From ccae24d695941cbd60d64872102cb29198f4e56e Mon Sep 17 00:00:00 2001 From: Yahav Date: Wed, 1 Jan 2025 14:32:39 -0800 Subject: [PATCH 01/45] gitignore --- flux_serve/.gitignore | 3 + flux_serve/README.md | 23 + flux_serve/app/Dockerfile-assets | 16 + flux_serve/app/Dockerfile.template | 21 + flux_serve/app/build-assets.sh | 50 + flux_serve/app/build.sh | 22 + flux_serve/app/flux_gradio.py | 143 + flux_serve/app/flux_model_api.py | 259 ++ flux_serve/oci-image-build/README.md | 34 + flux_serve/oci-image-build/cdk.out/cdk.out | 1 + .../flux-oci-image-inference-cdk.assets.json | 20 + ...flux-oci-image-inference-cdk.template.json | 1960 +++++++++++++ .../oci-image-build/cdk.out/manifest.json | 357 +++ flux_serve/oci-image-build/cdk.out/tree.json | 2599 +++++++++++++++++ flux_serve/oci-image-build/deploy-pipeline.sh | 12 + flux_serve/oci-image-build/package-lock.json | 615 ++++ flux_serve/oci-image-build/package.json | 9 + flux_serve/oci-image-build/pipeline-stack.js | 329 +++ flux_serve/oci-image-build/pipeline-stack.ts | 321 ++ flux_serve/oci-image-build/pipeline.js | 44 + flux_serve/oci-image-build/pipeline.ts | 10 + flux_serve/oci-image-build/tsconfig.json | 111 + flux_serve/src/README.md | 11 + flux_serve/src/compile.sh | 10 + flux_serve/src/decoder/compile.py | 62 + flux_serve/src/decoder/model.py | 19 + flux_serve/src/inference.py | 232 ++ flux_serve/src/inference.sh | 5 + flux_serve/src/text_encoder_1/compile.py | 45 + flux_serve/src/text_encoder_1/model.py | 33 + flux_serve/src/text_encoder_2/compile.py | 86 + flux_serve/src/text_encoder_2/model.py | 215 ++ flux_serve/src/transformer/compile.py | 218 ++ flux_serve/src/transformer/model.py | 475 +++ 34 files changed, 8370 insertions(+) create mode 100644 flux_serve/.gitignore create mode 100644 flux_serve/README.md create mode 100644 flux_serve/app/Dockerfile-assets create mode 100644 flux_serve/app/Dockerfile.template create mode 100755 flux_serve/app/build-assets.sh create mode 100755 flux_serve/app/build.sh create mode 100644 flux_serve/app/flux_gradio.py create mode 100644 flux_serve/app/flux_model_api.py create mode 100644 flux_serve/oci-image-build/README.md create mode 100644 flux_serve/oci-image-build/cdk.out/cdk.out create mode 100644 flux_serve/oci-image-build/cdk.out/flux-oci-image-inference-cdk.assets.json create mode 100644 flux_serve/oci-image-build/cdk.out/flux-oci-image-inference-cdk.template.json create mode 100644 flux_serve/oci-image-build/cdk.out/manifest.json create mode 100644 flux_serve/oci-image-build/cdk.out/tree.json create mode 100755 flux_serve/oci-image-build/deploy-pipeline.sh create mode 100644 flux_serve/oci-image-build/package-lock.json create mode 100644 flux_serve/oci-image-build/package.json create mode 100644 flux_serve/oci-image-build/pipeline-stack.js create mode 100644 flux_serve/oci-image-build/pipeline-stack.ts create mode 100644 flux_serve/oci-image-build/pipeline.js create mode 100644 flux_serve/oci-image-build/pipeline.ts create mode 100644 flux_serve/oci-image-build/tsconfig.json create mode 100644 flux_serve/src/README.md create mode 100755 flux_serve/src/compile.sh create mode 100644 flux_serve/src/decoder/compile.py create mode 100644 flux_serve/src/decoder/model.py create mode 100644 flux_serve/src/inference.py create mode 100755 flux_serve/src/inference.sh create mode 100644 flux_serve/src/text_encoder_1/compile.py create mode 100644 flux_serve/src/text_encoder_1/model.py create mode 100644 flux_serve/src/text_encoder_2/compile.py create mode 100644 flux_serve/src/text_encoder_2/model.py create mode 100644 flux_serve/src/transformer/compile.py create mode 100644 flux_serve/src/transformer/model.py diff --git a/flux_serve/.gitignore b/flux_serve/.gitignore new file mode 100644 index 0000000..4c9908f --- /dev/null +++ b/flux_serve/.gitignore @@ -0,0 +1,3 @@ +package-lock.json +.DS_Store +node_modules/* diff --git a/flux_serve/README.md b/flux_serve/README.md new file mode 100644 index 0000000..b9c4ba3 --- /dev/null +++ b/flux_serve/README.md @@ -0,0 +1,23 @@ +# Compile and serve ultra large vision transformers like Flux on Neuron devices at scale + +challenges in deploying large models for serving: +1/large model graph traces load - caching S3 buckets on EKS nodes with CSI drivers +https://docs.aws.amazon.com/eks/latest/userguide/s3-csi.html + +Using DLCs to simplify OCI image build - easy to streamline with automation + +https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/monitoring-tools.html#monitoring-tools are availible out of the box with container insights + +cost effectiveness requires to allocate the minimal required cores. The device plugin and myscheduler through Helm simplify deployment +https://awsdocs-neuron.readthedocs-hosted.com/en/latest/containers/kubernetes-getting-started.html +Neuron device plugin, Neuron scheduler extension, Neuron scheduler + +## Walkthrough +* [Create cluster with Karpenter node pools that provisions `trn1` instances](https://karpenter.sh/docs/getting-started/getting-started-with-karpenter/) +* Create an S3 bucket to store the model compiled graph and export the S3 bucket name as follow: +```bash +export MODEL_GRAPH_BUCKET="black-forest-labs-flux1-dev-neuron" +``` +* [Enable Amazon S3 objects with Mountpoint for Amazon S3 CSI driver](https://docs.aws.amazon.com/eks/latest/userguide/s3-csi.html) +* [Deploy the OCI image pipeline](./oci-image-build) +* diff --git a/flux_serve/app/Dockerfile-assets b/flux_serve/app/Dockerfile-assets new file mode 100644 index 0000000..e934b72 --- /dev/null +++ b/flux_serve/app/Dockerfile-assets @@ -0,0 +1,16 @@ +ARG image + +FROM public.ecr.aws/docker/library/python:latest as base +RUN apt-get update -y --fix-missing +RUN apt-get install -y python3-venv g++ gettext-base jq bc +RUN mkdir -p /etc/apt/keyrings/ +RUN curl -fsSL https://pkgs.k8s.io/core:/stable:/v1.29/deb/Release.key | gpg --dearmor -o /etc/apt/keyrings/kubernetes-apt-keyring.gpg +RUN echo 'deb [signed-by=/etc/apt/keyrings/kubernetes-apt-keyring.gpg] https://pkgs.k8s.io/core:/stable:/v1.29/deb/ /' | tee /etc/apt/sources.list.d/kubernetes.list +RUN apt-get update +RUN apt-get install -y kubectl +RUN kubectl version --client +RUN python -m pip install wget +RUN python -m pip install awscli +RUN pip install boto3 +RUN mkdir /root/.aws +ADD config /root/.aws diff --git a/flux_serve/app/Dockerfile.template b/flux_serve/app/Dockerfile.template new file mode 100644 index 0000000..3a22297 --- /dev/null +++ b/flux_serve/app/Dockerfile.template @@ -0,0 +1,21 @@ +FROM $BASE_IMAGE as base + +RUN apt-get update --fix-missing +RUN apt-get install -y apt-transport-https ca-certificates curl gpg net-tools gettext-base python3-venv g++ +RUN python -m pip install wget +RUN python -m pip install awscli +RUN python -m pip install gradio +RUN python -m pip install "uvicorn[standard]" +RUN python -m pip install fastapi +RUN python -m pip install matplotlib Pillow +RUN pip install diffusers==0.30.3 sentencepiece +RUN pip install httpx + +RUN apt-get update +RUN mkdir -p /etc/apt/keyrings/ +RUN curl -fsSL https://pkgs.k8s.io/core:/stable:/v1.29/deb/Release.key | gpg --dearmor -o /etc/apt/keyrings/kubernetes-apt-keyring.gpg +RUN echo 'deb [signed-by=/etc/apt/keyrings/kubernetes-apt-keyring.gpg] https://pkgs.k8s.io/core:/stable:/v1.29/deb/ /' | tee /etc/apt/sources.list.d/kubernetes.list +RUN apt-get update +RUN apt-get install -y kubectl +RUN kubectl version --client +COPY * / diff --git a/flux_serve/app/build-assets.sh b/flux_serve/app/build-assets.sh new file mode 100755 index 0000000..ee2439a --- /dev/null +++ b/flux_serve/app/build-assets.sh @@ -0,0 +1,50 @@ +#!/bin/bash -x +DLC_ECR_ACCOUNT="763104351884" + +DLC_NEURON_IMAGE="pytorch-inference-neuronx" +DLC_NEURON_TAG=$BASE_IMAGE_TAG +DLC_NEURON_ECR=$DLC_ECR_ACCOUNT".dkr.ecr.us-west-2.amazonaws.com" +if [ "$IMAGE_TAG" == "amd64-neuron" ]; then + docker logout + aws ecr get-login-password --region us-west-2 | docker login --username AWS --password-stdin $DLC_NEURON_ECR + docker pull $DLC_NEURON_ECR/$DLC_NEURON_IMAGE:$DLC_NEURON_TAG + dlc_xla_image_id=$(docker images | grep $DLC_ECR_ACCOUNT | grep $DLC_NEURON_IMAGE | awk '{print $3}') + docker tag $dlc_xla_image_id $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$BASE_REPO:$DLC_NEURON_TAG + docker logout + aws ecr get-login-password --region $AWS_REGION | docker login --username AWS --password-stdin $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$BASE_REPO + docker push $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$BASE_REPO:$DLC_NEURON_TAG +fi +DLC_CUDA_IMAGE="pytorch-inference" +DLC_CUDA_TAG=$BASE_IMAGE_TAG +DLC_CUDA_ECR=$DLC_ECR_ACCOUNT".dkr.ecr.us-east-1.amazonaws.com" +if [ "$IMAGE_TAG" == "amd64-cuda" ]; then + docker logout + aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin $DLC_CUDA_ECR + docker pull $DLC_CUDA_ECR/$DLC_CUDA_IMAGE:$DLC_CUDA_TAG + dlc_cuda_image_id=$(docker images | grep $DLC_ECR_ACCOUNT | grep $DLC_CUDA_TAG | awk '{print $3}') + docker tag $dlc_cuda_image_id $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$BASE_REPO:$DLC_CUDA_TAG + docker logout + aws ecr get-login-password --region $AWS_REGION | docker login --username AWS --password-stdin $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$BASE_REPO + docker push $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$BASE_REPO:$DLC_CUDA_TAG +fi +DLC_ARM_CPU_IMAGE="pytorch-inference-graviton" +DLC_ARM_CPU_TAG=$BASE_IMAGE_TAG +DLC_ARM_CPU_ECR=$DLC_ECR_ACCOUNT".dkr.ecr.us-east-1.amazonaws.com" +if [ "$IMAGE_TAG" == "aarch64-cpu" ]; then + docker logout + aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin $DLC_ARM_CPU_ECR + docker pull $DLC_ARM_CPU_ECR/$DLC_ARM_CPU_IMAGE:$DLC_ARM_CPU_TAG + dlc_arm_cpu_image_id=$(docker images | grep $DLC_ECR_ACCOUNT | grep $DLC_ARM_CPU_TAG | awk '{print $3}') + docker tag $dlc_arm_cpu_image_id $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$BASE_REPO:$DLC_ARM_CPU_TAG + docker logout + aws ecr get-login-password --region $AWS_REGION | docker login --username AWS --password-stdin $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$BASE_REPO + docker push $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$BASE_REPO:$DLC_ARM_CPU_TAG +fi + +docker images + +ASSETS="-assets" +export IMAGE=$AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$BASE_REPO:$IMAGE_TAG$ASSETS +aws ecr get-login-password --region $AWS_REGION | docker login --username AWS --password-stdin $IMAGE +docker build -t $IMAGE --build-arg ai_chip=$IMAGE_TAG -f Dockerfile-assets . +docker push $IMAGE diff --git a/flux_serve/app/build.sh b/flux_serve/app/build.sh new file mode 100755 index 0000000..3beda15 --- /dev/null +++ b/flux_serve/app/build.sh @@ -0,0 +1,22 @@ +#!