEL_CACHE = "diffusers-cache" -SAFETY_MODEL_ID = "CompVis/stable-diffusion-safety-checker" +logtime("imported diffusers, importing transformers") +from transformers import CLIPFeatureExtractor + + +def Input(default, **kwargs): + return default + class Predictor(BasePredictor): + @annotate("setup") def setup(self): """Load the model into memory to make running multiple predictions efficient""" print("Loading pipeline...") - safety_checker = StableDiffusionSafetyChecker.from_pretrained( - SAFETY_MODEL_ID, - cache_dir=MODEL_CACHE, - local_files_only=True, - ) - self.pipe = StableDiffusionPipeline.from_pretrained( - MODEL_ID, - safety_checker=safety_checker, - cache_dir=MODEL_CACHE, - local_files_only=True, - ).to("cuda") + logtime("predict setup") + if pget_proc: + pget_proc.wait() + else: + print("error!! no pget proc") + logtime("finished waiting for pget, loading model") + # safety_checker = StableDiffusionSafetyChecker.from_pretrained( + # SAFETY_MODEL_ID, + # cache_dir=MODEL_CACHE, + # local_files_only=True, + # torch_dtype=torch.float16, + # revision=SAFETY_REVISION, + # use_safetensors=True, + # ) + # logtime("loaded safety checker") + # # ? wasn't previously necessary + # feature_extractor = CLIPFeatureExtractor.from_pretrained( + # "openai/clip-vit-base-patch32", + # cache_dir=MODEL_CACHE, + # torch_dtype=torch.float16, + # local_files_only=True, + # use_safetensors=True, + # ) + # logtime("loaded feature extractor") + # self.pipe = StableDiffusionPipeline.from_pretrained( + # MODEL_ID, + # safety_checker=safety_checker, + # feature_extractor=feature_extractor, + # revision=REVISION, + # cache_dir=MODEL_CACHE, + # local_files_only=True, + # torch_dtype=torch.float16, + # use_safetensors=True, + # ) + # logtime("loaded pipe") + # self.pipe = self.pipe.to("cuda") + # logtime("moved pipe to cuda") + self.pipe = torch.load(MODEL_FILE) + logtime("done loading model") + @annotate("predict") @torch.inference_mode() def predict( self, @@ -88,6 +153,7 @@ def predict( ), ) -> List[Path]: """Run a single prediction on the model""" + logtime("predict predict start") if seed is None: seed = int.from_bytes(os.urandom(2), "big") print(f"Using seed: {seed}") @@ -100,6 +166,7 @@ def predict( self.pipe.scheduler = make_scheduler(scheduler, self.pipe.scheduler.config) generator = torch.Generator("cuda").manual_seed(seed) + logtime("actually doing prediction") output = self.pipe( prompt=[prompt] * num_outputs if prompt is not None else None, negative_prompt=[negative_prompt] * num_outputs @@ -111,6 +178,7 @@ def predict( generator=generator, num_inference_steps=num_inference_steps, ) + logtime("got pipe output") output_paths = [] for i, sample in enumerate(output.images): @@ -120,6 +188,7 @@ def predict( output_path = f"/tmp/out-{i}.png" sample.save(output_path) output_paths.append(Path(output_path)) + logtime("saved files") if len(output_paths) == 0: raise Exception( diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..a5d31b4 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,16 @@ +[tool.poetry] +name = "sd" +version = "0.0" +description = "" +authors = [] + +[tool.poetry.dependencies] +python = "^3.11" +accelerate = "*" +diffusers = "*" +huggingface-hub = "*" +scipy = "==1.9.3" +ftfy = "==6.1.1" +transformers = "*" +torch = { url = "https://download.pytorch.org/whl/cu118/torch-2.0.1%2Bcu118-cp311-cp311-linux_x86_64.whl" } +cog = { file = ".cog/tmp/build503992272/cog-0.0.1.dev-py3-none-any.whl" } diff --git a/safetensors_convert.py b/safetensors_convert.py new file mode 100644 index 0000000..39793b5 --- /dev/null +++ b/safetensors_convert.py @@ -0,0 +1,65 @@ +import torch +import os +from collections import defaultdict +from safetensors.torch import load_file, save_file + + +def check_file_size(sf_filename: str, pt_filename: str): + sf_size = os.stat(sf_filename).st_size + pt_size = os.stat(pt_filename).st_size + + if (sf_size - pt_size) / pt_size > 0.01: + raise RuntimeError( + f"""The file size different is more than 1%: + - {sf_filename}: {sf_size} + - {pt_filename}: {pt_size} + """ + ) + + +def