diff --git a/docs/requirements.txt b/docs/requirements.txt index 5839d9d..d414119 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,3 @@ mkdocs==1.3.1 ; python_version >= "3.9" and python_version < "3.12" -pygments==2.17.2 ; python_version >= "3.9" and python_version < "3.12" +pygments==2.18.0 ; python_version >= "3.9" and python_version < "3.12" jinja2==3.1.4 ; python_version >= "3.9" and python_version < "3.12" diff --git a/instill/__init__.py b/instill/__init__.py index 2ecb204..86fb9db 100644 --- a/instill/__init__.py +++ b/instill/__init__.py @@ -1,4 +1,4 @@ -# pylint: disable=no-name-in-module +# pylint: disable=no-name-in-module,wrong-import-position import os import sys from importlib.metadata import PackageNotFoundError, version @@ -7,7 +7,7 @@ Logger.initialize() -sys.path.append(os.path.join(os.path.dirname(__file__), "protogen")) +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "protogen")) try: __version__ = version("instill-sdk") diff --git a/instill/helpers/__init__.py b/instill/helpers/__init__.py index bc52916..e05591a 100644 --- a/instill/helpers/__init__.py +++ b/instill/helpers/__init__.py @@ -1,15 +1,28 @@ -from instill.helpers.protobufs.parse import ( - Metadata, - construct_image_to_image_infer_response, - construct_image_to_image_metadata_response, - construct_infer_response, - construct_metadata_response, - construct_text_generation_chat_infer_response, - construct_text_generation_chat_metadata_response, - construct_text_generation_infer_response, - construct_text_generation_metadata_response, - construct_text_to_image_infer_response, - construct_text_to_image_metadata_response, - construct_visual_question_answering_infer_response, - construct_visual_question_answering_metadata_response, +# pylint: disable=no-name-in-module +from instill.helpers.protobufs.ray_pb2 import CallRequest, CallResponse +from instill.helpers.ray_io import ( + construct_custom_output, + construct_task_chat_output, + construct_task_classification_output, + construct_task_completion_output, + construct_task_detection_output, + construct_task_embedding_output, + construct_task_instance_segmentation_output, + construct_task_keypoint_output, + construct_task_ocr_output, + construct_task_semantic_segmentation_output, + construct_task_text_to_image_output, + parse_custom_input, + parse_task_chat_to_chat_input, + parse_task_chat_to_multimodal_chat_input, + parse_task_classification_to_vision_input, + parse_task_completion_to_completion_input, + parse_task_detection_to_vision_input, + parse_task_embedding_to_image_embedding_input, + parse_task_embedding_to_text_embedding_input, + parse_task_instance_segmentation_to_vision_input, + parse_task_keypoint_to_vision_input, + parse_task_ocr_to_vision_input, + parse_task_semantic_segmentation_to_vision_input, + parse_task_text_to_image_input, ) diff --git a/instill/helpers/cli.py b/instill/helpers/cli.py index fa0a101..fac12ba 100644 --- a/instill/helpers/cli.py +++ b/instill/helpers/cli.py @@ -6,6 +6,8 @@ import shutil import subprocess import tempfile +import time +import uuid import ray import yaml @@ -53,18 +55,27 @@ def cli(): required=False, ) build_parser.add_argument( + "-n", "--no-cache", help="build the image without cache", action="store_true", required=False, ) build_parser.add_argument( + "-a", "--target-arch", help="target platform architecture for the model image, default to host", default=default_platform, choices=["arm64", "amd64"], required=False, ) + build_parser.add_argument( + "-w", + "--sdk-wheel", + help="instill sdk wheel absolute path for debug purpose", + default=None, + required=False, + ) # push push_parser = subcommands.add_parser("push", help="Push model image") @@ -88,6 +99,34 @@ def cli(): required=False, ) + # run + run_parser = subcommands.add_parser("run", help="Run inference on model image") + run_parser.set_defaults(func=run) + run_parser.add_argument( + "name", + help="user and model namespace, in the format of /", + ) + run_parser.add_argument( + "-g", + "--gpu", + help="whether the model needs gpu", + action="store_true", + required=False, + ) + run_parser.add_argument( + "-t", + "--tag", + help="tag for the model image, default to `latest`", + default="latest", + required=False, + ) + run_parser.add_argument( + "-i", + "--input", + help="inference input json", + required=True, + ) + args = parser.parse_args() args.func(args) @@ -109,9 +148,9 @@ def init(_): def build(args): try: - Logger.i("[Instill Builder] Loading config file...") + Logger.i("[Instill] Loading config file...") with open("instill.yaml", "r", encoding="utf8") as f: - Logger.i("[Instill Builder] Parsing config file...") + Logger.i("[Instill] Parsing config file...") config = yaml.safe_load(f) config_check_required_fields(config) @@ -148,7 +187,6 @@ def build(args): packages_str += p + " " for p in DEFAULT_DEPENDENCIES: packages_str += p + " " - packages_str += f"instill-sdk=={instill_version}" with tempfile.TemporaryDirectory() as tmpdir: shutil.copyfile( @@ -157,9 +195,15 @@ def build(args): ) shutil.copytree(os.getcwd(), tmpdir, dirs_exist_ok=True) + if args.sdk_wheel is not None: + shutil.copyfile( + args.sdk_wheel, + f"{tmpdir}/instill_sdk-{instill_version}dev-py3-none-any.whl", + ) + target_arch_suffix = "-aarch64" if args.target_arch == "arm64" else "" - Logger.i("[Instill Builder] Building model image...") + Logger.i("[Instill] Building model image...") command = [ "docker", "buildx", @@ -176,6 +220,8 @@ def build(args): f"PACKAGES={packages_str}", "--build-arg", f"SYSTEM_PACKAGES={system_str}", + "--build-arg", + f"SDK_VERSION={instill_version}", "--platform", f"linux/{args.target_arch}", "-t", @@ -189,14 +235,14 @@ def build(args): command, check=True, ) - Logger.i(f"[Instill Builder] {args.name}:{args.tag} built") + Logger.i(f"[Instill] {args.name}:{args.tag} built") except subprocess.CalledProcessError: - Logger.e("[Instill Builder] Build failed") + Logger.e("[Instill] Build failed") except Exception as e: - Logger.e("[Instill Builder] Prepare failed") + Logger.e("[Instill] Prepare failed") Logger.e(e) finally: - Logger.i("[Instill Builder] Done") + Logger.i("[Instill] Done") def push(args): @@ -212,18 +258,99 @@ def push(args): ], check=True, ) - Logger.i("[Instill Builder] Pushing model image...") + Logger.i("[Instill] Pushing model image...") subprocess.run( ["docker", "push", f"{registry}/{args.name}:{args.tag}"], check=True ) - Logger.i(f"[Instill Builder] {registry}/{args.name}:{args.tag} pushed") + Logger.i(f"[Instill] {registry}/{args.name}:{args.tag} pushed") except subprocess.CalledProcessError: - Logger.e("[Instill Builder] Push failed") + Logger.e("[Instill] Push failed") except Exception as e: - Logger.e("[Instill Builder] Prepare failed") + Logger.e("[Instill] Prepare failed") Logger.e(e) finally: - Logger.i("[Instill Builder] Done") + Logger.i("[Instill] Done") + + +def run(args): + docker_run = False + try: + name = uuid.uuid4() + + Logger.i("[Instill] Starting model image...") + if not args.gpu: + subprocess.run( + [ + "docker", + "run", + "--rm", + "-d", + "--name", + str(name), + f"{args.name}:{args.tag}", + "serve", + "run", + "_model:entrypoint", + ], + check=True, + stdout=subprocess.DEVNULL, + ) + else: + subprocess.run( + f"docker run --rm -d --name {str(name)} --gpus all {args.name}:{args.tag} /bin/bash -c \ + \"serve build _model:entrypoint -o serve.yaml && \ + sed -i 's/app1/default/' serve.yaml && \ + sed -i 's/num_cpus: 0.0/num_gpus: 1.0/' serve.yaml && \ + serve run serve.yaml\"", + shell=True, + check=True, + stdout=subprocess.DEVNULL, + ) + docker_run = True + time.sleep(10) + Logger.i("[Instill] Deploying model...") + subprocess.run( + [ + "docker", + "exec", + str(name), + "/bin/bash", + "-c", + "until serve status --name default | grep 'RUNNING: 1' > /dev/null; do sleep 1; done;", + ], + check=True, + ) + Logger.i("[Instill] Running inference...") + subprocess.run( + [ + "docker", + "exec", + str(name), + "python", + "-m", + "instill.helpers.test", + "-i", + args.input, + ], + check=True, + ) + except subprocess.CalledProcessError: + Logger.e("[Instill] Run failed") + except Exception as e: + Logger.e("[Instill] Prepare failed") + Logger.e(e) + finally: + if docker_run: + subprocess.run( + [ + "docker", + "stop", + str(name), + ], + check=True, + stdout=subprocess.DEVNULL, + ) + Logger.i("[Instill] Done") if __name__ == "__main__": diff --git a/instill/helpers/const.py b/instill/helpers/const.py index 644ba03..8b154ba 100644 --- a/instill/helpers/const.py +++ b/instill/helpers/const.py @@ -1,83 +1,90 @@ import os -from enum import Enum -from typing import Any, Dict, List, Union +from typing import Dict, List -import numpy as np +from PIL import Image +PROMPT_ROLES = ["user", "assistant", "system"] -class DataType(Enum): - TYPE_BOOL = 1 - TYPE_UINT8 = 2 - TYPE_UINT16 = 3 - TYPE_UINT32 = 4 - TYPE_UINT64 = 5 - TYPE_INT8 = 6 - TYPE_INT16 = 7 - TYPE_INT32 = 8 - TYPE_INT64 = 9 - TYPE_FP16 = 10 - TYPE_FP32 = 11 - TYPE_FP64 = 12 - TYPE_STRING = 13 +IMAGE_INPUT_TYPE_URL = "image-url" +IMAGE_INPUT_TYPE_BASE64 = "image-base64" +EMBEDDING_FORMAT_FLOAT = "float" +EMBEDDING_FORMAT_BASE64 = "base64" +EMBEDDING_INPUT_TYPE_QUERY = "query" +EMBEDDING_INPUT_TYPE_DOCUMENT = "document" +EMBEDDING_TRUNCATE_NONE = "none" +EMBEDDING_TRUNCATE_END = "end" +EMBEDDING_TRUNCATE_START = "start" -class TextGenerationInput: - prompt = "" - prompt_images: Union[List[np.ndarray], None] = None - chat_history: Union[List[str], None] = None - system_message: Union[str, None] = None - max_new_tokens = 100 - temperature = 0.8 - top_k = 1 - random_seed = 0 - stop_words: Any = "" # Optional - extra_params: Dict[str, str] = {} +class VisionInput: + image: Image.Image -class TextToImageInput: - prompt_image: Union[np.ndarray, None] = None - prompt = "" - negative_prompt = "" - steps = 5 - guidance_scale = 7.5 - seed = 0 - samples = 1 - extra_params: Dict[str, str] = {} +class CompletionInput: + prompt: str + system_message: str = "" + max_tokens: int = 50 + n: int = 1 + seed: int = 0 + temperature: float = 0.7 + top_p: int = 1 + stream: bool = False -class ImageToImageInput: - prompt_image: Union[np.ndarray, None] = None - prompt = "" - steps = 5 - guidance_scale = 7.5 - seed = 0 - samples = 1 - extra_params: Dict[str, str] = {} +class ChatInput: + messages: List[Dict[str, str]] + max_tokens: int = 50 + n: int = 1 + seed: int = 0 + temperature: float = 0.7 + top_p: int = 1 + stream: bool = False -class TextGenerationChatInput: - prompt = "" - prompt_images: Union[List[np.ndarray], None] = None - chat_history: Union[List[str], None] = None - system_message: Union[str, None] = None - max_new_tokens = 100 - temperature = 0.8 - top_k = 1 - random_seed = 0 - stop_words: Any = "" # Optional - extra_params: Dict[str, str] = {} +class ChatMultiModalInput: + messages: List[Dict[str, str]] + prompt_images: List[List[Image.Image]] + max_tokens: int = 50 + n: int = 1 + seed: int = 0 + temperature: float = 0.7 + top_p: int = 1 + stream: bool = False -class VisualQuestionAnsweringInput: + +class TextEmbeddingInput: + contents: List[str] + format: str = EMBEDDING_FORMAT_FLOAT + dimensions: int = 512 + input_type: str = EMBEDDING_INPUT_TYPE_QUERY + truncate: str = EMBEDDING_TRUNCATE_END + + +class ImageEmbeddingInput: + images: List[Image.Image] + format: str = EMBEDDING_FORMAT_FLOAT + dimensions: int = 512 + input_type: str = EMBEDDING_INPUT_TYPE_QUERY + truncate: str = EMBEDDING_TRUNCATE_END + + +class TextToImageInput: + prompt: str + negative_prompt: str = "" + n: int = 1 + aspect_ratio: str = "1:1" + seed: int = 0 + + +class ImageToImageInput: prompt = "" - prompt_images: Union[List[np.ndarray], None] = None - chat_history: Union[List[str], None] = None - system_message: Union[str, None] = None - max_new_tokens = 100 - temperature = 0.8 - top_k = 1 - random_seed = 0 - stop_words: Any = "" # Optional + steps: int = 5 + cfg_scale: float = 7.5 + seed: int = 0 + samples: int = 1 + low_threshold = 100 + high_threshold = 200 extra_params: Dict[str, str] = {} diff --git a/instill/helpers/errors.py b/instill/helpers/errors.py index 03023bc..4d7f593 100644 --- a/instill/helpers/errors.py +++ b/instill/helpers/errors.py @@ -16,3 +16,18 @@ def __str__( self, ) -> str: return f"model config file `instill.yaml` is missing {self.field} field" + + +class InvalidInputException(Exception): + def __init__(self, field): + self.field = field + + def __str__( + self, + ) -> str: + return f"trigger request input error: {self.field}" + + +class InvalidOutputShapeException(Exception): + def __str__(self) -> str: + return "outputs length not matched" diff --git a/instill/helpers/init-templates/Dockerfile b/instill/helpers/init-templates/Dockerfile index aee34c5..eaeb9c1 100644 --- a/instill/helpers/init-templates/Dockerfile +++ b/instill/helpers/init-templates/Dockerfile @@ -18,6 +18,14 @@ RUN for package in ${PACKAGES}; do \ pip install --default-timeout=1000 --no-cache-dir $package; \ done; -WORKDIR /home/ray COPY --chown=ray:users --exclude=model.py . . -COPY --chown=ray:users model.py model.py + +ARG SDK_VERSION +RUN if [ ! -f instill_sdk-${SDK_VERSION}dev-py3-none-any.whl ]; then \ + pip install --default-timeout=1000 --no-cache-dir instill-sdk==${SDK_VERSION}; \ + else \ + pip install instill_sdk-${SDK_VERSION}dev-py3-none-any.whl; \ + fi; + +WORKDIR /home/ray +COPY --chown=ray:users model.py _model.py diff --git a/instill/helpers/protobufs/parse.py b/instill/helpers/protobufs/parse.py deleted file mode 100644 index 07ff568..0000000 --- a/instill/helpers/protobufs/parse.py +++ /dev/null @@ -1,479 +0,0 @@ -from dataclasses import dataclass -from typing import List - -from instill.helpers.protobufs.ray_pb2 import ( - ModelMetadataRequest, - ModelMetadataResponse, - RayServiceCallRequest, - RayServiceCallResponse, - InferTensor, -) - -from instill.helpers.const import DataType - - -@dataclass -class Metadata: - name: str - datatype: str - shape: list - - -def construct_metadata_response( - req: ModelMetadataRequest, - inputs: List[Metadata], - outputs: List[Metadata], -) -> ModelMetadataResponse: - resp = ModelMetadataResponse( - name=req.name, - versions=req.version, - framework="python", - inputs=[], - outputs=[], - ) - - for i in inputs: - resp.inputs.append( - ModelMetadataResponse.TensorMetadata( - name=i.name, - datatype=i.datatype, - shape=i.shape, - ) - ) - - for o in outputs: - resp.outputs.append( - ModelMetadataResponse.TensorMetadata( - name=o.name, - datatype=o.datatype, - shape=o.shape, - ) - ) - - return resp - - -def construct_infer_response( - req: RayServiceCallRequest, - outputs: List[Metadata], - raw_outputs: List[bytes], -) -> RayServiceCallResponse: - resp = RayServiceCallResponse( - model_name=req.model_name, - model_version=req.model_version, - outputs=[], - raw_output_contents=[], - ) - - for o in outputs: - resp.outputs.append( - InferTensor( - name=o.name, - datatype=o.datatype, - shape=o.shape, - ) - ) - - resp.raw_output_contents.extend(raw_outputs) - - return resp - - -def construct_text_generation_metadata_response( - req: ModelMetadataRequest, -) -> ModelMetadataResponse: - return construct_metadata_response( - req=req, - inputs=[ - Metadata( - name="prompt", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="prompt_images", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="chat_history", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="system_message", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="max_new_tokens", - datatype=str(DataType.TYPE_UINT32.name), - shape=[1], - ), - Metadata( - name="temperature", - datatype=str(DataType.TYPE_FP32.name), - shape=[1], - ), - Metadata( - name="top_k", - datatype=str(DataType.TYPE_UINT32.name), - shape=[1], - ), - Metadata( - name="random_seed", - datatype=str(DataType.TYPE_UINT64.name), - shape=[1], - ), - Metadata( - name="extra_params", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - ], - outputs=[ - Metadata( - name="text", - datatype=str(DataType.TYPE_STRING.name), - shape=[-1, -1], - ), - ], - ) - - -def construct_text_generation_infer_response( - req: RayServiceCallRequest, - shape: list, - raw_outputs: List[bytes], -): - return construct_infer_response( - req=req, - outputs=[ - Metadata( - name="text", - datatype=str(DataType.TYPE_STRING.name), - shape=shape, - ) - ], - raw_outputs=raw_outputs, - ) - - -def construct_text_generation_chat_metadata_response( - req: ModelMetadataRequest, -) -> ModelMetadataResponse: - return construct_metadata_response( - req=req, - inputs=[ - Metadata( - name="prompt", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="prompt_images", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="chat_history", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="system_message", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="max_new_tokens", - datatype=str(DataType.TYPE_UINT32.name), - shape=[1], - ), - Metadata( - name="temperature", - datatype=str(DataType.TYPE_FP32.name), - shape=[1], - ), - Metadata( - name="top_k", - datatype=str(DataType.TYPE_UINT32.name), - shape=[1], - ), - Metadata( - name="random_seed", - datatype=str(DataType.TYPE_UINT64.name), - shape=[1], - ), - Metadata( - name="extra_params", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - ], - outputs=[ - Metadata( - name="text", - datatype=str(DataType.TYPE_STRING.name), - shape=[-1, -1], - ), - ], - ) - - -def construct_text_generation_chat_infer_response( - req: RayServiceCallRequest, - shape: list, - raw_outputs: List[bytes], -): - return construct_infer_response( - req=req, - outputs=[ - Metadata( - name="text", - datatype=str(DataType.TYPE_STRING.name), - shape=shape, - ) - ], - raw_outputs=raw_outputs, - ) - - -def construct_text_to_image_metadata_response( - req: ModelMetadataRequest, -): - return construct_metadata_response( - req=req, - inputs=[ - Metadata( - name="prompt", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="negative_prompt", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="prompt_image", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="samples", - datatype=str(DataType.TYPE_INT32.name), - shape=[1], - ), - Metadata( - name="scheduler", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="steps", - datatype=str(DataType.TYPE_INT32.name), - shape=[1], - ), - Metadata( - name="guidance_scale", - datatype=str(DataType.TYPE_FP32.name), - shape=[1], - ), - Metadata( - name="seed", - datatype=str(DataType.TYPE_INT64.name), - shape=[1], - ), - Metadata( - name="extra_params", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - ], - outputs=[ - Metadata( - name="images", - datatype=str(DataType.TYPE_FP32.name), - shape=[-1, -1, -1, -1], - ), - ], - ) - - -def construct_text_to_image_infer_response( - req: RayServiceCallRequest, - shape: list, - raw_outputs: List[bytes], -): - return construct_infer_response( - req=req, - outputs=[ - Metadata( - name="images", - datatype=str(DataType.TYPE_FP32.name), - shape=shape, - ) - ], - raw_outputs=raw_outputs, - ) - - -def construct_image_to_image_metadata_response( - req: ModelMetadataRequest, -): - return construct_metadata_response( - req=req, - inputs=[ - Metadata( - name="prompt", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="negative_prompt", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="prompt_image", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="samples", - datatype=str(DataType.TYPE_INT32.name), - shape=[1], - ), - Metadata( - name="scheduler", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="steps", - datatype=str(DataType.TYPE_INT32.name), - shape=[1], - ), - Metadata( - name="guidance_scale", - datatype=str(DataType.TYPE_FP32.name), - shape=[1], - ), - Metadata( - name="seed", - datatype=str(DataType.TYPE_INT64.name), - shape=[1], - ), - Metadata( - name="extra_params", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - ], - outputs=[ - Metadata( - name="images", - datatype=str(DataType.TYPE_FP32.name), - shape=[-1, -1, -1, -1], - ), - ], - ) - - -def construct_image_to_image_infer_response( - req: RayServiceCallRequest, - shape: list, - raw_outputs: List[bytes], -): - return construct_infer_response( - req=req, - outputs=[ - Metadata( - name="images", - datatype=str(DataType.TYPE_FP32.name), - shape=shape, - ) - ], - raw_outputs=raw_outputs, - ) - - -def construct_visual_question_answering_metadata_response( - req: ModelMetadataRequest, -): - return construct_metadata_response( - req=req, - inputs=[ - Metadata( - name="prompt", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="prompt_images", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="chat_history", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="system_message", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - Metadata( - name="max_new_tokens", - datatype=str(DataType.TYPE_UINT32.name), - shape=[1], - ), - Metadata( - name="temperature", - datatype=str(DataType.TYPE_FP32.name), - shape=[1], - ), - Metadata( - name="top_k", - datatype=str(DataType.TYPE_UINT32.name), - shape=[1], - ), - Metadata( - name="random_seed", - datatype=str(DataType.TYPE_UINT64.name), - shape=[1], - ), - Metadata( - name="extra_params", - datatype=str(DataType.TYPE_STRING.name), - shape=[1], - ), - ], - outputs=[ - Metadata( - name="text", - datatype=str(DataType.TYPE_STRING.name), - shape=[-1, -1], - ), - ], - ) - - -def construct_visual_question_answering_infer_response( - req: RayServiceCallRequest, - shape: list, - raw_outputs: List[bytes], -): - return construct_infer_response( - req=req, - outputs=[ - Metadata( - name="text", - datatype=str(DataType.TYPE_STRING.name), - shape=shape, - ) - ], - raw_outputs=raw_outputs, - ) diff --git a/instill/helpers/protobufs/ray_pb2.py b/instill/helpers/protobufs/ray_pb2.py index 0fbd815..c3541df 100644 --- a/instill/helpers/protobufs/ray_pb2.py +++ b/instill/helpers/protobufs/ray_pb2.py @@ -1,6 +1,7 @@ # -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: ray.proto +# Protobuf Python Version: 4.25.1 """Generated protocol buffer code.""" from google.protobuf import descriptor as _descriptor from google.protobuf import descriptor_pool as _descriptor_pool @@ -11,30 +12,21 @@ _sym_db = _symbol_database.Default() +from google.protobuf import struct_pb2 as google_dot_protobuf_dot_struct__pb2 -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\tray.proto\x12\tray.serve\"5\n\x14ModelMetadataRequest\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07version\x18\x02 \x01(\t\"<\n\x0bInferTensor\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x10\n\x08\x64\x61tatype\x18\x02 \x01(\t\x12\r\n\x05shape\x18\x03 \x03(\x03\"\x8e\x02\n\x15ModelMetadataResponse\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x10\n\x08versions\x18\x02 \x03(\t\x12\x11\n\tframework\x18\x03 \x01(\t\x12?