/bin/bash -x + +export AWS_ACCOUNT_ID=$(aws sts get-caller-identity --output text --query Account) + +ASSETS="-assets" +export BASE_IMAGE=$AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$BASE_REPO:$BASE_IMAGE_TAG +export ASSETS_IMAGE=$AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$BASE_REPO:$IMAGE_TAG$ASSETS +export IMAGE=$AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/$BASE_REPO:$IMAGE_TAG + +#if [ "$IMAGE_TAG" == "1.13.1-neuronx-py310-sdk2.17.0-ubuntu20.04" ]; then +# docker tag $dlc_xla_image_id $BASE_IMAGE +#fi +#if [ "$IMAGE_TAG" == "2.0.1-gpu-py310-cu118-ubuntu20.04-ec2" ]; then +# docker tag $dlc_cuda_image_id $BASE_IMAGE +#fi +#docker images + +cat Dockerfile.template | envsubst > Dockerfile +cat Dockerfile +aws ecr get-login-password --region $AWS_REGION | docker login --username AWS --password-stdin $BASE_IMAGE +docker build -t $IMAGE . +docker push $IMAGE diff --git a/flux_serve/app/flux_gradio.py b/flux_serve/app/flux_gradio.py new file mode 100644 index 0000000..131ef9e --- /dev/null +++ b/flux_serve/app/flux_gradio.py @@ -0,0 +1,143 @@ +import gradio as gr +import requests +from PIL import Image +import io +import os +from fastapi import FastAPI +import base64 +import asyncio +import httpx + +app = FastAPI() + +model_id=os.environ['MODEL_ID'] + +models = [ + { + 'name': '256x144', + 'host_env': 'FLUX_NEURON_256X144_MODEL_API_SERVICE_HOST', + 'port_env': 'FLUX_NEURON_256X144_MODEL_API_SERVICE_PORT', + 'height': 256, + 'width': 144 + }, + { + 'name': '1024x576', + 'host_env': 'FLUX_NEURON_1024X576_MODEL_API_SERVICE_HOST', + 'port_env': 'FLUX_NEURON_1024X576_MODEL_API_SERVICE_PORT', + 'height': 1024, + 'width': 576 + }, + { + 'name': '512x512', + 'host_env': 'FLUX_NEURON_512X512_MODEL_API_SERVICE_HOST', + 'port_env': 'FLUX_NEURON_512X512_MODEL_API_SERVICE_PORT', + 'height': 512, + 'width': 512 + } +] + +for model in models: + host = os.environ[model['host_env']] + port = os.environ[model['port_env']] + model['url'] = f"http://{host}:{port}/generate" + +async def fetch_image(client, url, prompt, num_inference_steps): + payload = { + "prompt": prompt, + "num_inference_steps": int(num_inference_steps) + } + try: + response = await client.post(url, json=payload, timeout=60.0) + response.raise_for_status() + data = response.json() + image_bytes = base64.b64decode(data['image']) + image = Image.open(io.BytesIO(image_bytes)) + execution_time = data.get('execution_time', 0) + return image, f"{execution_time:.2f} seconds" + except httpx.RequestError as e: + return None, f"Request Error: {str(e)}" + except Exception as e: + return None, f"Error: {str(e)}" + +async def call_model_api(prompt, num_inference_steps): + async with httpx.AsyncClient() as client: + tasks = [ + fetch_image(client, model['url'], prompt, num_inference_steps) + for model in models + ] + results = await asyncio.gather(*tasks) + images = [] + exec_times = [] + for image, exec_time in results: + images.append(image) + exec_times.append(exec_time) + return images + exec_times + +@app.get("/health") +def healthy(): + return {"message": "Service is healthy"} + +@app.get("/readiness") +def ready(): + return {"message": "Service is ready"} + +with gr.Blocks() as interface: + gr.Markdown(f"# {model_id} Image Generation App") + gr.Markdown("Enter a prompt and specify the number of inference steps to generate images in different shapes.") + + with gr.Row(): + with gr.Column(scale=1): + prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Enter your prompt here...",elem_id="prompt-box") + inference_steps = gr.Number( + label="Inference Steps", + value=10, + precision=0, + info="Enter the number of inference steps; higher number takes more time but produces better image", + elem_id="steps-number" + ) + generate_button = gr.Button("Generate Images",variant="primary") + + with gr.Column(scale=2): + image_components = [] + exec_time_components = [] + + with gr.Row(equal_height=True): + for idx, model in enumerate(models): + with gr.Column(scale=1,min_width=300): + img = gr.Image(label=f"{model['name']}",height=model['height'],width=model['width'],interactive=False) + exec_time = gr.Textbox(label=f"Execution Time ({model['name']})",interactive=False,lines=1,placeholder="Execution time will appear here...") + image_components.append(img) + exec_time_components.append(exec_time) + + # callback for the button + generate_button.click( + fn=call_model_api, + inputs=[prompt, inference_steps], + outputs=image_components + exec_time_components, + api_name="generate_images" + ) +app = gr.mount_gradio_app(app, interface, path="/serve") +''' +interface = gr.Interface( + fn=call_model_api, + inputs=[ + gr.Textbox(label="Prompt", lines=1, placeholder="Enter your prompt here..."), + gr.Number( + label="Inference Steps", + value=10, + precision=0, + info="Enter the number of inference steps; higher number takes more time but produces better image" + ) + ], + outputs=[ + gr.Image(label=f"Image from {models[0]['name']}", height=models[0]['height'], width=models[0]['width']), + gr.Textbox(label=f"Execution Time ({models[0]['name']})"), + gr.Image(label=f"Image from {models[1]['name']}", height=models[1]['height'], width=models[1]['width']), + gr.Textbox(label=f"Execution Time ({models[1]['name']})"), + gr.Image(label=f"Image from {models[2]['name']}", height=models[2]['height'], width=models[2]['width']), + gr.Textbox(label=f"Execution Time ({models[2]['name']})"), + ], + description="Enter a prompt and specify the number of inference steps to generate images using the model pipeline." +) +app = gr.mount_gradio_app(app,interface, path="/serve") +''' diff --git a/flux_serve/app/flux_model_api.py b/flux_serve/app/flux_model_api.py new file mode 100644 index 0000000..8f424ea --- /dev/null +++ b/flux_serve/app/flux_model_api.py @@ -0,0 +1,259 @@ +import math +import boto3 +import time +import argparse +import torch +import torch.nn as nn +import torch_neuronx +import neuronx_distributed +import os +from fastapi import FastAPI, HTTPException +from pydantic import BaseModel, Field +from typing import Any, Dict, Optional, Union +from huggingface_hub import login +from diffusers import FluxPipeline +from diffusers.models.modeling_outputs import Transformer2DModelOutput +from starlette.responses import StreamingResponse +import base64 + +cw_namespace='hw-agnostic-infer' +cloudwatch = boto3.client('cloudwatch', region_name='us-west-2') + +# Initialize FastAPI app +app = FastAPI() + +# Environment Variables +app_name=os.environ['APP'] +nodepool=os.environ['NODEPOOL'] +model_id = os.environ['MODEL_ID'] +device = os.environ["DEVICE"] +pod_name = os.environ['POD_NAME'] +hf_token = os.environ['HUGGINGFACE_TOKEN'].strip() +height = int(os.environ['HEIGHT']) +width = int(os.environ['WIDTH']) +max_sequence_length = int(os.environ['MAX_SEQ_LEN']) +guidance_scale = float(os.environ['GUIDANCE_SCALE']) +COMPILER_WORKDIR_ROOT = os.environ['COMPILER_WORKDIR_ROOT'] + +DTYPE=torch.bfloat16 + +def cw_pub_metric(metric_name,metric_value,metric_unit): + response = cloudwatch.put_metric_data( + Namespace=cw_namespace, + MetricData=[ + { + 'MetricName':metric_name, + 'Value':metric_value, + 'Unit':metric_unit, + }, + ] + ) + print(f"in pub_deployment_counter - response:{response}") + return response + +# Model Paths +TEXT_ENCODER_PATH = os.path.join( + COMPILER_WORKDIR_ROOT, + 'text_encoder_1/compiled_model/model.pt') +TEXT_ENCODER_2_PATH = os.path.join( + COMPILER_WORKDIR_ROOT, + 'text_encoder_2/compiled_model/model.pt') +VAE_DECODER_PATH = os.path.join( + COMPILER_WORKDIR_ROOT, + 'decoder/compiled_model/model.pt') + +EMBEDDERS_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'transformer/compiled_model/embedders') +OUT_LAYERS_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'transformer/compiled_model/out_layers') +SINGLE_TRANSFORMER_BLOCKS_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'transformer/compiled_model/single_transformer_blocks') +TRANSFORMER_BLOCKS_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'transformer/compiled_model/transformer_blocks') + +# Login to Hugging Face +login(hf_token, add_to_git_credential=True) + +class GenerateImageRequest(BaseModel): + prompt: str + num_inference_steps: int + +class GenerateImageResponse(BaseModel): + image: str = Field(..., description="Base64-encoded image") + execution_time: float + +class NeuronFluxTransformer2DModel(nn.Module): + def __init__( + self, + config, + x_embedder, + context_embedder + ): + super().__init__() + with torch_neuronx.experimental.neuron_cores_context(start_nc=4, + nc_count=8): + self.embedders_model = \ + neuronx_distributed.trace.parallel_model_load(EMBEDDERS_DIR) + self.transformer_blocks_model = \ + neuronx_distributed.trace.parallel_model_load( + TRANSFORMER_BLOCKS_DIR) + self.single_transformer_blocks_model = \ + neuronx_distributed.trace.parallel_model_load( + SINGLE_TRANSFORMER_BLOCKS_DIR) + self.out_layers_model = \ + neuronx_distributed.trace.parallel_model_load( + OUT_LAYERS_DIR) + self.config = config + self.x_embedder = x_embedder + self.context_embedder = context_embedder + self.device = torch.device("cpu") + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.Tensor = None, + pooled_projections: torch.Tensor = None, + timestep: torch.LongTensor = None, + img_ids: torch.Tensor = None, + txt_ids: torch.Tensor = None, + guidance: torch.Tensor = None, + joint_attention_kwargs: Optional[Dict[str, Any]] = None, + return_dict: bool = False, + ) -> Union[torch.FloatTensor, Transformer2DModelOutput]: + + hidden_states = self.x_embedder(hidden_states) + + hidden_states, temb, image_rotary_emb = self.embedders_model( + hidden_states, + timestep, + guidance, + pooled_projections, + txt_ids, + img_ids + ) + + encoder_hidden_states = self.context_embedder(encoder_hidden_states) + + image_rotary_emb = image_rotary_emb.type(DTYPE) + + encoder_hidden_states, hidden_states = self.transformer_blocks_model( + hidden_states, + encoder_hidden_states, + temb, + image_rotary_emb + ) + + hidden_states = torch.cat([encoder_hidden_states, hidden_states], + dim=1) + + hidden_states = self.single_transformer_blocks_model( + hidden_states, + temb, + image_rotary_emb + ) + + hidden_states = hidden_states.to(DTYPE) + + return self.out_layers_model( + hidden_states, + encoder_hidden_states, + temb + ) + + +class NeuronFluxCLIPTextEncoderModel(nn.Module): + def __init__(self, dtype, encoder): + super().__init__() + self.dtype = dtype + self.encoder = encoder + self.device = torch.device("cpu") + + def forward(self, emb, output_hidden_states): + output = self.encoder(emb) + output = CLIPEncoderOutput(output) + return output + + +class CLIPEncoderOutput(): + def __init__(self, dictionary): + self.pooler_output = dictionary["pooler_output"] + + +class NeuronFluxT5TextEncoderModel(nn.Module): + def __init__(self, dtype, encoder): + super().__init__() + self.dtype = dtype + self.encoder = encoder + self.device = torch.device("cpu") + + def forward(self, emb, output_hidden_states): + return torch.unsqueeze(self.encoder(emb)["last_hidden_state"], 1) + +# Load the model pipeline +def load_model(): + pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=DTYPE) + with torch_neuronx.experimental.neuron_cores_context(start_nc=0): + pipe.text_encoder = NeuronFluxCLIPTextEncoderModel( + pipe.text_encoder.dtype, + torch.jit.load(TEXT_ENCODER_PATH)) + with torch_neuronx.experimental.neuron_cores_context(start_nc=8): + pipe.text_encoder_2 = NeuronFluxT5TextEncoderModel( + pipe.text_encoder_2.dtype, + torch.jit.load(TEXT_ENCODER_2_PATH)) + pipe.transformer = NeuronFluxTransformer2DModel( + pipe.transformer.config, + pipe.transformer.x_embedder, + pipe.transformer.context_embedder) + with torch_neuronx.experimental.neuron_cores_context(start_nc=8): + pipe.vae.decoder = torch.jit.load(VAE_DECODER_PATH) + return pipe + +model = load_model() + +# Define the image generation endpoint +@app.post("/generate", response_model=GenerateImageResponse) +def generate_image(request: GenerateImageRequest): + start_time = time.time() + try: + model_args = { + 'prompt': request.prompt, + 'height': height, + 'width': width, + 'max_sequence_length': max_sequence_length, + 'num_inference_steps': request.num_inference_steps, + 'guidance_scale': guidance_scale + } + with torch.no_grad(): + output = model(**model_args) + image = output.images[0] + # Save image to bytes + from io import BytesIO + buf = BytesIO() + image.save(buf, format='PNG') + image_bytes = buf.getvalue() + image_base64 = base64.b64encode(image_bytes).decode('utf-8') + total_time = time.time() - start_time + counter_metric=app_name+'-counter' + cw_pub_metric(counter_metric,1,'Count') + counter_metric=nodepool + cw_pub_metric(counter_metric,1,'Count') + latency_metric=app_name+'-latency' + cw_pub_metric(latency_metric,total_time,'Seconds') + return GenerateImageResponse(image=image_base64, execution_time=total_time) + except Exception as e: + raise HTTPException(status_code=500, detail=f"Image serialization failed: {img_err}") + +# Health and readiness endpoints +@app.get("/health") +def healthy(): + return {"message": f"{pod_name} is healthy"} + +@app.get("/readiness") +def ready(): + return {"message": f"{pod_name} is ready"} diff --git a/flux_serve/oci-image-build/README.md b/flux_serve/oci-image-build/README.md new file mode 100644 index 0000000..bf145c9 --- /dev/null +++ b/flux_serve/oci-image-build/README.md @@ -0,0 +1,34 @@ + +* Fork https://github.com/aws-samples/scalable-hw-agnostic-inference and populate the `GITHUB_USER` and `GITHUB_OAUTH_TOKEN` based on `Settings/Developer Settings/Personal access tokens`. +* Check the latest [DLC](https://github.com/aws/deep-learning-containers/blob/master/available_images.md) for `BASE_IMAGE_AMD_XLA_TAG` and `BASE_IMAGE_AMD_CUD_TAG` values. +* Export the following variables: +```bash +export AWS_ACCOUNT_ID=$(aws sts get-caller-identity --output text --query Account) +export AWS_REGION=us-west-2 +export BASE_IMAGE_AMD_XLA_TAG=2.5.1-neuronx-py310-sdk2.21.0-ubuntu22.04 +export BASE_IMAGE_AMD_CUD_TAG=2.3.0-gpu-py311-cu121-ubuntu20.04-ec2 +export BASE_IMAGE_ARM_CPU_TAG=2.3.0-cpu-py311-ubuntu20.04-ec2 +export IMAGE_AMD_XLA_TAG=amd64-neuron +export IMAGE_AMD_CUD_TAG=amd64-cuda +export IMAGE_ARM_CPU_TAG=aarch64-cpu +export BASE_REPO=model +export BASE_TAG=multiarch-ubuntu +export BASE_AMD_TAG=amd64 +export GITHUB_BRANCH=master +export GITHUB_USER=yahavb +export GITHUB_REPO=scalable-hw-agnostic-inference +export