shared_pointers(tensors): + ptrs = defaultdict(list) + for k, v in tensors.items(): + ptrs[v.data_ptr()].append(k) + failing = [] + for ptr, names in ptrs.items(): + if len(names) > 1: + failing.append(names) + return failing + + +def convert_file( + pt_filename: str, + sf_filename: str, +): + loaded = torch.load(pt_filename, map_location="cpu") + if "state_dict" in loaded: + loaded = loaded["state_dict"] + shared = shared_pointers(loaded) + for shared_weights in shared: + for name in shared_weights[1:]: + loaded.pop(name) + + # For tensors to be contiguous + loaded = {k: v.contiguous() for k, v in loaded.items()} + + dirname = os.path.dirname(sf_filename) + os.makedirs(dirname, exist_ok=True) + save_file(loaded, sf_filename, metadata={"format": "pt"}) + check_file_size(sf_filename, pt_filename) + reloaded = load_file(sf_filename) + for k in loaded: + pt_tensor = loaded[k] + sf_tensor = reloaded[k] + if not torch.equal(pt_tensor, sf_tensor): + raise RuntimeError(f"The output tensors do not match for key {k}") + + +if __name__ == "__main__": + import glob + for bin_fn in glob.glob('weights/**/*.bin'): + if 'text_encoder' in bin_fn: + continue + st_fn = bin_fn.replace('.bin', '.safetensors') + convert_file(bin_fn, st_fn) + os.remove(bin_fn) diff --git a/script/download-weights b/script/download-weights index 6cf233d..0bd0e3a 100755 --- a/script/download-weights +++ b/script/download-weights @@ -1,28 +1,49 @@ #!/usr/bin/env python - +import torch import os import shutil import sys - +import subprocess +from transformers import CLIPFeatureExtractor from diffusers import StableDiffusionPipeline -from diffusers.pipelines.stable_diffusion.safety_checker import \ - StableDiffusionSafetyChecker +from diffusers.pipelines.stable_diffusion.safety_checker import ( + StableDiffusionSafetyChecker, +) # append project directory to path so predict.py can be imported -sys.path.append('.') +sys.path.append(".") -from predict import MODEL_CACHE, MODEL_ID, SAFETY_MODEL_ID +from version import MODEL_CACHE, MODEL_ID, REVISION, SAFETY_MODEL_ID, SAFETY_REVISION if os.path.exists(MODEL_CACHE): shutil.rmtree(MODEL_CACHE) os.makedirs(MODEL_CACHE, exist_ok=True) -saftey_checker = StableDiffusionSafetyChecker.from_pretrained( +feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32", + cache_dir=MODEL_CACHE, + torch_dtype=torch.float16, + use_safetensors=True, +) + +safety_checker = StableDiffusionSafetyChecker.from_pretrained( SAFETY_MODEL_ID, cache_dir=MODEL_CACHE, + torch_dtype=torch.float16, + revision=SAFETY_REVISION, + use_safetensors=True, ) pipe = StableDiffusionPipeline.from_pretrained( MODEL_ID, + safety_checker=safety_checker, + feature_extractor=feature_extractor, + revision=REVISION, cache_dir=MODEL_CACHE, + torch_dtype=torch.float16, + use_safetensors=True ) +# fname = os.environ["MODEL_FILE"] # "sd-2.1-fp16-safetensors.tar" +# TOKEN = os.environ["GCP_TOKEN"] +# subprocess.run(["tar", "--create", "--file", fname, MODEL_CACHE], shell=True) +# subprocess.run(["curl", "-v", "-T", fname, "-H", f"Authorization: Bearer {TOKEN}", f"https://storage.googleapis.com/replicate-weights/{fname}"]) +# ...do something to upload here... diff --git a/torch-requirements.txt b/torch-requirements.txt new file mode 100644 index 0000000..2bd584e --- /dev/null +++ b/torch-requirements.txt @@ -0,0 +1,10 @@ +cmake==3.26.4 +filelock==3.12.2 +jinja2==3.1.2 +lit==16.0.6 +mpmath==1.3.0 +networkx==3.1 +sympy==1.12 +#torch @ https://download.pytorch.org/whl/cu118/torch-2.0.1%2Bcu118-cp311-cp311-linux_x86_64.whl +#triton==2.0.0 +typing-extensions==4.7.0 diff --git a/version.py b/version.py new file mode 100644 index 0000000..04a5ced --- /dev/null +++ b/version.py @@ -0,0 +1,8 @@ +# MODEL_ID refers to a diffusers-compatible model on HuggingFace +# e.g. prompthero/openjourney-v2, wavymulder/Analog-Diffusion, etc +MODEL_ID = "stabilityai/stable-diffusion-2-1" +MODEL_CACHE = "diffusers-cache" +SAFETY_MODEL_ID = "CompVis/stable-diffusion-safety-checker" +# these are both fp16 safetensors I made +REVISION = "refs/pr/71" +SAFETY_REVISION = "refs/pr/41"