\n\x06inputs\x18\x04 \x03(\x0b\x32/.ray.serve.ModelMetadataResponse.TensorMetadata\x12@\n\x07outputs\x18\x05 \x03(\x0b\x32/.ray.serve.ModelMetadataResponse.TensorMetadata\x1a?\n\x0eTensorMetadata\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x10\n\x08\x64\x61tatype\x18\x02 \x01(\t\x12\r\n\x05shape\x18\x03 \x03(\x03\"\x80\x02\n\x15RayServiceCallRequest\x12\x12\n\nmodel_name\x18\x01 \x01(\t\x12\x15\n\rmodel_version\x18\x02 \x01(\t\x12&\n\x06inputs\x18\x03 \x03(\x0b\x32\x16.ray.serve.InferTensor\x12L\n\x07outputs\x18\x06 \x03(\x0b\x32;.ray.serve.RayServiceCallRequest.InferRequestedOutputTensor\x12\x1a\n\x12raw_input_contents\x18\x07 \x03(\x0c\x1a*\n\x1aInferRequestedOutputTensor\x12\x0c\n\x04name\x18\x01 \x01(\t\"\x89\x01\n\x16RayServiceCallResponse\x12\x12\n\nmodel_name\x18\x01 \x01(\t\x12\x15\n\rmodel_version\x18\x02 \x01(\t\x12\'\n\x07outputs\x18\x05 \x03(\x0b\x32\x16.ray.serve.InferTensor\x12\x1b\n\x13raw_output_contents\x18\x06 \x03(\x0c\x32\xb5\x01\n\nRayService\x12T\n\rModelMetadata\x12\x1f.ray.serve.ModelMetadataRequest\x1a .ray.serve.ModelMetadataResponse\"\x00\x12Q\n\x08__call__\x12 .ray.serve.RayServiceCallRequest\x1a!.ray.serve.RayServiceCallResponse\"\x00\x42\x07Z\x05./rayb\x06proto3') +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\tray.proto\x12\x06ray.v1\x1a\x1cgoogle/protobuf/struct.proto\";\n\x0b\x43\x61llRequest\x12,\n\x0btask_inputs\x18\x01 \x03(\x0b\x32\x17.google.protobuf.Struct\"=\n\x0c\x43\x61llResponse\x12-\n\x0ctask_outputs\x18\x01 \x03(\x0b\x32\x17.google.protobuf.Struct2C\n\nRayService\x12\x35\n\x08__call__\x12\x13.ray.v1.CallRequest\x1a\x14.ray.v1.CallResponseB\rZ\x0b./rayserverb\x06proto3') _globals = globals() _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'ray_pb2', _globals) if _descriptor._USE_C_DESCRIPTORS == False: - DESCRIPTOR._options = None - DESCRIPTOR._serialized_options = b'Z\005./ray' - _globals['_MODELMETADATAREQUEST']._serialized_start=24 - _globals['_MODELMETADATAREQUEST']._serialized_end=77 - _globals['_INFERTENSOR']._serialized_start=79 - _globals['_INFERTENSOR']._serialized_end=139 - _globals['_MODELMETADATARESPONSE']._serialized_start=142 - _globals['_MODELMETADATARESPONSE']._serialized_end=412 - _globals['_MODELMETADATARESPONSE_TENSORMETADATA']._serialized_start=349 - _globals['_MODELMETADATARESPONSE_TENSORMETADATA']._serialized_end=412 - _globals['_RAYSERVICECALLREQUEST']._serialized_start=415 - _globals['_RAYSERVICECALLREQUEST']._serialized_end=671 - _globals['_RAYSERVICECALLREQUEST_INFERREQUESTEDOUTPUTTENSOR']._serialized_start=629 - _globals['_RAYSERVICECALLREQUEST_INFERREQUESTEDOUTPUTTENSOR']._serialized_end=671 - _globals['_RAYSERVICECALLRESPONSE']._serialized_start=674 - _globals['_RAYSERVICECALLRESPONSE']._serialized_end=811 - _globals['_RAYSERVICE']._serialized_start=814 - _globals['_RAYSERVICE']._serialized_end=995 + _globals['DESCRIPTOR']._options = None + _globals['DESCRIPTOR']._serialized_options = b'Z\013./rayserver' + _globals['_CALLREQUEST']._serialized_start=51 + _globals['_CALLREQUEST']._serialized_end=110 + _globals['_CALLRESPONSE']._serialized_start=112 + _globals['_CALLRESPONSE']._serialized_end=173 + _globals['_RAYSERVICE']._serialized_start=175 + _globals['_RAYSERVICE']._serialized_end=242 # @@protoc_insertion_point(module_scope) diff --git a/instill/helpers/protobufs/ray_pb2.pyi b/instill/helpers/protobufs/ray_pb2.pyi index 4a67105..c54d796 100644 --- a/instill/helpers/protobufs/ray_pb2.pyi +++ b/instill/helpers/protobufs/ray_pb2.pyi @@ -1,3 +1,4 @@ +from google.protobuf import struct_pb2 as _struct_pb2 from google.protobuf.internal import containers as _containers from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message @@ -5,74 +6,14 @@ from typing import ClassVar as _ClassVar, Iterable as _Iterable, Mapping as _Map DESCRIPTOR: _descriptor.FileDescriptor -class ModelMetadataRequest(_message.Message): - __slots__ = ["name", "version"] - NAME_FIELD_NUMBER: _ClassVar[int] - VERSION_FIELD_NUMBER: _ClassVar[int] - name: str - version: str - def __init__(self, name: _Optional[str] = ..., version: _Optional[str] = ...) -> None: ... +class CallRequest(_message.Message): + __slots__ = ("task_inputs",) + TASK_INPUTS_FIELD_NUMBER: _ClassVar[int] + task_inputs: _containers.RepeatedCompositeFieldContainer[_struct_pb2.Struct] + def __init__(self, task_inputs: _Optional[_Iterable[_Union[_struct_pb2.Struct, _Mapping]]] = ...) -> None: ... -class InferTensor(_message.Message): - __slots__ = ["name", "datatype", "shape"] - NAME_FIELD_NUMBER: _ClassVar[int] - DATATYPE_FIELD_NUMBER: _ClassVar[int] - SHAPE_FIELD_NUMBER: _ClassVar[int] - name: str - datatype: str - shape: _containers.RepeatedScalarFieldContainer[int] - def __init__(self, name: _Optional[str] = ..., datatype: _Optional[str] = ..., shape: _Optional[_Iterable[int]] = ...) -> None: ... - -class ModelMetadataResponse(_message.Message): - __slots__ = ["name", "versions", "framework", "inputs", "outputs"] - class TensorMetadata(_message.Message): - __slots__ = ["name", "datatype", "shape"] - NAME_FIELD_NUMBER: _ClassVar[int] - DATATYPE_FIELD_NUMBER: _ClassVar[int] - SHAPE_FIELD_NUMBER: _ClassVar[int] - name: str - datatype: str - shape: _containers.RepeatedScalarFieldContainer[int] - def __init__(self, name: _Optional[str] = ..., datatype: _Optional[str] = ..., shape: _Optional[_Iterable[int]] = ...) -> None: ... - NAME_FIELD_NUMBER: _ClassVar[int] - VERSIONS_FIELD_NUMBER: _ClassVar[int] - FRAMEWORK_FIELD_NUMBER: _ClassVar[int] - INPUTS_FIELD_NUMBER: _ClassVar[int] - OUTPUTS_FIELD_NUMBER: _ClassVar[int] - name: str - versions: _containers.RepeatedScalarFieldContainer[str] - framework: str - inputs: _containers.RepeatedCompositeFieldContainer[ModelMetadataResponse.TensorMetadata] - outputs: _containers.RepeatedCompositeFieldContainer[ModelMetadataResponse.TensorMetadata] - def __init__(self, name: _Optional[str] = ..., versions: _Optional[_Iterable[str]] = ..., framework: _Optional[str] = ..., inputs: _Optional[_Iterable[_Union[ModelMetadataResponse.TensorMetadata, _Mapping]]] = ..., outputs: _Optional[_Iterable[_Union[ModelMetadataResponse.TensorMetadata, _Mapping]]] = ...) -> None: ... - -class RayServiceCallRequest(_message.Message): - __slots__ = ["model_name", "model_version", "inputs", "outputs", "raw_input_contents"] - class InferRequestedOutputTensor(_message.Message): - __slots__ = ["name"] - NAME_FIELD_NUMBER: _ClassVar[int] - name: str - def __init__(self, name: _Optional[str] = ...) -> None: ... - MODEL_NAME_FIELD_NUMBER: _ClassVar[int] - MODEL_VERSION_FIELD_NUMBER: _ClassVar[int] - INPUTS_FIELD_NUMBER: _ClassVar[int] - OUTPUTS_FIELD_NUMBER: _ClassVar[int] - RAW_INPUT_CONTENTS_FIELD_NUMBER: _ClassVar[int] - model_name: str - model_version: str - inputs: _containers.RepeatedCompositeFieldContainer[InferTensor] - outputs: _containers.RepeatedCompositeFieldContainer[RayServiceCallRequest.InferRequestedOutputTensor] - raw_input_contents: _containers.RepeatedScalarFieldContainer[bytes] - def __init__(self, model_name: _Optional[str] = ..., model_version: _Optional[str] = ..., inputs: _Optional[_Iterable[_Union[InferTensor, _Mapping]]] = ..., outputs: _Optional[_Iterable[_Union[RayServiceCallRequest.InferRequestedOutputTensor, _Mapping]]] = ..., raw_input_contents: _Optional[_Iterable[bytes]] = ...) -> None: ... - -class RayServiceCallResponse(_message.Message): - __slots__ = ["model_name", "model_version", "outputs", "raw_output_contents"] - MODEL_NAME_FIELD_NUMBER: _ClassVar[int] - MODEL_VERSION_FIELD_NUMBER: _ClassVar[int] - OUTPUTS_FIELD_NUMBER: _ClassVar[int] - RAW_OUTPUT_CONTENTS_FIELD_NUMBER: _ClassVar[int] - model_name: str - model_version: str - outputs: _containers.RepeatedCompositeFieldContainer[InferTensor] - raw_output_contents: _containers.RepeatedScalarFieldContainer[bytes] - def __init__(self, model_name: _Optional[str] = ..., model_version: _Optional[str] = ..., outputs: _Optional[_Iterable[_Union[InferTensor, _Mapping]]] = ..., raw_output_contents: _Optional[_Iterable[bytes]] = ...) -> None: ... +class CallResponse(_message.Message): + __slots__ = ("task_outputs",) + TASK_OUTPUTS_FIELD_NUMBER: _ClassVar[int] + task_outputs: _containers.RepeatedCompositeFieldContainer[_struct_pb2.Struct] + def __init__(self, task_outputs: _Optional[_Iterable[_Union[_struct_pb2.Struct, _Mapping]]] = ...) -> None: ... diff --git a/instill/helpers/protobufs/ray_pb2_grpc.py b/instill/helpers/protobufs/ray_pb2_grpc.py index cb1dec7..01a1ae7 100644 --- a/instill/helpers/protobufs/ray_pb2_grpc.py +++ b/instill/helpers/protobufs/ray_pb2_grpc.py @@ -15,15 +15,10 @@ def __init__(self, channel): Args: channel: A grpc.Channel. """ - self.ModelMetadata = channel.unary_unary( - '/ray.serve.RayService/ModelMetadata', - request_serializer=ray__pb2.ModelMetadataRequest.SerializeToString, - response_deserializer=ray__pb2.ModelMetadataResponse.FromString, - ) self.__call__ = channel.unary_unary( - '/ray.serve.RayService/__call__', - request_serializer=ray__pb2.RayServiceCallRequest.SerializeToString, - response_deserializer=ray__pb2.RayServiceCallResponse.FromString, + '/ray.v1.RayService/__call__', + request_serializer=ray__pb2.CallRequest.SerializeToString, + response_deserializer=ray__pb2.CallResponse.FromString, ) @@ -31,16 +26,8 @@ class RayServiceServicer(object): """Ray service for internal process """ - def ModelMetadata(self, request, context): - """ModelMetadata method receives a ModelMetadataRequest message and - returns a ModelMetadataResponse - """ - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details('Method not implemented!') - raise NotImplementedError('Method not implemented!') - def __call__(self, request, context): - """__call__ method is the defaut trigger entry for ray deployment + """Trigger method is the defaut trigger entry for ray deployment """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') @@ -49,19 +36,14 @@ def __call__(self, request, context): def add_RayServiceServicer_to_server(servicer, server): rpc_method_handlers = { - 'ModelMetadata': grpc.unary_unary_rpc_method_handler( - servicer.ModelMetadata, - request_deserializer=ray__pb2.ModelMetadataRequest.FromString, - response_serializer=ray__pb2.ModelMetadataResponse.SerializeToString, - ), '__call__': grpc.unary_unary_rpc_method_handler( servicer.__call__, - request_deserializer=ray__pb2.RayServiceCallRequest.FromString, - response_serializer=ray__pb2.RayServiceCallResponse.SerializeToString, + request_deserializer=ray__pb2.CallRequest.FromString, + response_serializer=ray__pb2.CallResponse.