MODEL_DIR=sd2_compile_dir +export CF_STACK=flux-oci-image-inference-cdk +``` +* Install needed packages + +```bash +npm uninstall -g aws-cdk +npm install -g aws-cdk +``` + +* Deploy the pipeline + +```bash +./deploy-pipeline.sh +``` diff --git a/flux_serve/oci-image-build/cdk.out/cdk.out b/flux_serve/oci-image-build/cdk.out/cdk.out new file mode 100644 index 0000000..c6e6125 --- /dev/null +++ b/flux_serve/oci-image-build/cdk.out/cdk.out @@ -0,0 +1 @@ +{"version":"38.0.1"} \ No newline at end of file diff --git a/flux_serve/oci-image-build/cdk.out/flux-oci-image-inference-cdk.assets.json b/flux_serve/oci-image-build/cdk.out/flux-oci-image-inference-cdk.assets.json new file mode 100644 index 0000000..b7f5cc8 --- /dev/null +++ b/flux_serve/oci-image-build/cdk.out/flux-oci-image-inference-cdk.assets.json @@ -0,0 +1,20 @@ +{ + "version": "38.0.1", + "files": { + "54c3377f007eccdd78146d6ef1c9fe016f1cd6dc99c2088e7f65bb90e95689cc": { + "source": { + "path": "flux-oci-image-inference-cdk.template.json", + "packaging": "file" + }, + "destinations": { + "133573815653-us-west-2": { + "bucketName": "cdk-hnb659fds-assets-133573815653-us-west-2", + "objectKey": 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GITHUBREPO=$GITHUB_REPO --parameters GITHUBUSER=$GITHUB_USER --parameters GITHUBBRANCH=$GITHUB_BRANCH --parameters GITHUBOAUTHTOKEN=$GITHUB_OAUTH_TOKEN --parameters BASEIMAGEARMCPUTAG=$BASE_IMAGE_ARM_CPU_TAG --parameters IMAGEARMCPUTAG=$IMAGE_ARM_CPU_TAG diff --git a/flux_serve/oci-image-build/package-lock.json b/flux_serve/oci-image-build/package-lock.json new file mode 100644 index 0000000..813ddc3 --- /dev/null +++ b/flux_serve/oci-image-build/package-lock.json @@ -0,0 +1,615 @@ +{ + "name": "oci-image-build", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "dependencies": { + "aws-cdk-lib": "^2.173.4", + "ts-node": "^10.9.2" + }, + "devDependencies": { + "typescript": "^5.7.2" + } + }, + "node_modules/@aws-cdk/asset-awscli-v1": { + "version": "2.2.217", + "resolved": "https://registry.npmjs.org/@aws-cdk/asset-awscli-v1/-/asset-awscli-v1-2.2.217.tgz", + "integrity": 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0000000..8926107 --- /dev/null +++ b/flux_serve/oci-image-build/package.json @@ -0,0 +1,9 @@ +{ + "dependencies": { + "aws-cdk-lib": "^2.173.4", + "ts-node": "^10.9.2" + }, + "devDependencies": { + "typescript": "^5.7.2" + } +} diff --git a/flux_serve/oci-image-build/pipeline-stack.js b/flux_serve/oci-image-build/pipeline-stack.js new file mode 100644 index 0000000..2cace63 --- /dev/null +++ b/flux_serve/oci-image-build/pipeline-stack.js @@ -0,0 +1,329 @@ +"use strict"; +var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) { + if (k2 === undefined) k2 = k; + var desc = Object.getOwnPropertyDescriptor(m, k); + if (!desc || ("get" in desc ? !m.__esModule : desc.writable || desc.configurable)) { + desc = { enumerable: true, get: function() { return m[k]; } }; + } + Object.defineProperty(o, k2, desc); +}) : (function(o, m, k, k2) { + if (k2 === undefined) k2 = k; + o[k2] = m[k]; +})); +var __setModuleDefault = (this && this.__setModuleDefault) || (Object.create ? (function(o, v) { + Object.defineProperty(o, "default", { enumerable: true, value: v }); +}) : function(o, v) { + o["default"] = v; +}); +var __importStar = (this && this.__importStar) || (function () { + var ownKeys = function(o) { + ownKeys = Object.getOwnPropertyNames || function (o) { + var ar = []; + for (var k in o) if (Object.prototype.hasOwnProperty.call(o, k)) ar[ar.length] = k; + return ar; + }; + return ownKeys(o); + }; + return function (mod) { + if (mod && mod.__esModule) return mod; + var result = {}; + if (mod != null) for (var k = ownKeys(mod), i = 0; i < k.length; i++) if (k[i] !== "default") __createBinding(result, mod, k[i]); + __setModuleDefault(result, mod); + return result; + }; +})(); +Object.defineProperty(exports, "__esModule", { value: true }); +exports.PipelineStack = void 0; +const aws_cdk_lib_1 = require("aws-cdk-lib"); +const ecr = __importStar(require("aws-cdk-lib/aws-ecr")); +const codebuild = __importStar(require("aws-cdk-lib/aws-codebuild")); +const codepipeline = __importStar(require("aws-cdk-lib/aws-codepipeline")); +const codepipeline_actions = __importStar(require("aws-cdk-lib/aws-codepipeline-actions")); +const iam = __importStar(require("aws-cdk-lib/aws-iam")); +const secretsmanager = __importStar(require("aws-cdk-lib/aws-secretsmanager")); +const cdk = __importStar(require("aws-cdk-lib/core")); +class PipelineStack extends aws_cdk_lib_1.Stack { + constructor(scope, id, props) { + super(scope, id, props); + const BASE_REPO = new aws_cdk_lib_1.CfnParameter(this, "BASEREPO", { type: "String" }); + const BASE_IMAGE_AMD_XLA_TAG = new aws_cdk_lib_1.CfnParameter(this, "BASEIMAGEAMDXLATAG", { type: "String" }); + const BASE_IMAGE_AMD_CUD_TAG = new aws_cdk_lib_1.CfnParameter(this, "BASEIMAGEAMDCUDTAG", { type: "String" }); + const BASE_IMAGE_ARM_CPU_TAG = new aws_cdk_lib_1.CfnParameter(this, "BASEIMAGEARMCPUTAG", { type: "String" }); + const IMAGE_AMD_XLA_TAG = new aws_cdk_lib_1.CfnParameter(this, "IMAGEAMDXLATAG", { type: "String" }); + const IMAGE_AMD_CUD_TAG = new aws_cdk_lib_1.CfnParameter(this, "IMAGEAMDCUDTAG", { type: "String" }); + const IMAGE_ARM_CPU_TAG = new aws_cdk_lib_1.CfnParameter(this, "IMAGEARMCPUTAG", { type: "String" }); + const GITHUB_OAUTH_TOKEN = new aws_cdk_lib_1.CfnParameter(this, "GITHUBOAUTHTOKEN", { type: "String" }); + const GITHUB_USER = new aws_cdk_lib_1.CfnParameter(this, "GITHUBUSER", { type: "String" }); + const GITHUB_REPO = new aws_cdk_lib_1.CfnParameter(this, "GITHUBREPO", { type: "String" }); + const GITHUB_BRANCH = new aws_cdk_lib_1.CfnParameter(this, "GITHUBBRANCH", { type: "String" }); + // uncomment when you test the stack and dont want to manually delete the ecr registry + const base_registry = new ecr.Repository(this, `base_repo`, { + repositoryName: BASE_REPO.valueAsString, + imageScanOnPush: true + }); + //const base_registry = ecr.Repository.fromRepositoryName(this,`base_repo`,BASE_REPO.valueAsString) + //create a roleARN for codebuild + const buildRole = new iam.Role(this, 'BaseCodeBuildDeployRole', { + roleName: 'hwagnisticBaseCodeBuildDeployRole', + assumedBy: new iam.ServicePrincipal('codebuild.amazonaws.com'), + }); + buildRole.addToPolicy(new iam.PolicyStatement({ + resources: ['*'], + actions: ['ssm:*', 's3:*'], + })); + const githubSecret = new secretsmanager.Secret(this, 'githubSecret', { + secretObjectValue: { + token: aws_cdk_lib_1.SecretValue.unsafePlainText(GITHUB_OAUTH_TOKEN.valueAsString) + }, + }); + const githubOAuthToken = aws_cdk_lib_1.SecretValue.secretsManager(githubSecret.secretArn, { jsonField: 'token' }); + new cdk.CfnOutput(this, 'githubOAuthTokenRuntimeOutput1', { + //value: SecretValue.secretsManager("githubtoken",{jsonField: "token"}).toString() + value: githubSecret.secretValueFromJson('token').toString() + }); + new cdk.CfnOutput(this, 'githubOAuthTokenRuntimeOutput2', { + value: aws_cdk_lib_1.SecretValue.secretsManager(githubSecret.secretArn, { jsonField: "token" }).toString() + }); + const base_image_amd_cud_build = new codebuild.Project(this, `ImageCudAmdBuild`, { + environment: { privileged: true, buildImage: codebuild.LinuxBuildImage.AMAZON_LINUX_2_3 }, + cache: codebuild.Cache.local(codebuild.LocalCacheMode.DOCKER_LAYER, codebuild.LocalCacheMode.CUSTOM), + role: buildRole, + buildSpec: codebuild.BuildSpec.fromObject({ + version: "0.2", + env: { + 'exported-variables': [ + 'AWS_ACCOUNT_ID', 'AWS_REGION', 'BASE_REPO', 'IMAGE_AMD_CUD_TAG', 'BASE_IMAGE_AMD_CUD_TAG' + ], + }, + phases: { + build: { + commands: [ + `export AWS_ACCOUNT_ID="${this.account}"`, + `export AWS_REGION="${this.region}"`, + `export BASE_REPO="${BASE_REPO.valueAsString}"`, + `export IMAGE_TAG="${IMAGE_AMD_CUD_TAG.valueAsString}"`, + `export BASE_IMAGE_TAG="${BASE_IMAGE_AMD_CUD_TAG.valueAsString}"`, + `cd app`, + `chmod +x ./build.sh && ./build.sh` + ], + } + }, + artifacts: { + files: ['imageDetail.json'] + }, + }), + }); + const base_image_arm_cpu_build = new codebuild.Project(this, `ImageCpuArmBuild`, { + environment: { privileged: true, buildImage: codebuild.LinuxBuildImage.AMAZON_LINUX_2_ARM_2 }, + cache: codebuild.Cache.local(codebuild.LocalCacheMode.DOCKER_LAYER, codebuild.LocalCacheMode.CUSTOM), + role: buildRole, + buildSpec: codebuild.BuildSpec.fromObject({ + version: "0.2", + env: { + 'exported-variables': [ + 'AWS_ACCOUNT_ID', 'AWS_REGION', 'BASE_REPO', 'IMAGE_ARM_CPU_TAG', 'BASE_IMAGE_ARM_CPU_TAG' + ], + }, + phases: { + build: { + commands: [ + `export AWS_ACCOUNT_ID="${this.account}"`, + `export AWS_REGION="${this.region}"`, + `export BASE_REPO="${BASE_REPO.valueAsString}"`, + `export IMAGE_TAG="${IMAGE_ARM_CPU_TAG.valueAsString}"`, + `export BASE_IMAGE_TAG="${BASE_IMAGE_ARM_CPU_TAG.valueAsString}"`, + `cd app`, + `chmod +x ./build.sh && ./build.sh` + ], + } + }, + artifacts: { + files: ['imageDetail.json'] + }, + }), + }); + const base_image_amd_xla_build = new codebuild.Project(this, `ImageXlaAmdBuild`, { + environment: { privileged: true, buildImage: codebuild.LinuxBuildImage.AMAZON_LINUX_2_3 }, + cache: codebuild.Cache.local(codebuild.LocalCacheMode.DOCKER_LAYER, codebuild.LocalCacheMode.CUSTOM), + role: buildRole, + buildSpec: codebuild.BuildSpec.fromObject({ + version: "0.2", + env: { + 'exported-variables': [ + 'AWS_ACCOUNT_ID', 'AWS_REGION', 'BASE_REPO', 'IMAGE_AMD_XLA_TAG', 'BASE_IMAGE_AMD_XLA_TAG' + ], + }, + phases: { + build: { + commands: [ + `export AWS_ACCOUNT_ID="${this.account}"`, + `export AWS_REGION="${this.region}"`, + `export BASE_REPO="${BASE_REPO.valueAsString}"`, + `export IMAGE_TAG="${IMAGE_AMD_XLA_TAG.valueAsString}"`, + `export BASE_IMAGE_TAG="${BASE_IMAGE_AMD_XLA_TAG.valueAsString}"`, + `cd app`, + `chmod +x ./build.sh && ./build.sh` + ], + } + }, + artifacts: { + files: ['imageDetail.json'] + }, + }), + }); + const assets_image_xla_amd_build = new codebuild.Project(this, `AssetsImageXlaAmdBuild`, { + environment: { privileged: true, buildImage: codebuild.LinuxBuildImage.AMAZON_LINUX_2_3 }, + cache: codebuild.Cache.local(codebuild.LocalCacheMode.DOCKER_LAYER, codebuild.LocalCacheMode.CUSTOM), + role: buildRole, + buildSpec: codebuild.BuildSpec.fromObject({ + version: "0.2", + env: { + 'exported-variables': [ + 'AWS_ACCOUNT_ID', 'AWS_REGION', 'BASE_REPO', 'IMAGE_AMD_XLA_TAG', 'BASE_IMAGE_AMD_XLA_TAG' + ], + }, + phases: { + build: { + commands: [ + `export AWS_ACCOUNT_ID="${this.account}"`, + `export AWS_REGION="${this.region}"`, + `export BASE_REPO="${BASE_REPO.valueAsString}"`, + `export IMAGE_TAG="${IMAGE_AMD_XLA_TAG.valueAsString}"`, + `export BASE_IMAGE_TAG="${BASE_IMAGE_AMD_XLA_TAG.valueAsString}"`, + `cd app`, + `chmod +x ./build-assets.sh && ./build-assets.sh` + ], + } + }, + artifacts: { + files: ['imageDetail.json'] + }, + }), + }); + const assets_image_cpu_arm_build = new codebuild.Project(this, `AssetsImageCpuArmBuild`, { + environment: { privileged: true, buildImage: codebuild.LinuxBuildImage.AMAZON_LINUX_2_ARM_2 }, + cache: codebuild.Cache.local(codebuild.LocalCacheMode.DOCKER_LAYER, codebuild.LocalCacheMode.CUSTOM), + role: buildRole, + buildSpec: codebuild.BuildSpec.fromObject({ + version: "0.2", + env: { + 'exported-variables': [ + 'AWS_ACCOUNT_ID', 'AWS_REGION', 'BASE_REPO', 'IMAGE_ARM_CPU_TAG', 'BASE_IMAGE_ARM_CPU_TAG' + ], + }, + phases: { + build: { + commands: [ + `export AWS_ACCOUNT_ID="${this.account}"`, + `export AWS_REGION="${this.region}"`, + `export BASE_REPO="${BASE_REPO.valueAsString}"`, + `export IMAGE_TAG="${IMAGE_ARM_CPU_TAG.valueAsString}"`, + `export BASE_IMAGE_TAG="${BASE_IMAGE_ARM_CPU_TAG.valueAsString}"`, + `cd app`, + `chmod +x ./build-assets.sh && ./build-assets.sh` + ], + } + }, + artifacts: { + files: ['imageDetail.json'] + }, + }), + }); + const assets_image_cud_amd_build = new codebuild.Project(this, `AssetsImageCudAmdBuild`, { + environment: { privileged: true, buildImage: codebuild.LinuxBuildImage.AMAZON_LINUX_2_3 }, + cache: codebuild.Cache.local(codebuild.LocalCacheMode.DOCKER_LAYER, codebuild.LocalCacheMode.CUSTOM), + role: buildRole, + buildSpec: codebuild.BuildSpec.fromObject({ + version: "0.2", + env: { + 'exported-variables': [ + 'AWS_ACCOUNT_ID', 'AWS_REGION', 'BASE_REPO', 'IMAGE_AMD_CUD_TAG', 'BASE_IMAGE_AMD_CUD_TAG' + ], + }, + phases: { + build: { + commands: [ + `export AWS_ACCOUNT_ID="${this.account}"`, + `export AWS_REGION="${this.region}"`, + `export BASE_REPO="${BASE_REPO.valueAsString}"`, + `export IMAGE_TAG="${IMAGE_AMD_CUD_TAG.valueAsString}"`, + `export BASE_IMAGE_TAG="${BASE_IMAGE_AMD_CUD_TAG.valueAsString}"`, + `cd app`, + `chmod +x ./build-assets.sh && ./build-assets.sh` + ], + } + }, + artifacts: { + files: ['imageDetail.json'] + }, + }), + }); + //we allow the buildProject principal to push images to ecr + base_registry.grantPullPush(assets_image_cud_amd_build.grantPrincipal); + base_registry.grantPullPush(assets_image_xla_amd_build.grantPrincipal); + base_registry.grantPullPush(assets_image_cpu_arm_build.grantPrincipal); + base_registry.grantPullPush(base_image_amd_xla_build.grantPrincipal); + base_registry.grantPullPush(base_image_amd_cud_build.grantPrincipal); + base_registry.grantPullPush(base_image_arm_cpu_build.grantPrincipal); + // here we define our pipeline and put together the assembly line + const sourceOutput = new codepipeline.Artifact(); + const basebuildpipeline = new codepipeline.Pipeline(this, `BuildBasePipeline`); + basebuildpipeline.addStage({ + stageName: 'Source', + actions: [ + new codepipeline_actions.GitHubSourceAction({ + actionName: 'GitHub_Source', + owner: GITHUB_USER.valueAsString, + repo: GITHUB_REPO.valueAsString, + branch: GITHUB_BRANCH.valueAsString, + output: sourceOutput, + oauthToken: aws_cdk_lib_1.SecretValue.secretsManager("githubtoken", { jsonField: "token" }), + trigger: codepipeline_actions.GitHubTrigger.WEBHOOK, + //oauthToken: SecretValue.unsafePlainText(GITHUB_OAUTH_TOKEN.valueAsString) + }) + ] + }); + basebuildpipeline.addStage({ + stageName: 'ImageBuild', + actions: [ + new codepipeline_actions.CodeBuildAction({ + actionName: 'AssetsImageXlaAmdBuild', + input: sourceOutput, + runOrder: 1, + project: assets_image_xla_amd_build + }), + new codepipeline_actions.CodeBuildAction({ + actionName: 'AssetsImageCudAmdBuild', + input: sourceOutput, + runOrder: 1, + project: assets_image_cud_amd_build + }), + new codepipeline_actions.CodeBuildAction({ + actionName: 'AssetsImageCpuArmBuild', + input: sourceOutput, + runOrder: 1, + project: assets_image_cpu_arm_build + }), + new codepipeline_actions.CodeBuildAction({ + actionName: 'BaseImageAmdXlaBuild', + input: sourceOutput, + runOrder: 2, + project: base_image_amd_xla_build + }), + new codepipeline_actions.CodeBuildAction({ + actionName: 'BaseImageArmCpuBuild', + input: sourceOutput, + runOrder: 2, + project: base_image_arm_cpu_build + }), + new codepipeline_actions.CodeBuildAction({ + actionName: 'BaseImageAmdCudBuild', + input: sourceOutput, + runOrder: 2, + project: base_image_amd_cud_build + }) + ] + }); + } +} +exports.PipelineStack = PipelineStack; diff --git a/flux_serve/oci-image-build/pipeline-stack.ts b/flux_serve/oci-image-build/pipeline-stack.ts new file mode 100644 index 0000000..a5beb03 --- /dev/null +++ b/flux_serve/oci-image-build/pipeline-stack.ts @@ -0,0 +1,321 @@ +import { Stack, StackProps,CfnParameter,SecretValue} from 'aws-cdk-lib'; +import { Construct } from 'constructs' +import * as codecommit from 'aws-cdk-lib/aws-codecommit'; +import * as ecr from 'aws-cdk-lib/aws-ecr'; +import * as codebuild from 'aws-cdk-lib/aws-codebuild'; +import * as codepipeline from 'aws-cdk-lib/aws-codepipeline'; +import * as codepipeline_actions from 'aws-cdk-lib/aws-codepipeline-actions'; +import * as iam from "aws-cdk-lib/aws-iam"; +import * as secretsmanager from 'aws-cdk-lib/aws-secretsmanager'; +import * as cdk from 'aws-cdk-lib/core'; +import * as cfn from 'aws-cdk-lib/aws-cloudformation'; + +export class PipelineStack extends Stack { + constructor(scope: Construct, id: string, props?: StackProps) { + super(scope, id, props); + const BASE_REPO = new CfnParameter(this,"BASEREPO",{type:"String"}); + const BASE_IMAGE_AMD_XLA_TAG = new CfnParameter(this,"BASEIMAGEAMDXLATAG",{type:"String"}); + const BASE_IMAGE_AMD_CUD_TAG = new CfnParameter(this,"BASEIMAGEAMDCUDTAG",{type:"String"}); + const BASE_IMAGE_ARM_CPU_TAG = new CfnParameter(this,"BASEIMAGEARMCPUTAG",{type:"String"}); + const IMAGE_AMD_XLA_TAG = new CfnParameter(this,"IMAGEAMDXLATAG",{type:"String"}); + const IMAGE_AMD_CUD_TAG = new CfnParameter(this,"IMAGEAMDCUDTAG",{type:"String"}); + const IMAGE_ARM_CPU_TAG = new CfnParameter(this,"IMAGEARMCPUTAG",{type:"String"}); + const GITHUB_OAUTH_TOKEN = new CfnParameter(this,"GITHUBOAUTHTOKEN",{type:"String"}); + const GITHUB_USER = new CfnParameter(this,"GITHUBUSER",{type:"String"}); + const GITHUB_REPO = new CfnParameter(this,"GITHUBREPO",{type:"String"}); + const GITHUB_BRANCH = new CfnParameter(this,"GITHUBBRANCH",{type:"String"}); + // uncomment when you test the stack and dont want to manually delete the ecr registry + const base_registry = new ecr.Repository(this,`base_repo`,{ + repositoryName:BASE_REPO.valueAsString, + imageScanOnPush: true + }); + + //const base_registry = ecr.Repository.fromRepositoryName(this,`base_repo`,BASE_REPO.valueAsString) + + //create a roleARN for codebuild + const buildRole = new iam.Role(this, 'BaseCodeBuildDeployRole',{ + roleName: 'hwagnisticBaseCodeBuildDeployRole', + assumedBy: new iam.ServicePrincipal('codebuild.amazonaws.com'), + }); + + buildRole.addToPolicy(new iam.PolicyStatement({ + resources: ['*'], + actions: ['ssm:*','s3:*'], + })); + + const githubSecret = new secretsmanager.Secret(this, 'githubSecret', { + secretObjectValue: { + token: SecretValue.unsafePlainText(GITHUB_OAUTH_TOKEN.valueAsString) + }, + }); + const githubOAuthToken = SecretValue.secretsManager(githubSecret.secretArn,{jsonField:'token'}); + new cdk.CfnOutput(this, 'githubOAuthTokenRuntimeOutput1', { + //value: SecretValue.secretsManager("githubtoken",{jsonField: "token"}).toString() + value: githubSecret.secretValueFromJson('token').toString() + }); + new cdk.CfnOutput(this, 'githubOAuthTokenRuntimeOutput2', { + value: SecretValue.secretsManager(githubSecret.secretArn,{jsonField: "token"}).toString() + }); + + const base_image_amd_cud_build = new codebuild.Project(this, `ImageCudAmdBuild`, { + environment: {privileged:true,buildImage: codebuild.LinuxBuildImage.AMAZON_LINUX_2_3}, + cache: codebuild.Cache.local(codebuild.LocalCacheMode.DOCKER_LAYER, codebuild.LocalCacheMode.CUSTOM), + role: buildRole, + buildSpec: codebuild.BuildSpec.fromObject( + { + version: "0.2", + env: { + 'exported-variables': [ + 'AWS_ACCOUNT_ID','AWS_REGION','BASE_REPO','IMAGE_AMD_CUD_TAG','BASE_IMAGE_AMD_CUD_TAG' + ], + }, + phases: { + build: { + commands: [ + `export AWS_ACCOUNT_ID="${this.account}"`, + `export AWS_REGION="${this.region}"`, + `export BASE_REPO="${BASE_REPO.valueAsString}"`, + `export IMAGE_TAG="${IMAGE_AMD_CUD_TAG.valueAsString}"`, + `export BASE_IMAGE_TAG="${BASE_IMAGE_AMD_CUD_TAG.valueAsString}"`, + `cd app`, + `chmod +x ./build.sh && ./build.sh` + ], + } + }, + artifacts: { + files: ['imageDetail.json'] + }, + } + ), + }); + + const base_image_arm_cpu_build = new codebuild.Project(this, `ImageCpuArmBuild`, { + environment: {privileged:true,buildImage: codebuild.LinuxBuildImage.AMAZON_LINUX_2_ARM_2}, + cache: codebuild.Cache.local(codebuild.LocalCacheMode.DOCKER_LAYER, codebuild.LocalCacheMode.CUSTOM), + role: buildRole, + buildSpec: codebuild.BuildSpec.fromObject( + { + version: "0.2", + env: { + 'exported-variables': [ + 'AWS_ACCOUNT_ID','AWS_REGION','BASE_REPO','IMAGE_ARM_CPU_TAG','BASE_IMAGE_ARM_CPU_TAG' + ], + }, + phases: { + build: { + commands: [ + `export AWS_ACCOUNT_ID="${this.account}"`, + `export AWS_REGION="${this.region}"`, + `export BASE_REPO="${BASE_REPO.valueAsString}"`, + `export IMAGE_TAG="${IMAGE_ARM_CPU_TAG.valueAsString}"`, + `export BASE_IMAGE_TAG="${BASE_IMAGE_ARM_CPU_TAG.valueAsString}"`, + `cd app`, + `chmod +x ./build.sh && ./build.sh` + ], + } + }, + artifacts: { + files: ['imageDetail.json'] + }, + } + ), + }); + + const base_image_amd_xla_build = new codebuild.Project(this, `ImageXlaAmdBuild`, { + environment: {privileged:true,buildImage: codebuild.LinuxBuildImage.AMAZON_LINUX_2_3}, + cache: codebuild.Cache.local(codebuild.LocalCacheMode.DOCKER_LAYER, codebuild.LocalCacheMode.CUSTOM), + role: buildRole, + buildSpec: codebuild.BuildSpec.fromObject( + { + version: "0.2", + env: { + 'exported-variables': [ + 'AWS_ACCOUNT_ID','AWS_REGION','BASE_REPO','IMAGE_AMD_XLA_TAG','BASE_IMAGE_AMD_XLA_TAG' + ], + }, + phases: { + build: { + commands: [ + `export AWS_ACCOUNT_ID="${this.account}"`, + `export AWS_REGION="${this.region}"`, + `export BASE_REPO="${BASE_REPO.valueAsString}"`, + `export IMAGE_TAG="${IMAGE_AMD_XLA_TAG.valueAsString}"`, + `export BASE_IMAGE_TAG="${BASE_IMAGE_AMD_XLA_TAG.valueAsString}"`, + `cd app`, + `chmod +x ./build.sh && ./build.sh` + ], + } + }, + artifacts: { + files: ['imageDetail.json'] + }, + } + ), + }); + + const assets_image_xla_amd_build = new codebuild.Project(this, `AssetsImageXlaAmdBuild`, { + environment: {privileged:true,buildImage: codebuild.LinuxBuildImage.AMAZON_LINUX_2_3}, + cache: codebuild.Cache.local(codebuild.LocalCacheMode.DOCKER_LAYER, codebuild.LocalCacheMode.CUSTOM), + role: buildRole, + buildSpec: codebuild.BuildSpec.fromObject( + { + version: "0.2", + env: { + 'exported-variables': [ + 'AWS_ACCOUNT_ID','AWS_REGION','BASE_REPO','IMAGE_AMD_XLA_TAG','BASE_IMAGE_AMD_XLA_TAG' + ], + }, + phases: { + build: { + commands: [ + `export AWS_ACCOUNT_ID="${this.account}"`, + `export AWS_REGION="${this.region}"`, + `export BASE_REPO="${BASE_REPO.valueAsString}"`, + `export IMAGE_TAG="${IMAGE_AMD_XLA_TAG.valueAsString}"`, + `export BASE_IMAGE_TAG="${BASE_IMAGE_AMD_XLA_TAG.valueAsString}"`, + `cd app`, + `chmod +x ./build-assets.sh && ./build-assets.sh` + ], + } + }, + artifacts: { + files: ['imageDetail.json'] + }, + } + ), + }); + + const assets_image_cpu_arm_build = new codebuild.Project(this, `AssetsImageCpuArmBuild`, { + environment: {privileged:true,buildImage: codebuild.LinuxBuildImage.AMAZON_LINUX_2_ARM_2}, + cache: codebuild.Cache.local(codebuild.LocalCacheMode.DOCKER_LAYER, codebuild.LocalCacheMode.CUSTOM), + role: buildRole, + buildSpec: codebuild.BuildSpec.fromObject( + { + version: "0.2", + env: { + 'exported-variables': [ + 'AWS_ACCOUNT_ID','AWS_REGION','BASE_REPO','IMAGE_ARM_CPU_TAG','BASE_IMAGE_ARM_CPU_TAG' + ], + }, + phases: { + build: { + commands: [ + `export AWS_ACCOUNT_ID="${this.account}"`, + `export AWS_REGION="${this.region}"`, + `export BASE_REPO="${BASE_REPO.valueAsString}"`, + `export IMAGE_TAG="${IMAGE_ARM_CPU_TAG.valueAsString}"`, + `export BASE_IMAGE_TAG="${BASE_IMAGE_ARM_CPU_TAG.valueAsString}"`, + `cd app`, + `chmod +x ./build-assets.sh && ./build-assets.sh` + ], + } + }, + artifacts: { + files: ['imageDetail.json'] + }, + } + ), + }); + + const assets_image_cud_amd_build = new codebuild.Project(this, `AssetsImageCudAmdBuild`, { + environment: {privileged:true,buildImage: codebuild.LinuxBuildImage.AMAZON_LINUX_2_3}, + cache: codebuild.Cache.local(codebuild.LocalCacheMode.DOCKER_LAYER, codebuild.LocalCacheMode.CUSTOM), + role: buildRole, + buildSpec: codebuild.BuildSpec.fromObject( + { + version: "0.2", + env: { + 'exported-variables': [ + 'AWS_ACCOUNT_ID','AWS_REGION','BASE_REPO','IMAGE_AMD_CUD_TAG','BASE_IMAGE_AMD_CUD_TAG' + ], + }, + phases: { + build: { + commands: [ + `export AWS_ACCOUNT_ID="${this.account}"`, + `export AWS_REGION="${this.region}"`, + `export BASE_REPO="${BASE_REPO.valueAsString}"`, + `export IMAGE_TAG="${IMAGE_AMD_CUD_TAG.valueAsString}"`, + `export BASE_IMAGE_TAG="${BASE_IMAGE_AMD_CUD_TAG.valueAsString}"`, + `cd app`, + `chmod +x ./build-assets.sh && ./build-assets.sh` + ], + } + }, + artifacts: { + files: ['imageDetail.json'] + }, + } + ), + }); + + //we allow the buildProject principal to push images to ecr + base_registry.grantPullPush(assets_image_cud_amd_build.grantPrincipal); + base_registry.grantPullPush(assets_image_xla_amd_build.grantPrincipal); + base_registry.grantPullPush(assets_image_cpu_arm_build.grantPrincipal); + base_registry.grantPullPush(base_image_amd_xla_build.grantPrincipal); + base_registry.grantPullPush(base_image_amd_cud_build.grantPrincipal); + base_registry.grantPullPush(base_image_arm_cpu_build.grantPrincipal); + + // here we define our pipeline and put together the assembly line + const sourceOutput = new codepipeline.Artifact(); + const basebuildpipeline = new codepipeline.Pipeline(this,`BuildBasePipeline`); + basebuildpipeline.addStage({ + stageName: 'Source', + actions: [ + new codepipeline_actions.GitHubSourceAction({ + actionName: 'GitHub_Source', + owner: GITHUB_USER.valueAsString, + repo: GITHUB_REPO.valueAsString, + branch: GITHUB_BRANCH.valueAsString, + output: sourceOutput, + oauthToken: SecretValue.secretsManager("githubtoken",{jsonField: "token"}), + trigger: codepipeline_actions.GitHubTrigger.WEBHOOK, + //oauthToken: SecretValue.unsafePlainText(GITHUB_OAUTH_TOKEN.valueAsString) + }) + ] + }); + + basebuildpipeline.addStage({ + stageName: 'ImageBuild', + actions: [ + new codepipeline_actions.CodeBuildAction({ + actionName: 'AssetsImageXlaAmdBuild', + input: sourceOutput, + runOrder: 1, + project: assets_image_xla_amd_build + }), + new codepipeline_actions.CodeBuildAction({ + actionName: 'AssetsImageCudAmdBuild', + input: sourceOutput, + runOrder: 1, + project: assets_image_cud_amd_build + }), + new codepipeline_actions.CodeBuildAction({ + actionName: 'AssetsImageCpuArmBuild', + input: sourceOutput, + runOrder: 1, + project: assets_image_cpu_arm_build + }), + new codepipeline_actions.CodeBuildAction({ + actionName: 'BaseImageAmdXlaBuild', + input: sourceOutput, + runOrder: 2, + project: base_image_amd_xla_build + }), + new codepipeline_actions.CodeBuildAction({ + actionName: 'BaseImageArmCpuBuild', + input: sourceOutput, + runOrder: 2, + project: base_image_arm_cpu_build + }), + new codepipeline_actions.CodeBuildAction({ + actionName: 'BaseImageAmdCudBuild', + input: sourceOutput, + runOrder: 2, + project: base_image_amd_cud_build + }) + ] + }); + } +} diff --git a/flux_serve/oci-image-build/pipeline.js b/flux_serve/oci-image-build/pipeline.js new file mode 100644 index 0000000..d8dc6e9 --- /dev/null +++ b/flux_serve/oci-image-build/pipeline.js @@ -0,0 +1,44 @@ +#!