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( - 'ray.serve.RayService', rpc_method_handlers) + 'ray.v1.RayService', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,)) @@ -70,23 +52,6 @@ class RayService(object): """Ray service for internal process """ - @staticmethod - def ModelMetadata(request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None): - return grpc.experimental.unary_unary(request, target, '/ray.serve.RayService/ModelMetadata', - ray__pb2.ModelMetadataRequest.SerializeToString, - ray__pb2.ModelMetadataResponse.FromString, - options, channel_credentials, - insecure, call_credentials, compression, wait_for_ready, timeout, metadata) - @staticmethod def __call__(request, target, @@ -98,8 +63,8 @@ def __call__(request, wait_for_ready=None, timeout=None, metadata=None): - return grpc.experimental.unary_unary(request, target, '/ray.serve.RayService/__call__', - ray__pb2.RayServiceCallRequest.SerializeToString, - ray__pb2.RayServiceCallResponse.FromString, + return grpc.experimental.unary_unary(request, target, '/ray.v1.RayService/__call__', + ray__pb2.CallRequest.SerializeToString, + ray__pb2.CallResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) diff --git a/instill/helpers/ray_io.py b/instill/helpers/ray_io.py index c63f788..3d8d5ba 100644 --- a/instill/helpers/ray_io.py +++ b/instill/helpers/ray_io.py @@ -1,706 +1,1236 @@ +# pylint: disable=no-member,no-name-in-module, inconsistent-return-statements, unused-import import base64 import io -import json -import struct -from json.decoder import JSONDecodeError -from typing import List +import re +from typing import Any, Dict, List, Union import numpy as np +import requests +from google.protobuf import json_format, struct_pb2 from PIL import Image - +from starlette.requests import Request + +import instill.protogen.model.model.v1alpha.common_pb2 as commonpb +import instill.protogen.model.model.v1alpha.model_pb2 as modelpb +import instill.protogen.model.model.v1alpha.task_classification_pb2 as classificationpb +import instill.protogen.model.model.v1alpha.task_detection_pb2 as detectionpb +import instill.protogen.model.model.v1alpha.task_image_to_image_pb2 as imagetoimagepb +import instill.protogen.model.model.v1alpha.task_instance_segmentation_pb2 as instancesegmentationpb +import instill.protogen.model.model.v1alpha.task_keypoint_pb2 as keypointpb +import instill.protogen.model.model.v1alpha.task_ocr_pb2 as ocrpb +import instill.protogen.model.model.v1alpha.task_semantic_segmentation_pb2 as semanticsegmentationpb +import instill.protogen.model.model.v1alpha.task_text_generation_chat_pb2 as textgenerationchatpb +import instill.protogen.model.model.v1alpha.task_text_generation_pb2 as textgenerationpb +import instill.protogen.model.model.v1alpha.task_text_to_image_pb2 as texttoimagepb +import instill.protogen.model.model.v1alpha.task_visual_question_answering_pb2 as visualquestionansweringpb from instill.helpers.const import ( + IMAGE_INPUT_TYPE_BASE64, + IMAGE_INPUT_TYPE_URL, + PROMPT_ROLES, + ChatInput, + ChatMultiModalInput, + CompletionInput, + ImageEmbeddingInput, ImageToImageInput, - TextGenerationChatInput, - TextGenerationInput, + TextEmbeddingInput, TextToImageInput, - VisualQuestionAnsweringInput, + VisionInput, ) +from instill.helpers.errors import InvalidInputException, InvalidOutputShapeException +from instill.helpers.protobufs.ray_pb2 import CallRequest, CallResponse + + +def base64_to_pil_image(base64_str): + return Image.open( + io.BytesIO( + base64.b64decode( + re.sub( + "^data:image/.+;base64,", + "", + base64_str, + ) + ) + ) + ) + + +def url_to_pil_image(url): + resp = requests.get(url, timeout=10) + resp.raise_for_status() + return Image.open(io.BytesIO(resp.content)) + + +def snake_to_lower_camel(name): + """Convert snake_case to lowerCamelCase.""" + components = name.split("_") + return components[0] + "".join(x.title() for x in components[1:]) + + +def dict_to_struct(dict_data): + """Convert Dict to Struct""" + struct_pb = struct_pb2.Struct() + json_format.ParseDict(dict_data, struct_pb) + + return struct_pb + + +async def _parse_vision_task_to_vision_input( + request: Union[CallRequest, Request], +) -> List[VisionInput]: + + def extract_data(data: dict) -> VisionInput: + image_type = data["type"] + + inp = VisionInput() + if image_type == IMAGE_INPUT_TYPE_URL: + inp.image = url_to_pil_image(data[image_type]) + elif image_type == IMAGE_INPUT_TYPE_BASE64: + inp.image = base64_to_pil_image(data[image_type]) + else: + raise InvalidInputException("input image type not supported") + + return inp + + # http test input + if isinstance(request, Request): + data: dict = await request.json() + + if "type" not in data: + data["type"] = IMAGE_INPUT_TYPE_URL + + return [extract_data(data)] + + input_list = [] + for task_input in request.task_inputs: + task_input_dict = json_format.MessageToDict(task_input) + + data = task_input_dict["data"] + + input_list.append(extract_data(data)) + + return input_list + + +async def parse_task_classification_to_vision_input( + request: Union[CallRequest, Request], +) -> List[VisionInput]: + + return await _parse_vision_task_to_vision_input(request) + + +def construct_task_classification_output( + request: Union[CallRequest, Request], + categories: List[str], + scores: List[float], +) -> Union[CallResponse, List]: + if not len(categories) == len(scores): + raise InvalidOutputShapeException + + task_outputs = [] + for category, score in zip(categories, scores): + data = {"category": str(category), "score": float(score)} + + if isinstance(request, Request): + task_outputs.append({"data": data}) + else: + task_outputs.append(dict_to_struct({"data": data})) + + if isinstance(request, Request): + return task_outputs + + return CallResponse(task_outputs=task_outputs) -def serialize_byte_tensor(input_tensor): +async def parse_task_detection_to_vision_input( + request: Union[CallRequest, Request], +) -> List[VisionInput]: + + return await _parse_vision_task_to_vision_input(request) + + +def construct_task_detection_output( + request: Union[CallRequest, Request], + categories: List[List[str]], + scores: List[List[float]], + bounding_boxes: List[List[tuple]], +) -> Union[CallResponse, List]: + """Construct trigger output for detection task + + Args: + categories (List[List[str]]): for each image input, the list of detected object's category + scores (List[List[float]]): for each image input, the list of detected object's score + bounding_boxes (List[List[tuple]]): for each image input, the list of detected object's bbox, with the format + (top left x, top left y, width, height) """ - Serializes a bytes tensor into a flat numpy array of length prepended - bytes. The numpy array should use dtype of np.object_. For np.bytes_, - numpy will remove trailing zeros at the end of byte sequence and because - of this it should be avoided. - Parameters - ---------- - input_tensor : np.array - The bytes tensor to serialize. - Returns - ------- - serialized_bytes_tensor : np.array - The 1-D numpy array of type uint8 containing the serialized bytes in 'C' order. - Raises - ------ - InferenceServerException - If unable to serialize the given tensor. + if not len(categories) == len(scores) == len(bounding_boxes): + raise InvalidOutputShapeException + + task_outputs = [] + for category, score, bbox in zip(categories, scores, bounding_boxes): + data = {} + objects = [] + for cat, sc, bb in zip(category, score, bbox): + bb_dict = { + "top": bb[1], + "left": bb[0], + "width": bb[2], + "height": bb[3], + } + objects.append( + {"category": str(cat), "score": float(sc), "bounding-box": bb_dict} + ) + + data["objects"] = objects + + if isinstance(request, Request): + task_outputs.append({"data": data}) + else: + task_outputs.append(dict_to_struct({"data": data})) + + if isinstance(request, Request): + return task_outputs + + return CallResponse(task_outputs=task_outputs) + + +async def parse_task_ocr_to_vision_input( + request: Union[CallRequest, Request], +) -> List[VisionInput]: + + return await _parse_vision_task_to_vision_input(request) + + +def construct_task_ocr_output( + request: Union[CallRequest, Request], + texts: List[List[str]], + scores: List[List[float]], + bounding_boxes: List[List[tuple]], +) -> Union[CallResponse, List]: + """Construct trigger output for ocr task + + Args: + texts (List[List[str]]): for each image input, the list of detected text + scores (List[List[float]]): for each image input, the list of detected text's score + bounding_boxes (List[List[tuple]]): for each image input, the list of detected text's bbox, with the format + (top left x, top left y, width, height) """ + if not len(texts) == len(scores) == len(bounding_boxes): + raise InvalidOutputShapeException + + task_outputs = [] + for text, score, bbox in zip(texts, scores, bounding_boxes): + data = {} + objects = [] + for txt, sc, bb in zip(text, score, bbox): + bb_dict = { + "top": bb[1], + "left": bb[0], + "width": bb[2], + "height": bb[3], + } + objects.append( + {"text": str(txt), "score": float(sc), "bounding-box": bb_dict} + ) + + data["objects"] = objects + + if isinstance(request, Request): + task_outputs.append({"data": data}) + else: + task_outputs.append(dict_to_struct({"data": data})) + + if isinstance(request, Request): + return task_outputs + + return CallResponse(task_outputs=task_outputs) + + +async def parse_task_instance_segmentation_to_vision_input( + request: Union[CallRequest, Request], +) -> List[VisionInput]: + + return await _parse_vision_task_to_vision_input(request) + + +def construct_task_instance_segmentation_output( + request: Union[CallRequest, Request], + rles: List[List[str]], + categories: List[List[str]], + scores: List[List[float]], + bounding_boxes: List[List[tuple]], +) -> Union[CallResponse, List]: + """Construct trigger output for instance segmentation task + + Args: + rles (List[List[str]]): for each image input, the list of detected object's rle + categories (List[List[str]]): for each image input, the list of detected object's category + scores (List[List[float]]): for each image input, the list of detected text's score + bounding_boxes (List[List[tuple]]): for each image input, the list of detected text's bbox, with the format + (top left x, top left y, width, height) + """ + if not len(rles) == len(categories) == len(scores) == len(bounding_boxes): + raise InvalidOutputShapeException + + task_outputs = [] + for rle, category, score, bbox in zip(rles, categories, scores, bounding_boxes): + data = {} + objects = [] + for r, cat, sc, bb in zip(rle, category, score, bbox): + bb_dict = { + "top": bb[1], + "left": bb[0], + "width": bb[2], + "height": bb[3], + } + objects.append( + { + "rle": str(r), + "category": str(cat), + "score": float(sc), + "bounding-box": bb_dict, + } + ) + + data["objects"] = objects + + if isinstance(request, Request): + task_outputs.append({"data": data}) + else: + task_outputs.append(dict_to_struct({"data": data})) + + if isinstance(request, Request): + return task_outputs + + return CallResponse(task_outputs=task_outputs) + + +async def parse_task_semantic_segmentation_to_vision_input( + request: Union[CallRequest, Request], +) -> List[VisionInput]: + + return await _parse_vision_task_to_vision_input(request) + + +def construct_task_semantic_segmentation_output( + request: Union[CallRequest, Request], + rles: List[List[str]], + categories: List[List[str]], +) -> Union[CallResponse, List]: + """Construct trigger output for semantic segmentation task + + Args: + rles (List[List[str]]): for each image input, the list of detected object's rle + categories (List[List[str]]): for each image input, the list of detected object's category + (top, left, width, height) + """ + if not len(rles) == len(categories): + raise InvalidOutputShapeException - if input_tensor.size == 0: - return () - - # If the input is a tensor of string/bytes objects, then must flatten those - # into a 1-dimensional array containing the 4-byte byte size followed by the - # actual element bytes. All elements are concatenated together in "C" order. - if (input_tensor.dtype == np.object_) or (input_tensor.dtype.type == np.bytes_): - flattened_ls: list = [] - for obj in np.nditer(input_tensor, flags=["refs_ok"], order="C"): - # If directly passing bytes to BYTES type, - # don't convert it to str as Python will encode the - # bytes which may distort the meaning - assert isinstance(obj, np.ndarray) - if input_tensor.dtype == np.object_: - if isinstance(obj.item(), bytes): - s = obj.item() - else: - s = str(obj.item()).encode("utf-8") - else: - s = obj.item() - flattened_ls.append(struct.pack(" List[VisionInput]: + + return await _parse_vision_task_to_vision_input(request) + + +def construct_task_keypoint_output( + request: Union[CallRequest, Request], + keypoints: List[List[List[tuple]]], + scores: List[List[float]], + bounding_boxes: List[List[tuple]], +) -> Union[CallResponse, List]: + """Construct trigger output for keypoint task + Args: + keypoints (List[List[List[str]]]): for each image input, the list of detected object's keypoints, + with the format (x_coordinate, y_coordinate, visibility) + scores (List[List[float]]): for each image input, the list of detected object's score + bounding_boxes (List[List[tuple]]): for each image input, the list of detected object's bbox, with the format + (top left x, top left y, width, height) """ - strs = [] - offset = 0 - val_buf = encoded_tensor - while offset < len(val_buf): - l = struct.unpack_from(" TextGenerationInput: - text_generation_input = TextGenerationInput() - - for i, b_input_tensor in zip(request.inputs, request.raw_input_contents): - input_name = i.name - - if input_name == "prompt": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - text_generation_input.prompt = str(input_tensor[0].decode("utf-8")) - print( - f"[DEBUG] input `prompt` type\ - ({type(text_generation_input.prompt)}): {text_generation_input.prompt}" - ) - if input_name == "prompt_images": - input_tensors = deserialize_bytes_tensor(b_input_tensor) - images = [] - for enc in input_tensors: - if len(enc) == 0: - continue - try: - enc_json = json.loads(str(enc.decode("utf-8"))) - if len(enc_json) == 0: - continue - decoded_enc = enc_json[0] - except JSONDecodeError: - print("[DEBUG] WARNING `enc_json` parsing faield!") - # pil_img = Image.open(io.BytesIO(enc.astype(bytes))) # RGB - pil_img = Image.open(io.BytesIO(base64.b64decode(decoded_enc))) - - image = np.array(pil_img) - if len(image.shape) == 2: # gray image - raise ValueError( - f"The image shape with {image.shape} is " - f"not in acceptable" - ) - images.append(image) - # TODO: check wethere there are issues in batch size dimention - text_generation_input.prompt_images = images - print( - "[DEBUG] input `prompt_images` type" - f"({type(text_generation_input.prompt_images)}): " - f"{text_generation_input.prompt_images}" - ) + if not len(keypoints) == len(scores) == len(bounding_boxes): + raise InvalidOutputShapeException + + task_outputs = [] + for keypoint, score, bbox in zip(keypoints, scores, bounding_boxes): + data = {} + objects = [] + for kps, sc, bb in zip(keypoint, score, bbox): + point_list = [] + for kp in kps: + point_list.append({"x": kp[0], "y": kp[1], "v": kp[2]}) + + bb_dict = { + "top": bb[1], + "left": bb[0], + "width": bb[2], + "height": bb[3], + } + objects.append( + {"keypoints": point_list, "score": float(sc), "bounding-box": bb_dict} + ) + + data["objects"] = objects + + if isinstance(request, Request): + task_outputs.append({"data": data}) + else: + task_outputs.append(dict_to_struct({"data": data})) + + if isinstance(request, Request): + return task_outputs + + return CallResponse(task_outputs=task_outputs) + + +async def parse_task_completion_to_completion_input( + request: Union[CallRequest, Request], +) -> List[CompletionInput]: + + # http test input + if isinstance(request, Request): + test_data: dict = await request.json() + + inp = CompletionInput() + inp.prompt = test_data["prompt"] + return [inp] + + input_list = [] + for task_input in request.task_inputs: + task_input_dict = json_format.MessageToDict(task_input) + + data = task_input_dict["data"] + parameter = ( + task_input_dict["parameter"] if "parameter" in task_input_dict else {} + ) + + inp = CompletionInput() + + inp.prompt = data["prompt"] + + # system message + if "system-message" in data: + inp.system_message = data["system-message"] + else: + inp.system_message = ( + "You are a helpful, respectful and honest assistant. " + "Always answer as helpfully as possible, while being safe. " + "Your answers should not include any harmful, unethical, racist, " + "sexist, toxic, dangerous, or illegal content. Please ensure that " + "your responses are socially unbiased and positive in nature. " + "If a question does not make any sense, or is not factually coherent, " + "explain why instead of answering something not correct. If you don't " + "know the answer to a question, please don't share false information." + ) + + # max tokens + if "max-tokens" in parameter: + inp.max_tokens = int(parameter["max-tokens"]) + + # temperature + if "temperature" in parameter: + inp.temperature = parameter["temperature"] + + # number of generated outputs + if "n" in parameter: + inp.n = parameter["n"] + + # top p + if "top-p" in parameter: + inp.top_p = int(parameter["top-p"]) + + # seed + if "seed" in parameter: + inp.seed = int(parameter["seed"]) + + # stream + if "stream" in parameter: + inp.seed = int(parameter["stream"]) + + input_list.append(inp) + + return input_list + + +def construct_task_completion_output( + request: Union[CallRequest, Request], + finish_reasons: List[List[str]], + indexes: List[List[int]], + created_timestamps: List[List[int]], + contents: List[List[str]], + completion_tokens: Union[List[int], None] = None, + prompt_tokens: Union[List[int], None] = None, + total_tokens: Union[List[int], None] = None, +) -> Union[CallResponse, List]: + + if ( + not len(finish_reasons) + == len(indexes) + == len(created_timestamps) + == len(contents) + ): + raise InvalidOutputShapeException + if ( + not len(finish_reasons[0]) + == len(indexes[0]) + == len(created_timestamps[0]) + == len(contents[0]) + ): + raise InvalidOutputShapeException + + if completion_tokens is None or prompt_tokens is None or total_tokens is None: + print("one or more of token usage number not set, ignore all ") + else: + if not len(completion_tokens) == len(prompt_tokens) == len(total_tokens): + raise InvalidOutputShapeException + + task_outputs = [] + # data + for finish_reason_list, index_list, created_timestamp_list, content_list in zip( + finish_reasons, indexes, created_timestamps, contents + ): + data = {} + choices = [] + for ( + finish_reason, + index, + created_timestamp, + content, + ) in zip(finish_reason_list, index_list, created_timestamp_list, content_list): + choices.append( + { + "finish-reason": finish_reason, + "index": index, + "content": content, + "created": created_timestamp, + } + ) + data["choices"] = choices + task_outputs.append({"data": data}) + + # metadata + if ( + completion_tokens is not None + and prompt_tokens is not None + and total_tokens is not None + ): + for i, (completion_token, prompt_token, total_token) in enumerate( + zip(completion_tokens, prompt_tokens, total_tokens) + ): + metadata = { + "usage": { + "completion-tokens": completion_token, + "prompt-tokens": prompt_token, + "total-tokens": total_token, + } + } + task_outputs[i]["metadata"] = metadata # type: ignore + + if isinstance(request, Request): + return task_outputs + + for i, o in enumerate(task_outputs): + task_outputs[i] = dict_to_struct(o) + + return CallResponse(task_outputs=task_outputs) + + +async def parse_task_chat_to_chat_input( + request: Union[CallRequest, Request], +) -> List[ChatInput]: + + # http test input + if isinstance(request, Request): + test_data: dict = await request.json() + + inp = ChatInput() + inp.messages = [{"role": "user", "content": test_data["prompt"]}] + return [inp] + + input_list = [] + for task_input in request.task_inputs: + task_input_dict = json_format.MessageToDict(task_input) + + data = task_input_dict["data"] + parameter = ( + task_input_dict["parameter"] if "parameter" in task_input_dict else {} + ) + + inp = ChatInput() + + messages: List[Dict[str, str]] = [] + + # messages + if len(data["messages"]) > 0: + for chat_entity in data["messages"]: + chat_message = None + if len(chat_entity["content"]) > 1: + raise ValueError( + "Multiple text message detected" + " in a single chat history entity" + ) - if input_name == "chat_history": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - chat_history_str = str(input_tensor[0].decode("utf-8")) - print( - "[DEBUG] input `chat_history_str` type" - f"({type(chat_history_str)}): " - f"{chat_history_str}" - ) - try: - text_generation_input.chat_history = json.loads(chat_history_str) - except JSONDecodeError: - print("[DEBUG] WARNING `extra_params` parsing faield!") - continue - - if input_name == "system_message": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - text_generation_input.system_message = str( - input_tensor[0].decode("utf-8") - ) - print( - "[DEBUG] input `system_message` type" - f"({type(text_generation_input.system_message)}): " - f"{text_generation_input.system_message}" - ) + if "text" in chat_entity["content"][0]: + chat_message = chat_entity["content"][0]["text"] + else: + raise ValueError( + "Unsupported chat_hisotry message type" + ", expected 'text'" + f" but get {chat_entity['content'][0]}" + ) - if input_name == "max_new_tokens": - text_generation_input.max_new_tokens = int.from_bytes( - b_input_tensor, "little" - ) - print( - "[DEBUG] input `max_new_tokens` type" - f"({type(text_generation_input.max_new_tokens)}): " - f"{text_generation_input.max_new_tokens}" - ) + if chat_entity["role"] not in PROMPT_ROLES: + raise ValueError( + f"Role `{chat_entity['role']}` is not in supported" + f"role list ({','.join(PROMPT_ROLES)})" + ) + if chat_message is None: + raise ValueError( + f"No message found in chat_history. {chat_message}" + ) - if input_name == "top_k": - text_generation_input.top_k = int.from_bytes(b_input_tensor, "little") - print( - "[DEBUG] input `top_k` type" - f"({type(text_generation_input.top_k)}): " - f"{text_generation_input.top_k}" + if ( + len(messages) > 0 + and messages[-1]["role"] != PROMPT_ROLES[0] + and chat_entity["role"] != PROMPT_ROLES[0] + ): + messages.append({"role": "user", "content": " "}) + + if len(messages) > 0 and messages[-1]["role"] == chat_entity["role"]: + last_conversation = messages.pop() + chat_message = f"{last_conversation['content']}\n\n{chat_message}" + + messages.append({"role": chat_entity["role"], "content": chat_message}) + + if messages[0]["role"] != PROMPT_ROLES[-1]: + messages.insert( + 