/usr/bin/env node +"use strict"; +var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) { + if (k2 === undefined) k2 = k; + var desc = Object.getOwnPropertyDescriptor(m, k); + if (!desc || ("get" in desc ? !m.__esModule : desc.writable || desc.configurable)) { + desc = { enumerable: true, get: function() { return m[k]; } }; + } + Object.defineProperty(o, k2, desc); +}) : (function(o, m, k, k2) { + if (k2 === undefined) k2 = k; + o[k2] = m[k]; +})); +var __setModuleDefault = (this && this.__setModuleDefault) || (Object.create ? (function(o, v) { + Object.defineProperty(o, "default", { enumerable: true, value: v }); +}) : function(o, v) { + o["default"] = v; +}); +var __importStar = (this && this.__importStar) || (function () { + var ownKeys = function(o) { + ownKeys = Object.getOwnPropertyNames || function (o) { + var ar = []; + for (var k in o) if (Object.prototype.hasOwnProperty.call(o, k)) ar[ar.length] = k; + return ar; + }; + return ownKeys(o); + }; + return function (mod) { + if (mod && mod.__esModule) return mod; + var result = {}; + if (mod != null) for (var k = ownKeys(mod), i = 0; i < k.length; i++) if (k[i] !== "default") __createBinding(result, mod, k[i]); + __setModuleDefault(result, mod); + return result; + }; +})(); +Object.defineProperty(exports, "__esModule", { value: true }); +require("source-map-support/register"); +const cdk = __importStar(require("aws-cdk-lib")); +const pipeline_stack_1 = require("./pipeline-stack"); +const app = new cdk.App(); +let stack = process.env.CF_STACK; +new pipeline_stack_1.PipelineStack(app, stack, { + env: { account: process.env.AWS_ACCOUNT_ID, region: process.env.AWS_REGION }, +}); diff --git a/flux_serve/oci-image-build/pipeline.ts b/flux_serve/oci-image-build/pipeline.ts new file mode 100644 index 0000000..f5f0bb6 --- /dev/null +++ b/flux_serve/oci-image-build/pipeline.ts @@ -0,0 +1,10 @@ +#!/usr/bin/env node +import 'source-map-support/register'; +import * as cdk from 'aws-cdk-lib'; +import { PipelineStack } from './pipeline-stack'; + +const app = new cdk.App(); +let stack = process.env.CF_STACK as string; +new PipelineStack(app,stack,{ + env: { account: process.env.AWS_ACCOUNT_ID, region: process.env.AWS_REGION}, +}); diff --git a/flux_serve/oci-image-build/tsconfig.json b/flux_serve/oci-image-build/tsconfig.json new file mode 100644 index 0000000..c9c555d --- /dev/null +++ b/flux_serve/oci-image-build/tsconfig.json @@ -0,0 +1,111 @@ +{ + "compilerOptions": { + /* Visit https://aka.ms/tsconfig to read more about this file */ + + /* Projects */ + // "incremental": true, /* Save .tsbuildinfo files to allow for incremental compilation of projects. */ + // "composite": true, /* Enable constraints that allow a TypeScript project to be used with project references. */ + // "tsBuildInfoFile": "./.tsbuildinfo", /* Specify the path to .tsbuildinfo incremental compilation file. */ + // "disableSourceOfProjectReferenceRedirect": true, /* Disable preferring source files instead of declaration files when referencing composite projects. */ + // "disableSolutionSearching": true, /* Opt a project out of multi-project reference checking when editing. */ + // "disableReferencedProjectLoad": true, /* Reduce the number of projects loaded automatically by TypeScript. */ + + /* Language and Environment */ + "target": "es2016", /* Set the JavaScript language version for emitted JavaScript and include compatible library declarations. */ + // "lib": [], /* Specify a set of bundled library declaration files that describe the target runtime environment. */ + // "jsx": "preserve", /* Specify what JSX code is generated. */ + // "experimentalDecorators": true, /* Enable experimental support for legacy experimental decorators. */ + // "emitDecoratorMetadata": true, /* Emit design-type metadata for decorated declarations in source files. */ + // "jsxFactory": "", /* Specify the JSX factory function used when targeting React JSX emit, e.g. 'React.createElement' or 'h'. */ + // "jsxFragmentFactory": "", /* Specify the JSX Fragment reference used for fragments when targeting React JSX emit e.g. 'React.Fragment' or 'Fragment'. */ + // "jsxImportSource": "", /* Specify module specifier used to import the JSX factory functions when using 'jsx: react-jsx*'. */ + // "reactNamespace": "", /* Specify the object invoked for 'createElement'. This only applies when targeting 'react' JSX emit. */ + // "noLib": true, /* Disable including any library files, including the default lib.d.ts. */ + // "useDefineForClassFields": true, /* Emit ECMAScript-standard-compliant class fields. */ + // "moduleDetection": "auto", /* Control what method is used to detect module-format JS files. */ + + /* Modules */ + "module": "commonjs", /* Specify what module code is generated. */ + // "rootDir": "./", /* Specify the root folder within your source files. */ + // "moduleResolution": "node10", /* Specify how TypeScript looks up a file from a given module specifier. */ + // "baseUrl": "./", /* Specify the base directory to resolve non-relative module names. */ + // "paths": {}, /* Specify a set of entries that re-map imports to additional lookup locations. */ + // "rootDirs": [], /* Allow multiple folders to be treated as one when resolving modules. */ + // "typeRoots": [], /* Specify multiple folders that act like './node_modules/@types'. */ + // "types": [], /* Specify type package names to be included without being referenced in a source file. */ + // "allowUmdGlobalAccess": true, /* Allow accessing UMD globals from modules. */ + // "moduleSuffixes": [], /* List of file name suffixes to search when resolving a module. */ + // "allowImportingTsExtensions": true, /* Allow imports to include TypeScript file extensions. Requires '--moduleResolution bundler' and either '--noEmit' or '--emitDeclarationOnly' to be set. */ + // "rewriteRelativeImportExtensions": true, /* Rewrite '.ts', '.tsx', '.mts', and '.cts' file extensions in relative import paths to their JavaScript equivalent in output files. */ + // "resolvePackageJsonExports": true, /* Use the package.json 'exports' field when resolving package imports. */ + // "resolvePackageJsonImports": true, /* Use the package.json 'imports' field when resolving imports. */ + // "customConditions": [], /* Conditions to set in addition to the resolver-specific defaults when resolving imports. */ + // "noUncheckedSideEffectImports": true, /* Check side effect imports. */ + // "resolveJsonModule": true, /* Enable importing .json files. */ + // "allowArbitraryExtensions": true, /* Enable importing files with any extension, provided a declaration file is present. */ + // "noResolve": true, /* Disallow 'import's, 'require's or ''s from expanding the number of files TypeScript should add to a project. */ + + /* JavaScript Support */ + // "allowJs": true, /* Allow JavaScript files to be a part of your program. Use the 'checkJS' option to get errors from these files. */ + // "checkJs": true, /* Enable error reporting in type-checked JavaScript files. */ + // "maxNodeModuleJsDepth": 1, /* Specify the maximum folder depth used for checking JavaScript files from 'node_modules'. Only applicable with 'allowJs'. */ + + /* Emit */ + // "declaration": true, /* Generate .d.ts files from TypeScript and JavaScript files in your project. */ + // "declarationMap": true, /* Create sourcemaps for d.ts files. */ + // "emitDeclarationOnly": true, /* Only output d.ts files and not JavaScript files. */ + // "sourceMap": true, /* Create source map files for emitted JavaScript files. */ + // "inlineSourceMap": true, /* Include sourcemap files inside the emitted JavaScript. */ + // "noEmit": true, /* Disable emitting files from a compilation. */ + // "outFile": "./", /* Specify a file that bundles all outputs into one JavaScript file. If 'declaration' is true, also designates a file that bundles all .d.ts output. */ + // "outDir": "./", /* Specify an output folder for all emitted files. */ + // "removeComments": true, /* Disable emitting comments. */ + // "importHelpers": true, /* Allow importing helper functions from tslib once per project, instead of including them per-file. */ + // "downlevelIteration": true, /* Emit more compliant, but verbose and less performant JavaScript for iteration. */ + // "sourceRoot": "", /* Specify the root path for debuggers to find the reference source code. */ + // "mapRoot": "", /* Specify the location where debugger should locate map files instead of generated locations. */ + // "inlineSources": true, /* Include source code in the sourcemaps inside the emitted JavaScript. */ + // "emitBOM": true, /* Emit a UTF-8 Byte Order Mark (BOM) in the beginning of output files. */ + // "newLine": "crlf", /* Set the newline character for emitting files. */ + // "stripInternal": true, /* Disable emitting declarations that have '@internal' in their JSDoc comments. */ + // "noEmitHelpers": true, /* Disable generating custom helper functions like '__extends' in compiled output. */ + // "noEmitOnError": true, /* Disable emitting files if any type checking errors are reported. */ + // "preserveConstEnums": true, /* Disable erasing 'const enum' declarations in generated code. */ + // "declarationDir": "./", /* Specify the output directory for generated declaration files. */ + + /* Interop Constraints */ + // "isolatedModules": true, /* Ensure that each file can be safely transpiled without relying on other imports. */ + // "verbatimModuleSyntax": true, /* Do not transform or elide any imports or exports not marked as type-only, ensuring they are written in the output file's format based on the 'module' setting. */ + // "isolatedDeclarations": true, /* Require sufficient annotation on exports so other tools can trivially generate declaration files. */ + // "allowSyntheticDefaultImports": true, /* Allow 'import x from y' when a module doesn't have a default export. */ + "esModuleInterop": true, /* Emit additional JavaScript to ease support for importing CommonJS modules. This enables 'allowSyntheticDefaultImports' for type compatibility. */ + // "preserveSymlinks": true, /* Disable resolving symlinks to their realpath. This correlates to the same flag in node. */ + "forceConsistentCasingInFileNames": true, /* Ensure that casing is correct in imports. */ + + /* Type Checking */ + "strict": true, /* Enable all strict type-checking options. */ + // "noImplicitAny": true, /* Enable error reporting for expressions and declarations with an implied 'any' type. */ + // "strictNullChecks": true, /* When type checking, take into account 'null' and 'undefined'. */ + // "strictFunctionTypes": true, /* When assigning functions, check to ensure parameters and the return values are subtype-compatible. */ + // "strictBindCallApply": true, /* Check that the arguments for 'bind', 'call', and 'apply' methods match the original function. */ + // "strictPropertyInitialization": true, /* Check for class properties that are declared but not set in the constructor. */ + // "strictBuiltinIteratorReturn": true, /* Built-in iterators are instantiated with a 'TReturn' type of 'undefined' instead of 'any'. */ + // "noImplicitThis": true, /* Enable error reporting when 'this' is given the type 'any'. */ + // "useUnknownInCatchVariables": true, /* Default catch clause variables as 'unknown' instead of 'any'. */ + // "alwaysStrict": true, /* Ensure 'use strict' is always emitted. */ + // "noUnusedLocals": true, /* Enable error reporting when local variables aren't read. */ + // "noUnusedParameters": true, /* Raise an error when a function parameter isn't read. */ + // "exactOptionalPropertyTypes": true, /* Interpret optional property types as written, rather than adding 'undefined'. */ + // "noImplicitReturns": true, /* Enable error reporting for codepaths that do not explicitly return in a function. */ + // "noFallthroughCasesInSwitch": true, /* Enable error reporting for fallthrough cases in switch statements. */ + // "noUncheckedIndexedAccess": true, /* Add 'undefined' to a type when accessed using an index. */ + // "noImplicitOverride": true, /* Ensure overriding members in derived classes are marked with an override modifier. */ + // "noPropertyAccessFromIndexSignature": true, /* Enforces using indexed accessors for keys declared using an indexed type. */ + // "allowUnusedLabels": true, /* Disable error reporting for unused labels. */ + // "allowUnreachableCode": true, /* Disable error reporting for unreachable code. */ + + /* Completeness */ + // "skipDefaultLibCheck": true, /* Skip type checking .d.ts files that are included with TypeScript. */ + "skipLibCheck": true /* Skip type checking all .d.ts files. */ + } +} diff --git a/flux_serve/src/README.md b/flux_serve/src/README.md new file mode 100644 index 0000000..7e7f6a6 --- /dev/null +++ b/flux_serve/src/README.md @@ -0,0 +1,11 @@ +# Compile Flux + +clean previous compilation +```bash +rm -rf decoder/compiler_workdir/ decoder/__pycache__/ text_encoder_1/compiler_workdir text_encoder_2/compiler_workdir/ text_encoder_2/compiler_workdir/ text_encoder_2/__pycache__/ transformer/compiler_workdir/ transformer/__pycache__/ transformer/compiled_model decoder/compiled_model text_encoder_1/compiled_model text_encoder_2/compiled_model/ text_encoder_1/__pycache__/ +``` +modify the shapes and:.... + +```bash +./compile.sh > compile.log 2>&1 & +``` diff --git a/flux_serve/src/compile.sh b/flux_serve/src/compile.sh new file mode 100755 index 0000000..40d4b2b --- /dev/null +++ b/flux_serve/src/compile.sh @@ -0,0 +1,10 @@ +#!/bin/bash +find . -type d \( -name "compiled_model" -o -name "__pycache__" -o -name "compiler_workdir" \) -exec rm -rf {} + + +myloc="$(dirname "$0")" + +python "$myloc/text_encoder_1/compile.py" +python "$myloc/text_encoder_2/compile.py" -m 32 +python "$myloc/transformer/compile.py" -hh 512 -w 512 -m 32 +python "$myloc/decoder/compile.py" -hh 512 -w 512 +echo "done!" diff --git a/flux_serve/src/decoder/compile.py b/flux_serve/src/decoder/compile.py new file mode 100644 index 0000000..51d7aaa --- /dev/null +++ b/flux_serve/src/decoder/compile.py @@ -0,0 +1,62 @@ +import argparse +import copy +import os +import torch +import torch_neuronx +from diffusers import FluxPipeline +from model import TracingVAEDecoderWrapper + +COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) +DTYPE=torch.bfloat16 + +def trace_vae(height, width): + pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=torch.float32) + decoder = copy.deepcopy(pipe.vae.decoder) + decoder = TracingVAEDecoderWrapper(decoder) + del pipe + + latents = torch.rand([1, 16, height // 8, width // 8], + dtype=DTYPE) + + decoder_neuron = torch_neuronx.trace( + decoder, + latents, + compiler_workdir=os.path.join(COMPILER_WORKDIR_ROOT, + 'compiler_workdir'), + compiler_args="""--model-type=unet-inference""" + ) + + torch_neuronx.async_load(decoder_neuron) + + compiled_model_path = os.path.join(COMPILER_WORKDIR_ROOT, 'compiled_model') + if not os.path.exists(compiled_model_path): + os.mkdir(compiled_model_path) + decoder_filename = os.path.join(COMPILER_WORKDIR_ROOT, + 'compiled_model/model.pt') + torch.jit.save(decoder_neuron, decoder_filename) + + del decoder + del decoder_neuron + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument( + "-hh", + "--height", + type=int, + default=1024, + help="height of images to be generated by compilation of this model" + ) + parser.add_argument( + "-w", + "--width", + type=int, + default=1024, + help="width of images to be generated by compilation of this model" + ) + args = parser.parse_args() + trace_vae(args.height, args.width) + diff --git a/flux_serve/src/decoder/model.py b/flux_serve/src/decoder/model.py new file mode 100644 index 0000000..43c24f6 --- /dev/null +++ b/flux_serve/src/decoder/model.py @@ -0,0 +1,19 @@ +import torch +import torch.nn as nn + +DTYPE=torch.bfloat16 + +class TracingVAEDecoderWrapper(nn.Module): + def __init__(self, decoder): + super().__init__() + self.decoder = decoder + + def forward( + self, + latents: torch.Tensor + ): + latents = latents.to(torch.float32) + return self.decoder( + latents + ) + diff --git a/flux_serve/src/inference.py b/flux_serve/src/inference.py new file mode 100644 index 0000000..57b4bc3 --- /dev/null +++ b/flux_serve/src/inference.py @@ -0,0 +1,232 @@ +import argparse +import torch +import torch.nn as nn +import torch_neuronx +import neuronx_distributed +import os + +from diffusers import FluxPipeline +from diffusers.models.modeling_outputs import Transformer2DModelOutput +from typing import Any, Dict, Optional, Union + +COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) + +TEXT_ENCODER_PATH = os.path.join( + COMPILER_WORKDIR_ROOT, + 'text_encoder_1/compiled_model/model.pt') +TEXT_ENCODER_2_PATH = os.path.join( + COMPILER_WORKDIR_ROOT, + 'text_encoder_2/compiled_model/model.pt') +VAE_DECODER_PATH = os.path.join( + COMPILER_WORKDIR_ROOT, + 'decoder/compiled_model/model.pt') + +TEXT_ENCODER_2_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'text_encoder_2/compiled_model/text_encoder_2') +EMBEDDERS_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'transformer/compiled_model/embedders') +OUT_LAYERS_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'transformer/compiled_model/out_layers') +SINGLE_TRANSFORMER_BLOCKS_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'transformer/compiled_model/single_transformer_blocks') +TRANSFORMER_BLOCKS_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'transformer/compiled_model/transformer_blocks') + +class CustomFluxPipeline(FluxPipeline): + @property + def _execution_device(self): + return torch.device("cpu") + +class TextEncoder2Wrapper(nn.Module): + def __init__(self, sharded_model,dtype=torch.bfloat16): + super().__init__() + self.sharded_model = sharded_model + self.dtype = dtype + + def forward(self, input_ids, output_hidden_states=False, **kwargs): + attention_mask = (input_ids != 0).long() + output = self.sharded_model(input_ids,attention_mask) + last_hidden_state = output[0] + processed_output = last_hidden_state + return (processed_output,) + +class NeuronFluxTransformer2DModel(nn.Module): + def __init__( + self, + config, + x_embedder, + context_embedder + ): + super().__init__() + with torch_neuronx.experimental.neuron_cores_context(start_nc=0, nc_count=8): + self.embedders_model = neuronx_distributed.trace.parallel_model_load(EMBEDDERS_DIR) + self.out_layers_model = neuronx_distributed.trace.parallel_model_load(OUT_LAYERS_DIR) + self.transformer_blocks_model = neuronx_distributed.trace.parallel_model_load(TRANSFORMER_BLOCKS_DIR) + self.single_transformer_blocks_model = neuronx_distributed.trace.parallel_model_load(SINGLE_TRANSFORMER_BLOCKS_DIR) + + self.config = config + self.x_embedder = x_embedder + self.context_embedder = context_embedder + self.device = torch.device("cpu") + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.Tensor = None, + pooled_projections: torch.Tensor = None, + timestep: torch.LongTensor = None, + img_ids: torch.Tensor = None, + txt_ids: torch.Tensor = None, + guidance: torch.Tensor = None, + joint_attention_kwargs: Optional[Dict[str, Any]] = None, + return_dict: bool = False, + ) -> Union[torch.FloatTensor, Transformer2DModelOutput]: + + hidden_states = self.x_embedder(hidden_states) + + hidden_states, temb, image_rotary_emb = self.embedders_model( + hidden_states, + timestep, + guidance, + pooled_projections, + txt_ids, + img_ids + ) + + encoder_hidden_states = self.context_embedder(encoder_hidden_states) + + image_rotary_emb = image_rotary_emb.type(torch.bfloat16) + + encoder_hidden_states, hidden_states = self.transformer_blocks_model( + hidden_states, + encoder_hidden_states, + temb, + image_rotary_emb + ) + + hidden_states = torch.cat([encoder_hidden_states, hidden_states], + dim=1) + + hidden_states = self.single_transformer_blocks_model( + hidden_states, + temb, + image_rotary_emb + ) + + hidden_states = hidden_states.to(torch.bfloat16) + + return self.out_layers_model( + hidden_states, + encoder_hidden_states, + temb + ) + + +class NeuronFluxCLIPTextEncoderModel(nn.Module): + def __init__(self, dtype, encoder): + super().__init__() + self.dtype = dtype + self.encoder = encoder + self.device = torch.device("cpu") + + def forward(self, emb, output_hidden_states): + output = self.encoder(emb) + output = CLIPEncoderOutput(output) + return output + + +class CLIPEncoderOutput(): + def __init__(self, dictionary): + self.pooler_output = dictionary["pooler_output"] + +def run_inference( + prompt, + height, + width, + max_sequence_length, + num_inference_steps): + + with torch_neuronx.experimental.neuron_cores_context(start_nc=8): + pipe = CustomFluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=torch.bfloat16) + + with torch_neuronx.experimental.neuron_cores_context(start_nc=8): + pipe.text_encoder = NeuronFluxCLIPTextEncoderModel( + pipe.text_encoder.dtype, + torch.jit.load(TEXT_ENCODER_PATH)) + + with torch_neuronx.experimental.neuron_cores_context(start_nc=8): + pipe.vae.decoder = torch.jit.load(VAE_DECODER_PATH) + + with torch_neuronx.experimental.neuron_cores_context(start_nc=0, nc_count=8): + sharded_text_encoder_2 = neuronx_distributed.trace.parallel_model_load(TEXT_ENCODER_2_DIR) + pipe.text_encoder_2 = TextEncoder2Wrapper(sharded_text_encoder_2) + + + pipe.transformer = NeuronFluxTransformer2DModel( + pipe.transformer.config, + pipe.transformer.x_embedder, + pipe.transformer.context_embedder) + + image = pipe( + prompt, + height=height, + width=width, + guidance_scale=3.5, + num_inference_steps=num_inference_steps, + max_sequence_length=max_sequence_length + ).images[0] + image.save(os.path.join(COMPILER_WORKDIR_ROOT, "flux-dev.png")) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument( + "-p", + "--prompt", + type=str, + default="A cat holding a sign that says hello world", + help="prompt for image to be generated; generates cat by default" + ) + parser.add_argument( + "-hh", + "--height", + type=int, + default=256, + help="height of images to be generated by compilation of this model" + ) + parser.add_argument( + "-w", + "--width", + type=int, + default=256, + help="width of images to be generated by compilation of this model" + ) + parser.add_argument( + "-m", + "--max_sequence_length", + type=int, + default=32, + help="maximum sequence length for the text embeddings" + ) + parser.add_argument( + "-n", + "--num_inference_steps", + type=int, + default=50, + help="number of inference steps to run in generating image" + ) + args = parser.parse_args() + run_inference( + args.prompt, + args.height, + args.width, + args.max_sequence_length, + args.num_inference_steps) + diff --git a/flux_serve/src/inference.sh b/flux_serve/src/inference.sh new file mode 100755 index 0000000..51c4f60 --- /dev/null +++ b/flux_serve/src/inference.sh @@ -0,0 +1,5 @@ +#!/bin/bash + +myloc="$(dirname "$0")" + +python "$myloc/inference.py" -p "A cat holding a sign that says hello world" -hh 512 -w 512 -m 32 -n 50 diff --git a/flux_serve/src/text_encoder_1/compile.py b/flux_serve/src/text_encoder_1/compile.py new file mode 100644 index 0000000..84065b3 --- /dev/null +++ b/flux_serve/src/text_encoder_1/compile.py @@ -0,0 +1,45 @@ +import copy +import os +import torch +import torch_neuronx +from diffusers import FluxPipeline +from model import TracingCLIPTextEncoderWrapper + +COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) +DTYPE=torch.bfloat16 + +def trace_text_encoder(): + pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=DTYPE) + text_encoder = copy.deepcopy(pipe.text_encoder) + del pipe + + text_encoder = TracingCLIPTextEncoderWrapper(text_encoder) + + emb = torch.zeros((1, 77), dtype=torch.int64) + + text_encoder_neuron = torch_neuronx.trace( + text_encoder.neuron_text_encoder, + emb, + compiler_workdir=os.path.join(COMPILER_WORKDIR_ROOT, + 'compiler_workdir'), + compiler_args=["--enable-fast-loading-neuron-binaries"] + ) + + torch_neuronx.async_load(text_encoder_neuron) + + compiled_model_path = os.path.join(COMPILER_WORKDIR_ROOT, 'compiled_model') + if not os.path.exists(compiled_model_path): + os.mkdir(compiled_model_path) + text_encoder_filename = os.path.join(COMPILER_WORKDIR_ROOT, + 'compiled_model/model.pt') + torch.jit.save(text_encoder_neuron, text_encoder_filename) + + del text_encoder + del text_encoder_neuron + + +if __name__ == '__main__': + trace_text_encoder() + diff --git a/flux_serve/src/text_encoder_1/model.py b/flux_serve/src/text_encoder_1/model.py new file mode 100644 index 0000000..c046b5c --- /dev/null +++ b/flux_serve/src/text_encoder_1/model.py @@ -0,0 +1,33 @@ +import torch +import torch.nn as nn +from transformers.modeling_outputs \ + import BaseModelOutputWithPooling +from typing import Optional, Union, Tuple + + +class TracingCLIPTextEncoderWrapper(nn.Module): + def __init__(self, text_encoder): + super().