0, + { + "role": PROMPT_ROLES[-1], + "content": ( + "You are a helpful, respectful and honest assistant. " + "Always answer as helpfully as possible, while being safe. " + "Your answers should not include any harmful, unethical, racist, " + "sexist, toxic, dangerous, or illegal content. Please ensure that " + "your responses are socially unbiased and positive in nature. " + "If a question does not make any sense, or is not factually coherent, " + "explain why instead of answering something not correct. If you don't " + "know the answer to a question, please don't share false information." + ), + }, ) - if input_name == "temperature": - text_generation_input.temperature = struct.unpack("f", b_input_tensor)[ - 0 - ] - print( - "[DEBUG] input `temperature` type" - f"({type(text_generation_input.temperature)}): " - f"{text_generation_input.temperature}" - ) - text_generation_input.temperature = round( - text_generation_input.temperature, 2 - ) + inp.messages = messages + + # max tokens + if "max-tokens" in parameter: + inp.max_tokens = int(parameter["max-tokens"]) + + # temperature + if "temperature" in parameter: + inp.temperature = parameter["temperature"] + + # number of generated outputs + if "n" in parameter: + inp.n = parameter["n"] + + # top p + if "top-p" in parameter: + inp.top_p = int(parameter["top-p"]) + + # seed + if "seed" in parameter: + inp.seed = int(parameter["seed"]) + + # stream + if "stream" in parameter: + inp.seed = int(parameter["stream"]) + + input_list.append(inp) + + return input_list + + +def construct_task_chat_output( + request: Union[CallRequest, Request], + finish_reasons: List[List[str]], + indexes: List[List[int]], + created_timestamps: List[List[int]], + messages: List[List[dict]], + completion_tokens: Union[List[int], None] = None, + prompt_tokens: Union[List[int], None] = None, + total_tokens: Union[List[int], None] = None, +) -> Union[CallResponse, List]: + + if ( + not len(finish_reasons) + == len(indexes) + == len(created_timestamps) + == len(messages) + ): + raise InvalidOutputShapeException + if ( + not len(finish_reasons[0]) + == len(indexes[0]) + == len(created_timestamps[0]) + == len(messages[0]) + ): + raise InvalidOutputShapeException + + if completion_tokens is None or prompt_tokens is None or total_tokens is None: + print("one or more of token usage number not set, ignore all ") + else: + if not len(completion_tokens) == len(prompt_tokens) == len(total_tokens): + raise InvalidOutputShapeException + + task_outputs = [] + # data + for finish_reason_list, index_list, created_timestamp_list, message_list in zip( + finish_reasons, indexes, created_timestamps, messages + ): + data = {} + choices = [] + for ( + finish_reason, + index, + created_timestamp, + message, + ) in zip(finish_reason_list, index_list, created_timestamp_list, message_list): + choices.append( + { + "finish-reason": finish_reason, + "index": index, + "message": message, + "created": created_timestamp, + } + ) + data["choices"] = choices + task_outputs.append({"data": data}) + + # metadata + if ( + completion_tokens is not None + and prompt_tokens is not None + and total_tokens is not None + ): + for i, (completion_token, prompt_token, total_token) in enumerate( + zip(completion_tokens, prompt_tokens, total_tokens) + ): + metadata = { + "usage": { + "completion-tokens": completion_token, + "prompt-tokens": prompt_token, + "total-tokens": total_token, + } + } + task_outputs[i]["metadata"] = metadata # type: ignore + + if isinstance(request, Request): + return task_outputs + + for i, o in enumerate(task_outputs): + task_outputs[i] = dict_to_struct(o) + + return CallResponse(task_outputs=task_outputs) + + +async def parse_task_chat_to_multimodal_chat_input( + request: Union[CallRequest, Request], +) -> List[ChatMultiModalInput]: + + # http test input + if isinstance(request, Request): + test_data: dict = await request.json() + + test_prompt = test_data["prompt"] + image_url = test_data["image-url"] + + inp = ChatMultiModalInput() + inp.messages = [ + { + "role": "user", + "content": test_prompt, + } + ] + inp.prompt_images = [[url_to_pil_image(image_url)]] + return [inp] + + input_list = [] + for task_input in request.task_inputs: + task_input_dict = json_format.MessageToDict(task_input) + + data = task_input_dict["data"] + parameter = ( + task_input_dict["parameter"] if "parameter" in task_input_dict else {} + ) + + inp = ChatMultiModalInput() + + # messages and prompt images + messages: List[Dict[str, str]] = [] + images: List[List[Image.Image]] = [] + for message in data["messages"]: + role = message["role"] + content = message["content"] + + imgs = [] + if role == PROMPT_ROLES[0]: + for c in content: + if c["type"] == "text": + messages.insert(0, {"role": role, "content": c["text"]}) + elif c["type"] == "image-url": + imgs.append(url_to_pil_image(c["image-url"])) + elif c["type"] == "image-base64": + imgs.append(base64_to_pil_image(c["image-base64"])) + else: + raise InvalidInputException("input content type not supported") + elif role == PROMPT_ROLES[1]: + messages.append({"role": role, "content": content[0]["text"]}) + elif role == PROMPT_ROLES[-1] and messages[0]["role"] != PROMPT_ROLES[-1]: + messages.insert(0, {"role": role, "content": content[0]["text"]}) + + images.append(imgs) + + if messages[0]["role"] != PROMPT_ROLES[-1]: + messages.insert( + 0, + { + "role": PROMPT_ROLES[-1], + "content": ( + "You are a helpful, respectful and honest assistant. " + "Always answer as helpfully as possible, while being safe. " + "Your answers should not include any harmful, unethical, racist, " + "sexist, toxic, dangerous, or illegal content. Please ensure that " + "your responses are socially unbiased and positive in nature. " + "If a question does not make any sense, or is not factually coherent, " + "explain why instead of answering something not correct. If you don't " + "know the answer to a question, please don't share false information." + ), + }, + ) + + inp.messages = messages + inp.prompt_images = images + + # max tokens + if "max-tokens" in parameter: + inp.max_tokens = int(parameter["max-tokens"]) + + # temperature + if "temperature" in parameter: + inp.temperature = parameter["temperature"] + + # number of generated outputs + if "n" in parameter: + inp.n = parameter["n"] + + # top p + if "top-p" in parameter: + inp.top_p = int(parameter["top-p"]) + + # seed + if "seed" in parameter: + inp.seed = int(parameter["seed"]) + + # stream + if "stream" in parameter: + inp.seed = int(parameter["stream"]) + + input_list.append(inp) + + return input_list + + +async def parse_task_text_to_image_input( + request: Union[CallRequest, Request], +) -> List[TextToImageInput]: + + # http test input + if isinstance(request, Request): + data: dict = await request.json() + + inp = TextToImageInput() + inp.prompt = data["prompt"] + + return [inp] + + input_list = [] + for task_input in request.task_inputs: + task_input_dict = json_format.MessageToDict(task_input) + + data = task_input_dict["data"] + parameter = ( + task_input_dict["parameter"] if "parameter" in task_input_dict else {} + ) + + inp = TextToImageInput() + + # prompt + inp.prompt = data["prompt"] + + # negative prompt + if "negative-prompt" in parameter: + inp.negative_prompt = str(parameter["negative-prompt"]) + + # aspect ratio + if "aspect-ratio" in parameter: + inp.aspect_ratio = str(parameter["aspect-ratio"]) + + # number of generated outputs + if "n" in parameter: + inp.n = parameter["n"] - if input_name == "random_seed": - text_generation_input.random_seed = int.from_bytes( - b_input_tensor, "little" - ) - print( - "[DEBUG] input `random_seed` type" - f"({type(text_generation_input.random_seed)}): " - f"{text_generation_input.random_seed}" - ) + # seed + if "seed" in parameter: + inp.seed = int(parameter["seed"]) - if input_name == "extra_params": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - extra_params_str = str(input_tensor[0].decode("utf-8")) - print( - "[DEBUG] input `extra_params` type" - f"({type(extra_params_str)}): " - f"{extra_params_str}" - ) + input_list.append(inp) - try: - text_generation_input.extra_params = json.loads(extra_params_str) - except JSONDecodeError: - print("[DEBUG] WARNING `extra_params` parsing faield!") - continue + return input_list - return text_generation_input - @staticmethod - def parse_task_text_generation_output(sequences: list): - text_outputs = [seq["generated_text"].encode("utf-8") for seq in sequences] +def construct_task_text_to_image_output( + request: Union[CallRequest, Request], + finish_reasons: List[List[str]], + images: List[List[str]], +) -> Union[CallResponse, List]: + """Construct trigger output for text to image task - return serialize_byte_tensor(np.asarray(text_outputs)) + Args: + images (List[List[str]]): for each input prompt, the generated images with the length of `samples` + """ - @staticmethod - def parse_task_text_to_image_input(request) -> TextToImageInput: - text_to_image_input = TextToImageInput() + if not len(finish_reasons) == len(images): + raise InvalidOutputShapeException + if not len(finish_reasons[0]) == len(images[0]): + raise InvalidOutputShapeException - for i, b_input_tensor in zip(request.inputs, request.raw_input_contents): - input_name = i.name + task_outputs = [] + for imgs, finishes in zip(images, finish_reasons): + data = {} + choices = [] - if input_name == "prompt": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - text_to_image_input.prompt = str(input_tensor[0].decode("utf-8")) - print( - f"[DEBUG] input `prompt` type\ - ({type(text_to_image_input.prompt)}): {text_to_image_input.prompt}" - ) + for img, finish in zip(imgs, finishes): + choices.append( + { + "finish-reason": finish, + "image": img, + } + ) + data["choices"] = choices + task_outputs.append({"data": data}) - if input_name == "negative_prompt": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - text_to_image_input.negative_prompt = str( - input_tensor[0].decode("utf-8") - ) - print( - f"[DEBUG] input `negative_prompt` type\ - ({type(text_to_image_input.negative_prompt)}): {text_to_image_input.negative_prompt}" - ) + if isinstance(request, Request): + return task_outputs + + for i, o in enumerate(task_outputs): + task_outputs[i] = dict_to_struct(o) - if input_name == "steps": - text_to_image_input.steps = int.from_bytes(b_input_tensor, "little") - print( - f"[DEBUG] input `steps` type\ - ({type(text_to_image_input.steps)}): {text_to_image_input.steps}" - ) + return CallResponse(task_outputs=task_outputs) - if input_name == "seed": - text_to_image_input.seed = int.from_bytes(b_input_tensor, "little") - print( - f"[DEBUG] input `seed` type\ - ({type(text_to_image_input.seed)}): {text_to_image_input.seed}" - ) - if input_name == "guidance_scale": - text_to_image_input.guidance_scale = struct.unpack("f", b_input_tensor)[ - 0 - ] - print( - f"[DEBUG] input `guidance_scale` type\ - ({type(text_to_image_input.guidance_scale)}): {text_to_image_input.guidance_scale}" - ) - text_to_image_input.guidance_scale = round( - text_to_image_input.guidance_scale, 2 - ) +# async def parse_task_image_to_image_input( +# request: Union[CallRequest, Request], +# ) -> List[ImageToImageInput]: - if input_name == "samples": - text_to_image_input.samples = int.from_bytes(b_input_tensor, "little") - print( - f"[DEBUG] input `samples` type\ - ({type(text_to_image_input.samples)}): {text_to_image_input.samples}" - ) +# # http test input +# if isinstance(request, Request): +# data: dict = await request.json() - if input_name == "extra_params": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - extra_params_str = str(input_tensor[0].decode("utf-8")) - print( - f"[DEBUG] input `extra_params` type\ - ({type(extra_params_str)}): {extra_params_str}" - ) +# test_prompt = data["prompt"] +# test_image_url = data["image_url"] - try: - text_to_image_input.extra_params = json.loads(extra_params_str) - except JSONDecodeError: - print("[DEBUG] WARNING `extra_params` parsing faield!") - continue - - return text_to_image_input - - @staticmethod - def parse_task_text_to_image_output(image): - return np.asarray(image).tobytes() - - @staticmethod - def parse_task_image_to_image_input(request) -> ImageToImageInput: - image_to_image_input = ImageToImageInput() - - for i, b_input_tensor in zip(request.inputs, request.raw_input_contents): - input_name = i.name - - if input_name == "prompt_image": - input_tensors = deserialize_bytes_tensor(b_input_tensor) - images = [] - for enc in input_tensors: - pil_img = Image.open(io.BytesIO(enc.astype(bytes))) # RGB - image = np.array(pil_img) - if len(image.shape) == 2: # gray image - raise ValueError( - f"The image shape with {image.shape} is " - f"not in acceptable" - ) - images.append(image) - image_to_image_input.prompt_image = images[0] - print( - f"[DEBUG] input `prompt_image` type\ - ({type(image_to_image_input.prompt_image)}): {image_to_image_input.prompt_image}" - ) +# inp = ImageToImageInput() +# inp.prompt = test_prompt +# inp.prompt_image = url_to_pil_image(test_image_url) - if input_name == "prompt": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - image_to_image_input.prompt = str(input_tensor[0].decode("utf-8")) - print( - f"[DEBUG] input `prompt` type\ - ({type(image_to_image_input.prompt)}): {image_to_image_input.prompt}" - ) +# return [inp] - if input_name == "steps": - image_to_image_input.steps = int.from_bytes(b_input_tensor, "little") - print( - f"[DEBUG] input `steps` type\ - ({type(image_to_image_input.steps)}): {image_to_image_input.steps}" - ) +# input_list = [] +# for task_input in request.task_inputs: +# task_input_dict = json_format.MessageToDict(task_input)["ImageToImage"] - if input_name == "seed": - image_to_image_input.seed = int.from_bytes(b_input_tensor, "little") - print( - f"[DEBUG] input `seed` type\ - ({type(image_to_image_input.seed)}): {image_to_image_input.seed}" - ) +# inp = ImageToImageInput() - if input_name == "guidance_scale": - image_to_image_input.guidance_scale = struct.unpack( - "f", b_input_tensor - )[0] - print( - f"[DEBUG] input `guidance_scale` type\ - ({type(image_to_image_input.guidance_scale)}): {image_to_image_input.guidance_scale}" - ) - image_to_image_input.guidance_scale = round( - image_to_image_input.guidance_scale, 2 - ) +# # prompt +# inp.prompt = task_input_dict["prompt"] - if input_name == "samples": - image_to_image_input.samples = int.from_bytes(b_input_tensor, "little") - print( - f"[DEBUG] input `samples` type\ - ({type(image_to_image_input.samples)}): {image_to_image_input.samples}" - ) +# # prompt images +# if ( +# "Promptimage-url" in task_input_dict["Type"] +# and "Promptimage-base64" in task_input_dict["Type"] +# ) or ( +# "Promptimage-url" not in task_input_dict["Type"] +# and "Promptimage-base64" not in task_input_dict["Type"] +# ): +# raise InvalidInputException +# if "Promptimage-url" in task_input_dict["Type"]: +# inp.prompt_image = url_to_pil_image( +# task_input_dict["Type"]["Promptimage-url"] +# ) +# elif "Promptimage-base64" in task_input_dict["Type"]: +# inp.prompt_image = base64_to_pil_image( +# task_input_dict["Type"]["Promptimage-base64"] +# ) - if input_name == "extra_params": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - extra_params_str = str(input_tensor[0].decode("utf-8")) - print( - f"[DEBUG] input `extra_params` type\ - ({type(extra_params_str)}): {extra_params_str}" - ) +# # steps +# if "steps" in task_input_dict: +# inp.steps = int(task_input_dict["steps"]) - try: - image_to_image_input.extra_params = json.loads(extra_params_str) - except JSONDecodeError: - print("[DEBUG] WARNING `extra_params` parsing faield!") - continue +# # cfg_scale +# if "cfg_scale" in task_input_dict: +# inp.cfg_scale = task_input_dict["cfg_scale"] - return image_to_image_input +# # samples +# if "samples" in task_input_dict: +# inp.samples = int(task_input_dict["samples"]) - @staticmethod - def parse_task_image_to_image_output(image): - return np.asarray(image).tobytes() +# # seed +# if "seed" in task_input_dict: +# inp.seed = int(task_input_dict["seed"]) - @staticmethod - def parse_task_text_generation_chat_input(request) -> TextGenerationChatInput: - text_generation_chat_input = TextGenerationChatInput() +# input_list.append(inp) - for i, b_input_tensor in zip(request.inputs, request.raw_input_contents): - input_name = i.name +# return input_list - if input_name == "prompt": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - text_generation_chat_input.prompt = str(input_tensor[0].decode("utf-8")) - print( - f"[DEBUG] input `prompt` type\ - ({type(text_generation_chat_input.prompt)}): {text_generation_chat_input.prompt}" - ) - if input_name == "prompt_images": - input_tensors = deserialize_bytes_tensor(b_input_tensor) - images = [] - for enc in input_tensors: - if len(enc) == 0: - continue - try: - enc_json = json.loads(str(enc.decode("utf-8"))) - if len(enc_json) == 0: - continue - decoded_enc = enc_json[0] - except JSONDecodeError: - print("[DEBUG] WARNING `enc_json` parsing faield!") - # pil_img = Image.open(io.BytesIO(enc.astype(bytes))) # RGB - pil_img = Image.open(io.BytesIO(base64.b64decode(decoded_enc))) - - image = np.array(pil_img) - if len(image.shape) == 2: # gray image - raise ValueError( - f"The image shape with {image.shape} is " - f"not in acceptable" - ) - images.append(image) - text_generation_chat_input.prompt_images = images - print( - "[DEBUG] input `prompt_images` type" - f"({type(text_generation_chat_input.prompt_images)}): " - f"{text_generation_chat_input.prompt_images}" - ) +# def construct_task_image_to_image_output( +# request: Union[CallRequest, Request], +# images: List[List[str]], +# ) -> Union[CallResponse, List[List[str]]]: +# """Construct trigger output for keypoint task - if input_name == "chat_history": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - chat_history_str = str(input_tensor[0].decode("utf-8")) - print( - "[DEBUG] input `chat_history_str` type" - f"({type(chat_history_str)}): " - f"{chat_history_str}" - ) - try: - text_generation_chat_input.chat_history = json.loads( - chat_history_str - ) - except JSONDecodeError: - print("[DEBUG] WARNING `extra_params` parsing faield!") - continue - - if input_name == "system_message": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - text_generation_chat_input.system_message = str( - input_tensor[0].decode("utf-8") - ) - print( - "[DEBUG] input `system_message` type" - f"({type(text_generation_chat_input.system_message)}): " - f"{text_generation_chat_input.system_message}" - ) +# Args: +# images (List[List[str]]): for each input prompt, the generated images with the length of `samples` +# """ - if input_name == "max_new_tokens": - text_generation_chat_input.max_new_tokens = int.from_bytes( - b_input_tensor, "little" - ) - print( - "[DEBUG] input `max_new_tokens` type" - f"({type(text_generation_chat_input.max_new_tokens)}): " - f"{text_generation_chat_input.max_new_tokens}" - ) +# if isinstance(request, Request): +# return images - if input_name == "top_k": - text_generation_chat_input.top_k = int.from_bytes( - b_input_tensor, "little" - ) - print( - "[DEBUG] input `top_k` type" - f"({type(text_generation_chat_input.top_k)}): " - f"{text_generation_chat_input.top_k}" - ) +# task_outputs = [] +# for imgs in images: +# task_outputs.append( +# protobuf_to_struct( +# modelpb.TaskOutput( +# image_to_image=imagetoimagepb.ImageToImageOutput(images=imgs) +# ) +# ) +# ) - if input_name == "temperature": - text_generation_chat_input.temperature = struct.unpack( - "f", b_input_tensor - )[0] - print( - "[DEBUG] input `temperature` type" - f"({type(text_generation_chat_input.temperature)}): " - f"{text_generation_chat_input.temperature}" - ) - text_generation_chat_input.temperature = round( - text_generation_chat_input.temperature, 2 - ) +# return CallResponse(task_outputs=task_outputs) - if input_name == "random_seed": - text_generation_chat_input.random_seed = int.from_bytes( - b_input_tensor, "little" - ) - print( - "[DEBUG] input `random_seed` type" - f"({type(text_generation_chat_input.random_seed)}): " - f"{text_generation_chat_input.random_seed}" - ) - if input_name == "extra_params": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - extra_params_str = str(input_tensor[0].decode("utf-8")) - print( - "[DEBUG] input `extra_params` type" - f"({type(extra_params_str)}): " - f"{extra_params_str}" - ) +async def parse_task_embedding_to_text_embedding_input( + request: Union[CallRequest, Request], +) -> List[TextEmbeddingInput]: - try: - text_generation_chat_input.extra_params = json.loads( - extra_params_str - ) - except JSONDecodeError: - print("[DEBUG] WARNING `extra_params` parsing faield!") - continue + # http test input + if isinstance(request, Request): + test_data: dict = await request.json() - return text_generation_chat_input + test_prompt = test_data["prompt"] - @staticmethod - def parse_task_text_generation_chat_output(sequences: list): - text_outputs = [seq["generated_text"].encode("utf-8") for seq in sequences] + inp = TextEmbeddingInput() + inp.contents = [test_prompt] - return serialize_byte_tensor(np.asarray(text_outputs)) + return [inp] - @staticmethod - def parse_task_visual_question_answering_input( - request, - ) -> VisualQuestionAnsweringInput: - text_visual_question_answering_input = VisualQuestionAnsweringInput() + input_list = [] + for task_input in request.task_inputs: + task_input_dict = json_format.MessageToDict(task_input) - for i, b_input_tensor in zip(request.inputs, request.raw_input_contents): - input_name = i.name + data = task_input_dict["data"] + parameter = ( + task_input_dict["parameter"] if "parameter" in task_input_dict else {} + ) - if input_name == "prompt": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - text_visual_question_answering_input.prompt = str( - input_tensor[0].decode("utf-8") - ) - print( - "[DEBUG] input `prompt` type" - f"({type(text_visual_question_answering_input.prompt)}): " - f"{text_visual_question_answering_input.prompt}" - ) + inp = TextEmbeddingInput() - if input_name == "prompt_images": - input_tensors = deserialize_bytes_tensor(b_input_tensor) - images = [] - for enc in input_tensors: - if len(enc) == 0: - continue - try: - enc_json = json.loads(str(enc.decode("utf-8"))) - if len(enc_json) == 0: - continue - decoded_enc = enc_json[0] - except JSONDecodeError: - print("[DEBUG] WARNING `enc_json` parsing faield!") - # pil_img = Image.open(io.BytesIO(enc.astype(bytes))) # RGB - pil_img = Image.open(io.BytesIO(base64.b64decode(decoded_enc))) - - image = np.array(pil_img) - if len(image.shape) == 2: # gray image - raise ValueError( - f"The image shape with {image.shape} is " - f"not in acceptable" - ) - images.append(image) - # TODO: check wethere there are issues in batch size dimention - text_visual_question_answering_input.prompt_images = images - print( - "[DEBUG] input `prompt_images` type" - f"({type(text_visual_question_answering_input.prompt_images)}): " - f"{text_visual_question_answering_input.prompt_images}" - ) + # messages and prompt images + contents: List[str] = [] + for inputs in data["input"]: + for content in inputs["content"]: + if content["type"] == "text": + contents.append(content["text"]) + else: + raise InvalidInputException("can only process text input") + + inp.contents = contents + + # format + if "format" in parameter: + inp.format = str(parameter["format"]) + + # dimensions + if "dimensions" in parameter: + inp.dimensions = int(parameter["dimensions"]) + + # input-type + if "input-type" in parameter: + inp.input_type = str(parameter["input-type"]) + + # truncate + if "truncate" in parameter: + inp.truncate = str(parameter["truncate"]) + + input_list.append(inp) + + return input_list + + +def construct_task_embedding_output( + request: Union[CallRequest, Request], + indexes: List[List[int]], + created_timestamps: List[List[int]], + embeddings: List[List[list]], +) -> Union[CallResponse, List]: + + if not len(embeddings) == len(indexes) == len(created_timestamps): + raise InvalidOutputShapeException + if not len(embeddings[0]) == len(indexes[0]) == len(created_timestamps[0]): + raise InvalidOutputShapeException + + task_outputs = [] + # data + for index_list, created_timestamp_list, embedding_list in zip( + indexes, created_timestamps, embeddings + ): + data = {} + embeds = [] + for ( + index, + created_timestamp, + embed, + ) in zip(index_list, created_timestamp_list, embedding_list): + if isinstance(embed, np.ndarray): + embed = embed.tolist() + embeds.append( + { + "index": index, + "vector": list(embed), + "created": created_timestamp, + } + ) + data["embeddings"] = embeds + task_outputs.append({"data": data}) + + if isinstance(request, Request): + return task_outputs + + for i, o in enumerate(task_outputs): + task_outputs[i] = dict_to_struct(o) + + return CallResponse(task_outputs=task_outputs) + + +async def parse_task_embedding_to_image_embedding_input( + request: Union[CallRequest, Request], +) -> List[ImageEmbeddingInput]: + + # http test input + if isinstance(request, Request): + test_data: dict = await request.json() + + test_img = test_data["image"] + + inp = ImageEmbeddingInput() + inp.images = [url_to_pil_image(test_img)] + + return [inp] + + input_list = [] + for task_input in request.task_inputs: + task_input_dict = json_format.MessageToDict(task_input) + + data = task_input_dict["data"] + parameter = ( + task_input_dict["parameter"] if "parameter" in task_input_dict else {} + ) + + inp = ImageEmbeddingInput() + + # messages and prompt images + images: List[Image.Image] = [] + for inputs in data["input"]: + for content in inputs["content"]: + if content["type"] == "image-url": + images.append(url_to_pil_image(content[content["type"]])) + elif content["type"] == "image-base64": + images.append(base64_to_pil_image(content[content["type"]])) + else: + raise InvalidInputException("can only process image input") - if input_name == "chat_history": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - chat_history_str = str(input_tensor[0].decode("utf-8")) - print( - "[DEBUG] input `chat_history_str` type" - f"({type(chat_history_str)}): " - f"{chat_history_str}" - ) - try: - text_visual_question_answering_input.chat_history = json.loads( - chat_history_str - ) - except JSONDecodeError: - print("[DEBUG] WARNING `extra_params` parsing faield!") - continue - - if input_name == "system_message": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - text_visual_question_answering_input.system_message = str( - input_tensor[0].decode("utf-8") - ) - print( - "[DEBUG] input `system_message` type" - f"({type(text_visual_question_answering_input.system_message)}): " - f"{text_visual_question_answering_input.system_message}" - ) + inp.images = images - if input_name == "max_new_tokens": - text_visual_question_answering_input.max_new_tokens = int.from_bytes( - b_input_tensor, "little" - ) - print( - "[DEBUG] input `max_new_tokens` type" - f"({type(text_visual_question_answering_input.max_new_tokens)}): " - f"{text_visual_question_answering_input.max_new_tokens}" - ) + # format + if "format" in parameter: + inp.format = str(parameter["format"]) - if input_name == "top_k": - text_visual_question_answering_input.top_k = int.from_bytes( - b_input_tensor, "little" - ) - print( - "[DEBUG] input `top_k` type" - f"({type(text_visual_question_answering_input.top_k)}): " - f"{text_visual_question_answering_input.top_k}" - ) + # dimensions + if "dimensions" in parameter: + inp.dimensions = int(parameter["dimensions"]) - if input_name == "temperature": - text_visual_question_answering_input.temperature = struct.unpack( - "f", b_input_tensor - )[0] - print( - "[DEBUG] input `temperature` type" - f"({type(text_visual_question_answering_input.temperature)}): " - f"{text_visual_question_answering_input.temperature}" - ) - text_visual_question_answering_input.temperature = round( - text_visual_question_answering_input.temperature, 2 - ) + # input-type + if "input-type" in parameter: + inp.input_type = str(parameter["input-type"]) - if input_name == "random_seed": - text_visual_question_answering_input.random_seed = int.from_bytes( - b_input_tensor, "little" - ) - print( - "[DEBUG] input `random_seed` type" - f"({type(text_visual_question_answering_input.random_seed)}): " - f"{text_visual_question_answering_input.random_seed}" - ) + # truncate + if "truncate" in parameter: + inp.truncate = str(parameter["truncate"]) - if input_name == "extra_params": - input_tensor = deserialize_bytes_tensor(b_input_tensor) - extra_params_str = str(input_tensor[0].decode("utf-8")) - print( - "[DEBUG] input `extra_params` type" - f"({type(extra_params_str)}): " - f"{extra_params_str}" - ) + input_list.append(inp) - try: - text_visual_question_answering_input.extra_params = json.loads( - extra_params_str - ) - except JSONDecodeError: - print("[DEBUG] WARNING `extra_params` parsing faield!") - continue + return input_list + + +async def parse_custom_input( + request: Union[CallRequest, Request], +) -> List[dict]: - return text_visual_question_answering_input + # http test input + if isinstance(request, Request): + test_data: dict = await request.json() - @staticmethod - def parse_task_visual_question_answering_output(sequences: list): - text_outputs = [seq["generated_text"].encode("utf-8") for seq in sequences] + return [test_data] - return serialize_byte_tensor(np.asarray(text_outputs)) + input_list = [] + for task_input in request.task_inputs: + task_input_dict = json_format.MessageToDict(task_input) + input_list.append(task_input_dict) -class RawIO: - @staticmethod - def parse_byte_tensor(byte_tensor) -> List[str]: - input_tensors = deserialize_bytes_tensor(byte_tensor) - outs = [str(tensor.decode("utf-8")) for tensor in input_tensors] + return input_list - return outs - @staticmethod - def parse_unsigned_int_tensor(int_tensor) -> int: - return int.from_bytes(int_tensor, "little") +def construct_custom_output( + request: Union[CallRequest, Request], outputs: List[dict] +) -> Union[CallResponse, List]: - @staticmethod - def parse_signed_int_tensor(int_tensor) -> int: - return int.from_bytes(int_tensor, "little", signed=True) + if isinstance(request, Request): + return outputs - @staticmethod - def parse_float_tensor(float_tensor) -> float: - return struct.unpack("f", float_tensor)[0] + for i, o in enumerate(outputs): + outputs[i] = dict_to_struct(o) - @staticmethod - def parse_boolean_tensor(bool_tensor) -> bool: - return struct.unpack("?", bool_tensor)[0] + return CallResponse(task_outputs=outputs) diff --git a/instill/helpers/test.py b/instill/helpers/test.py new file mode 100644 index 0000000..02db46c --- /dev/null +++ b/instill/helpers/test.py @@ -0,0 +1,25 @@ +import argparse +import json +import pprint + +import requests + +from instill.utils.logger import Logger + +parser = argparse.ArgumentParser() + +parser.add_argument( + "-i", + "--input", + help="inference input json", + required=True, +) + +args = parser.parse_args() + +resp = requests.post("http://127.0.0.1:8000/", json=json.loads(args.input), timeout=600) + +if resp.status_code == 200: + Logger.i(f"[Instill] Outputs:\n{pprint.pformat(resp.json())}") +else: + Logger.e(f"[Instill] Errors:\n{pprint.pformat(resp.text)}") diff --git a/instill/resources/__init__.py b/instill/resources/__init__.py index 62b3256..3027b19 100644 --- a/instill/resources/__init__.py +++ b/instill/resources/__init__.py @@ -1,34 +1,2 @@ -import instill.protogen.common.task.v1alpha.task_pb2 as task -import instill.protogen.model.model.v1alpha.model_pb2 as model_pb -import instill.protogen.model.model.v1alpha.task_classification_pb2 as task_classification -import instill.protogen.model.model.v1alpha.task_detection_pb2 as task_detection -import instill.protogen.model.model.v1alpha.task_keypoint_pb2 as task_keypoint -import instill.protogen.model.model.v1alpha.task_ocr_pb2 as task_ocr -import instill.protogen.model.model.v1alpha.task_semantic_segmentation_pb2 as task_semantic_segmentation -import instill.protogen.model.model.v1alpha.task_text_generation_pb2 as task_text_generation -import instill.protogen.model.model.v1alpha.task_text_to_image_pb2 as task_text_to_image -import instill.protogen.vdp.pipeline.v1beta.pipeline_pb2 as pipeline_pb - -# from instill.resources.connector_ai import ( -# HuggingfaceConnector, -# InstillModelConnector, -# OpenAIConnector, -# StabilityAIConnector, -# ) -# from instill.resources.connector_blockchain import NumbersConnector -# from instill.resources.connector_data import ( -# BigQueryConnector, -# GoogleCloudStorageConnector, -# GoogleSearchConnector, -# PineconeConnector, -# RedisConnector, -# WebsiteConnector, -# ) -# from instill.resources.operator import ( -# Base64Operator, -# ImageOperator, -# JSONOperator, -# TextOperator, -# ) from instill.resources.model import Model from instill.resources.pipeline import Pipeline diff --git a/poetry.lock b/poetry.lock index cc184df..3793468 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,98 +2,113 @@ [[package]] name = "aiohappyeyeballs" -version = "2.3.5" +version = "2.4.0" description = "Happy Eyeballs for asyncio" optional = false python-versions = ">=3.8" files = [ - {file = "aiohappyeyeballs-2.3.5-py3-none-any.whl", hash = "sha256:4d6dea59215537dbc746e93e779caea8178c866856a721c9c660d7a5a7b8be03"}, - {file = "aiohappyeyeballs-2.3.5.tar.gz", hash = "sha256:6fa48b9f1317254f122a07a131a86b71ca6946ca989ce6326fff54a99a920105"}, + {file = "aiohappyeyeballs-2.4.0-py3-none-any.whl", hash = "sha256:7ce92076e249169a13c2f49320d1967425eaf1f407522d707d59cac7628d62bd"}, + {file = "aiohappyeyeballs-2.4.0.tar.gz", hash = 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