__init__() + self.neuron_text_encoder = text_encoder + self.config = text_encoder.config + self.dtype = text_encoder.dtype + self.device = text_encoder.device + + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True + ) -> Union[Tuple, BaseModelOutputWithPooling]: + + return self.neuron_text_encoder( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=True, + ) diff --git a/flux_serve/src/text_encoder_2/compile.py b/flux_serve/src/text_encoder_2/compile.py new file mode 100644 index 0000000..74025f1 --- /dev/null +++ b/flux_serve/src/text_encoder_2/compile.py @@ -0,0 +1,86 @@ +import argparse +import copy +import os +import torch +import torch_neuronx +import neuronx_distributed +from diffusers import FluxPipeline +from transformers import T5EncoderModel + +from model import ( + TracingT5TextEncoderWrapper, + init_text_encoder_2, +) + +COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) +TP_DEGREE=8 +DTYPE=torch.bfloat16 + +def build_text_encoder_2(): + """ + Build the T5 text encoder model, shard it, and wrap it in our tracing class. + Returns the final model + any (optional) kwargs in a dict, i.e. (model, {}). + This pattern is required by neuronx_distributed.trace.parallel_model_trace. + """ + # Load pipeline and copy the text_encoder_2 from your flux pipeline + pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=DTYPE + ) + # text_encoder_2 is presumably a T5EncoderModel or custom T5-based module + text_encoder_2 = copy.deepcopy(pipe.text_encoder_2) + del pipe + + # Optionally, if text_encoder_2 is not already a T5EncoderModel, you can + # modify the approach. But presumably, it's a T5EncoderModel or T5-like module. + # Shard the T5 model parameters across tp_degree ranks: + init_text_encoder_2(text_encoder_2) + + # Wrap it in a tracing module + wrapper = TracingT5TextEncoderWrapper(text_encoder_2) + + # Return the model plus optional kwargs (unused here) + return wrapper, {} + + +def trace_text_encoder_2(max_sequence_length=512): + """ + Trace the T5 text encoder with parallel model trace. + """ + input_ids = torch.zeros((1, max_sequence_length), dtype=torch.int64) + attention_mask = torch.ones((1, max_sequence_length), dtype=torch.int64) + + sample_inputs = (input_ids, attention_mask) + + model = neuronx_distributed.trace.parallel_model_trace( + build_text_encoder_2, + sample_inputs, + tp_degree=TP_DEGREE, + compiler_workdir=os.path.join(COMPILER_WORKDIR_ROOT, "compiler_workdir"), + compiler_args=["--enable-fast-loading-neuron-binaries"], + ) + + torch_neuronx.async_load(model) + + compiled_model_path = os.path.join(COMPILER_WORKDIR_ROOT, "compiled_model") + if not os.path.exists(compiled_model_path): + os.mkdir(compiled_model_path) + + model_filename = os.path.join(compiled_model_path, "text_encoder_2") + neuronx_distributed.trace.parallel_model_save(model, model_filename) + + del model + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "-m", + "--max_sequence_length", + type=int, + default=32, + help="maximum sequence length for the text embeddings" + ) + args = parser.parse_args() + trace_text_encoder_2(args.max_sequence_length) + diff --git a/flux_serve/src/text_encoder_2/model.py b/flux_serve/src/text_encoder_2/model.py new file mode 100644 index 0000000..3916ec5 --- /dev/null +++ b/flux_serve/src/text_encoder_2/model.py @@ -0,0 +1,215 @@ +import torch +import torch.nn as nn +from transformers.modeling_outputs import BaseModelOutput +from typing import Optional, Union, Tuple + +from neuronx_distributed.parallel_layers import parallel_state +from neuronx_distributed.parallel_layers.layers import ( + ColumnParallelLinear, + RowParallelLinear, +) + +DTYPE=torch.bfloat16 + +def get_sharded_data(data: torch.Tensor, dim: int) -> torch.Tensor: + """ + Utility function to slice a parameter across tensor-parallel ranks. + """ + tp_rank = parallel_state.get_tensor_model_parallel_rank() + tp_size = parallel_state.get_tensor_model_parallel_size() + per_partition_size = data.shape[dim] // tp_size + + if dim == 0: + return ( + data[per_partition_size * tp_rank : per_partition_size * (tp_rank + 1)] + .clone() + .to(DTYPE) + ) + elif dim == 1: + return ( + data[:, per_partition_size * tp_rank : per_partition_size * (tp_rank + 1)] + .clone() + .to(DTYPE) + ) + else: + raise ValueError("Partition dimension must be 0 or 1.") + +def shard_t5_attention(t5_attention): + """ + Shard the T5 self-attention q, k, v, o projections to ColumnParallel & RowParallel. + This is analogous to shard_attn() in transformer/model.py but adapted for T5. + """ + # Shard q + orig_q = t5_attention.q + t5_attention.q = ColumnParallelLinear( + orig_q.in_features, + orig_q.out_features, + bias=(orig_q.bias is not None), + gather_output=True + ) + t5_attention.q.weight.data = get_sharded_data(orig_q.weight.data, 0) + # T5 uses bias=False by default, but we handle it just in case + if orig_q.bias is not None: + t5_attention.q.bias.data = get_sharded_data(orig_q.bias.data, 0) + del orig_q + + # Shard k + orig_k = t5_attention.k + t5_attention.k = ColumnParallelLinear( + orig_k.in_features, + orig_k.out_features, + bias=(orig_k.bias is not None), + gather_output=True + ) + t5_attention.k.weight.data = get_sharded_data(orig_k.weight.data, 0) + if orig_k.bias is not None: + t5_attention.k.bias.data = get_sharded_data(orig_k.bias.data, 0) + del orig_k + + # Shard v + orig_v = t5_attention.v + t5_attention.v = ColumnParallelLinear( + orig_v.in_features, + orig_v.out_features, + bias=(orig_v.bias is not None), + gather_output=True + ) + t5_attention.v.weight.data = get_sharded_data(orig_v.weight.data, 0) + if orig_v.bias is not None: + t5_attention.v.bias.data = get_sharded_data(orig_v.bias.data, 0) + del orig_v + + # Shard o + orig_o = t5_attention.o + t5_attention.o = RowParallelLinear( + orig_o.in_features, + orig_o.out_features, + bias=(orig_o.bias is not None), + input_is_parallel=False + ) + t5_attention.o.weight.data = get_sharded_data(orig_o.weight.data, 1) + if orig_o.bias is not None: + t5_attention.o.bias.data = orig_o.bias.data.detach() + del orig_o + +def shard_t5_ff(ff_block): + """ + Shard the T5 feed-forward block (DenseReluDense or DenseGatedActDense). + Depending on the HF version, T5 may implement FF as: + - T5DenseReluDense, with attributes: ff_block.wi, ff_block.wo + - T5DenseGatedActDense, with attributes: ff_block.wi_0, ff_block.wi_1, ff_block.wo + We'll shard them similarly: + - ColumnParallel for the 'wi_*' inputs + - RowParallel for the 'wo' output + """ + + # Helper function for ColumnParallel + def make_column_parallel(orig_layer): + from neuronx_distributed.parallel_layers.layers import ColumnParallelLinear + new_layer = ColumnParallelLinear( + orig_layer.in_features, + orig_layer.out_features, + bias=(orig_layer.bias is not None), + gather_output=False + ) + new_layer.weight.data = get_sharded_data(orig_layer.weight.data, 0) + if orig_layer.bias is not None: + new_layer.bias.data = get_sharded_data(orig_layer.bias.data, 0) + return new_layer + + # Helper function for RowParallel + def make_row_parallel(orig_layer): + from neuronx_distributed.parallel_layers.layers import RowParallelLinear + new_layer = RowParallelLinear( + orig_layer.in_features, + orig_layer.out_features, + bias=(orig_layer.bias is not None), + input_is_parallel=True + ) + # For RowParallel, we shard dimension=1 + new_layer.weight.data = get_sharded_data(orig_layer.weight.data, 1) + if orig_layer.bias is not None: + new_layer.bias.data = orig_layer.bias.data.detach() + return new_layer + + # ------------------------------------------------------------- + # 1) T5DenseReluDense -> ff_block.wi, ff_block.wo + # ------------------------------------------------------------- + if hasattr(ff_block, "wi") and hasattr(ff_block, "wo"): + orig_wi = ff_block.wi + ff_block.wi = make_column_parallel(orig_wi) + del orig_wi + + orig_wo = ff_block.wo + ff_block.wo = make_row_parallel(orig_wo) + del orig_wo + + # ------------------------------------------------------------- + # 2) T5DenseGatedActDense -> ff_block.wi_0, ff_block.wi_1, ff_block.wo + # ------------------------------------------------------------- + elif hasattr(ff_block, "wi_0") and hasattr(ff_block, "wi_1") and hasattr(ff_block, "wo"): + orig_wi_0 = ff_block.wi_0 + ff_block.wi_0 = make_column_parallel(orig_wi_0) + del orig_wi_0 + + orig_wi_1 = ff_block.wi_1 + ff_block.wi_1 = make_column_parallel(orig_wi_1) + del orig_wi_1 + + orig_wo = ff_block.wo + ff_block.wo = make_row_parallel(orig_wo) + del orig_wo + + else: + raise ValueError( + f"Unsupported T5 FF block type: {type(ff_block).__name__}. " + f"Expected T5DenseReluDense or T5DenseGatedActDense." + ) + +def init_text_encoder_2(t5_encoder): + """ + Loop over T5Blocks in t5_encoder to shard the self-attention and FF modules. + Typically, T5EncoderModel has model.encoder.block[i].layer[0/1]. + """ + encoder_stack = t5_encoder.encoder # T5Stack + for block in encoder_stack.block: + # block.layer[0] => T5LayerSelfAttention + # block.layer[1] => T5LayerFF + attn = block.layer[0].SelfAttention + shard_t5_attention(attn) + ff = block.layer[1].DenseReluDense + shard_t5_ff(ff) + +class TracingT5TextEncoderWrapper(nn.Module): + """ + Wrap the T5 encoder in a forward that returns a stable tuple or BaseModelOutput, + suitable for compilation and serving on Neuron devices. + """ + def __init__(self, text_encoder): + super().__init__() + self.neuron_text_encoder = text_encoder + self.config = text_encoder.config + self.dtype = text_encoder.dtype + self.device = text_encoder.device + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = False, + ) -> Union[Tuple[torch.FloatTensor], BaseModelOutput]: + + return self.neuron_text_encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + head_mask=head_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + diff --git a/flux_serve/src/transformer/compile.py b/flux_serve/src/transformer/compile.py new file mode 100644 index 0000000..c33b21b --- /dev/null +++ b/flux_serve/src/transformer/compile.py @@ -0,0 +1,218 @@ +import argparse +import copy +import neuronx_distributed +import os +import torch +import torch_neuronx +from diffusers import FluxPipeline +from diffusers.models.transformers.transformer_flux \ + import FluxTransformer2DModel +from model import (TracingTransformerEmbedderWrapper, + TracingTransformerBlockWrapper, + TracingSingleTransformerBlockWrapper, + TracingTransformerOutLayerWrapper, + MyAttentionProcessor, + init_transformer) + +COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) +TP_DEGREE=8 +DTYPE=torch.bfloat16 + +def trace_transformer_embedders(): + pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=DTYPE) + transformer: FluxTransformer2DModel = copy.deepcopy(pipe.transformer) + del pipe + init_transformer(transformer) + + mod_pipe_transformer_f = TracingTransformerEmbedderWrapper( + transformer.x_embedder, transformer.context_embedder, + transformer.time_text_embed, transformer.pos_embed) + return mod_pipe_transformer_f, {} + + +def trace_transformer_blocks(): + pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=DTYPE) + for block in pipe.transformer.transformer_blocks: + block.attn.processor = MyAttentionProcessor() + + transformer: FluxTransformer2DModel = copy.deepcopy(pipe.transformer) + del pipe + init_transformer(transformer) + + mod_pipe_transformer_f = TracingTransformerBlockWrapper( + transformer, transformer.transformer_blocks) + return mod_pipe_transformer_f, {} + + +def trace_single_transformer_blocks(): + pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=DTYPE) + for block in pipe.transformer.single_transformer_blocks: + block.attn.processor = MyAttentionProcessor() + transformer: FluxTransformer2DModel = copy.deepcopy(pipe.transformer) + del pipe + init_transformer(transformer) + + mod_pipe_transformer_f = TracingSingleTransformerBlockWrapper( + transformer, transformer.single_transformer_blocks) + return mod_pipe_transformer_f, {} + + +def trace_transformer_out_layers(): + pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=DTYPE) + transformer: FluxTransformer2DModel = copy.deepcopy(pipe.transformer) + del pipe + init_transformer(transformer) + + mod_pipe_transformer_f = TracingTransformerOutLayerWrapper( + transformer.norm_out, transformer.proj_out) + return mod_pipe_transformer_f, {} + + +def trace_transformer(height, width, max_sequence_length): + hidden_states = torch.rand([1, height * width // 256, 3072], + dtype=DTYPE) + timestep = torch.rand([1], dtype=DTYPE) + guidance = torch.rand([1], dtype=torch.float32) + pooled_projections = torch.rand([1, 768], dtype=DTYPE) + txt_ids = torch.rand([1, max_sequence_length, 3], dtype=DTYPE) + img_ids = torch.rand([1, height * width // 256, 3], dtype=DTYPE) + sample_inputs = hidden_states, timestep, guidance, pooled_projections, \ + txt_ids, img_ids + + model = neuronx_distributed.trace.parallel_model_trace( + trace_transformer_embedders, + sample_inputs, + tp_degree=TP_DEGREE, + compiler_workdir=os.path.join(COMPILER_WORKDIR_ROOT, + 'compiler_workdir'), + compiler_args="""--model-type=unet-inference""" + ) + + torch_neuronx.async_load(model) + + compiled_model_path = os.path.join(COMPILER_WORKDIR_ROOT, 'compiled_model') + if not os.path.exists(compiled_model_path): + os.mkdir(compiled_model_path) + model_filename = os.path.join(COMPILER_WORKDIR_ROOT, + 'compiled_model/embedders') + neuronx_distributed.trace.parallel_model_save(model, model_filename) + + del model + + hidden_states = torch.rand([1, height * width // 256, 3072], + dtype=DTYPE) + encoder_hidden_states = torch.rand([1, max_sequence_length, 3072], + dtype=DTYPE) + temb = torch.rand([1, 3072], dtype=DTYPE) + image_rotary_emb = torch.rand( + [1, 1, height * width // 256 + max_sequence_length, 64, 2, 2], + dtype=DTYPE) + sample_inputs = hidden_states, encoder_hidden_states, \ + temb, image_rotary_emb + + model = neuronx_distributed.trace.parallel_model_trace( + trace_transformer_blocks, + sample_inputs, + tp_degree=TP_DEGREE, + compiler_workdir=os.path.join(COMPILER_WORKDIR_ROOT, + 'compiler_workdir'), + compiler_args="""--model-type=unet-inference""" + ) + + torch_neuronx.async_load(model) + + model_filename = os.path.join(COMPILER_WORKDIR_ROOT, + 'compiled_model/transformer_blocks') + neuronx_distributed.trace.parallel_model_save(model, model_filename) + + del model + + hidden_states = torch.rand( + [1, height * width // 256 + max_sequence_length, 3072], + dtype=DTYPE) + temb = torch.rand([1, 3072], dtype=DTYPE) + image_rotary_emb = torch.rand( + [1, 1, height * width // 256 + max_sequence_length, 64, 2, 2], + dtype=DTYPE) + sample_inputs = hidden_states, temb, image_rotary_emb + + model = neuronx_distributed.trace.parallel_model_trace( + trace_single_transformer_blocks, + sample_inputs, + tp_degree=TP_DEGREE, + compiler_workdir=os.path.join(COMPILER_WORKDIR_ROOT, + 'compiler_workdir'), + compiler_args="""--model-type=unet-inference""" + ) + + torch_neuronx.async_load(model) + + model_filename = os.path.join(COMPILER_WORKDIR_ROOT, + 'compiled_model/single_transformer_blocks') + neuronx_distributed.trace.parallel_model_save(model, model_filename) + + del model + + hidden_states = torch.rand( + [1, height * width // 256 + max_sequence_length, 3072], + dtype=DTYPE) + encoder_hidden_states = torch.rand([1, max_sequence_length, 3072], + dtype=DTYPE) + temb = torch.rand([1, 3072], dtype=DTYPE) + sample_inputs = hidden_states, encoder_hidden_states, temb + + model = neuronx_distributed.trace.parallel_model_trace( + trace_transformer_out_layers, + sample_inputs, + tp_degree=TP_DEGREE, + compiler_workdir=os.path.join(COMPILER_WORKDIR_ROOT, + 'compiler_workdir'), + compiler_args="""--model-type=unet-inference""" + ) + + torch_neuronx.async_load(model) + + model_filename = os.path.join(COMPILER_WORKDIR_ROOT, + 'compiled_model/out_layers') + neuronx_distributed.trace.parallel_model_save(model, model_filename) + + del model + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument( + "-hh", + "--height", + type=int, + default=256, + help="height of images to be generated by compilation of this model" + ) + parser.add_argument( + "-w", + "--width", + type=int, + default=256, + help="width of images to be generated by compilation of this model" + ) + parser.add_argument( + "-m", + "--max_sequence_length", + type=int, + default=32, + help="maximum sequence length for the text embeddings" + ) + args = parser.parse_args() + trace_transformer( + args.height, + args.width, + args.max_sequence_length) + diff --git a/flux_serve/src/transformer/model.py b/flux_serve/src/transformer/model.py new file mode 100644 index 0000000..f7ef7e4 --- /dev/null +++ b/flux_serve/src/transformer/model.py @@ -0,0 +1,475 @@ +import torch +import torch.nn as nn +from diffusers.models.attention import FeedForward +from diffusers.models.attention_processor import Attention +from diffusers.models.embeddings import TimestepEmbedding, \ + PixArtAlphaTextProjection +from neuronx_distributed.parallel_layers import parallel_state +from neuronx_distributed.parallel_layers.layers \ + import ColumnParallelLinear, RowParallelLinear + +from diffusers.models.attention_processor import AttnProcessor + +DTYPE=torch.bfloat16 + +class MyAttentionProcessor(AttnProcessor): + def __call__(self, attn, hidden_states,encoder_hidden_states=None,cross_attention_kwargs=None,**kwargs): + if hidden_states is not None: + # If norm_q has shape [128], we assume n_heads * 128 = hidden_states.shape[-1]. + n_heads = 24 # For example + head_dim = 128 + batch, seq_len, dim = hidden_states.shape + assert dim == n_heads * head_dim, f"Dim mismatch: got {dim}, expected {n_heads*head_dim}" + + # 1) [B, T, 3072] -> [B, T, n_heads, head_dim] = [B, T, 24, 128] + hidden_states = hidden_states.view(batch, seq_len, n_heads, head_dim) + # 2) Apply LN per-head + hidden_states = attn.norm_q(hidden_states) + # 3) Flatten back + hidden_states = hidden_states.view(batch, seq_len, dim) + return hidden_states, encoder_hidden_states + #return hidden_states + +class TracingTransformerEmbedderWrapper(nn.Module): + def __init__( + self, + x_embedder, + context_embedder, + time_text_embed, + pos_embed): + super().__init__() + self.x_embedder = x_embedder + self.context_embedder = context_embedder + self.time_text_embed = time_text_embed + self.pos_embed = pos_embed + + def forward( + self, + hidden_states, + timestep, + guidance, + pooled_projections, + txt_ids, + img_ids): + + timestep = timestep.to(hidden_states.dtype) * 1000 + if guidance is not None: + guidance = guidance.to(hidden_states.dtype) * 1000 + else: + guidance = None + temb = ( + self.time_text_embed(timestep, pooled_projections) + if guidance is None + else self.time_text_embed(timestep, guidance, pooled_projections) + ) + + ids = torch.cat((txt_ids, img_ids), dim=1) + image_rotary_emb = self.pos_embed(ids) + return hidden_states, temb, image_rotary_emb + + +class TracingTransformerBlockWrapper(nn.Module): + def __init__(self, transformer, transformerblock): + super().__init__() + self.transformerblock = transformerblock + self.config = transformer.config + self.dtype = transformer.dtype + self.device = transformer.device + + def forward( + self, + hidden_states, + encoder_hidden_states, + temb, + image_rotary_emb): + for block in self.transformerblock: + encoder_hidden_states, hidden_states = block( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + temb=temb, + image_rotary_emb=image_rotary_emb + ) + return encoder_hidden_states, hidden_states + + +class TracingSingleTransformerBlockWrapper(nn.Module): + def __init__(self, transformer, transformerblock): + super().__init__() + self.transformerblock = transformerblock + self.config = transformer.config + self.dtype = transformer.dtype + self.device = transformer.device + + def forward(self, hidden_states, temb, image_rotary_emb): + for block in self.transformerblock: + hidden_states = block( + hidden_states=hidden_states, + temb=temb, + image_rotary_emb=image_rotary_emb + ) + return hidden_states + + +class TracingTransformerOutLayerWrapper(nn.Module): + def __init__(self, norm_out, proj_out): + super().__init__() + self.norm_out = norm_out + self.proj_out = proj_out + + def forward(self, hidden_states, encoder_hidden_states, temb): + hidden_states = hidden_states[:, encoder_hidden_states.shape[1]:, ...] + + hidden_states = self.norm_out(hidden_states, temb) + return (self.proj_out(hidden_states),) + + +class TracingSingleTransformerBlock(nn.Module): + def __init__(self): + super().__init__() + self.mlp_hidden_dim = None + + self.norm = None + self.proj_mlp = None + self.act_mlp = None + self.proj_out = None + self.proj_out_2 = None + + self.attn = None + + def forward( + self, + hidden_states: torch.FloatTensor, + temb: torch.FloatTensor, + image_rotary_emb=None, + ): + residual = hidden_states + norm_hidden_states, gate = self.norm(hidden_states, emb=temb) + mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states)) + + #attn_output = self.attn(hidden_states=norm_hidden_states,image_rotary_emb=image_rotary_emb,) + attn_output, cross_output = self.attn(hidden_states=norm_hidden_states, image_rotary_emb=image_rotary_emb) + gate = gate.unsqueeze(1) + hidden_states = gate * (self.proj_out(attn_output) + + self.proj_out_2(mlp_hidden_states)) + hidden_states = residual + hidden_states + if hidden_states.dtype == torch.float16: + hidden_states = hidden_states.clip(-65504, 65504) + print("norm_hidden_states.shape =", norm_hidden_states.shape) + out = self.proj_mlp(norm_hidden_states) + print("out.shape =", out.shape) + print("self.proj_mlp.bias.shape =", self.proj_mlp.bias.shape) + return hidden_states + + +def get_sharded_data(data, dim): + tp_rank = parallel_state.get_tensor_model_parallel_rank() + per_partition_size = \ + data.shape[dim] // parallel_state.get_tensor_model_parallel_size() + if dim == 0: + return data[ + per_partition_size * tp_rank: per_partition_size * (tp_rank + 1) + ].clone().to(DTYPE) + elif dim == 1: + return data[:, + per_partition_size * tp_rank: per_partition_size * + (tp_rank + 1) + ].clone().to(DTYPE) + else: + raise Exception( + f"Partiton value of 0,1 are supported, found {dim}." + ) + + +def shard_attn(attn: Attention): + #attn.heads = 3 + print("shard_attn -> attn.to_q.in_features =", attn.to_q.in_features) + print("shard_attn -> attn.norm_q.weight.shape =", attn.norm_q.weight.shape) + + orig_q = attn.to_q + attn.to_q = ColumnParallelLinear( + attn.to_q.in_features, + attn.to_q.out_features, + bias=(attn.to_q.bias is not None), + gather_output=True) + attn.to_q.weight.data = get_sharded_data(orig_q.weight.data, 0) + if attn.to_q.bias is not None: + attn.to_q.bias.data = get_sharded_data(orig_q.bias.data, 0) + del (orig_q) + + orig_k = attn.to_k + attn.to_k = ColumnParallelLinear( + attn.to_k.in_features, + attn.to_k.out_features, + bias=(attn.to_k.bias is not None), + gather_output=True) + attn.to_k.weight.data = get_sharded_data(orig_k.weight.data, 0) + if attn.to_k.bias is not None: + attn.to_k.bias.data = get_sharded_data(orig_k.bias.data, 0) + del (orig_k) + + orig_v = attn.to_v + attn.to_v = ColumnParallelLinear( + attn.to_v.in_features, + attn.to_v.out_features, + bias=(attn.to_v.bias is not None), + gather_output=True) + attn.to_v.weight.data = get_sharded_data(orig_v.weight.data, 0) + if attn.to_v.bias is not None: + attn.to_v.bias.data = get_sharded_data(orig_v.bias.data, 0) + del (orig_v) + + orig_q_proj = attn.add_q_proj + attn.add_q_proj = ColumnParallelLinear( + attn.add_q_proj.in_features, + attn.add_q_proj.out_features, + bias=(attn.add_q_proj.bias is not None), + gather_output=True) + attn.add_q_proj.weight.data = get_sharded_data(orig_q_proj.weight.data, 0) + if attn.add_q_proj.bias is not None: + attn.add_q_proj.bias.data = get_sharded_data(orig_q_proj.bias.data, 0) + del (orig_q_proj) + + orig_k_proj = attn.add_k_proj + attn.add_k_proj = ColumnParallelLinear( + attn.add_k_proj.in_features, + attn.add_k_proj.out_features, + bias=(attn.add_k_proj.bias is not None), + gather_output=True) + attn.add_k_proj.weight.data = get_sharded_data(orig_k_proj.weight.data, 0) + if attn.add_k_proj.bias is not None: + attn.add_k_proj.bias.data = get_sharded_data(orig_k_proj.bias.data, 0) + del (orig_k_proj) + + orig_v_proj = attn.add_v_proj + attn.add_v_proj = ColumnParallelLinear( + attn.add_v_proj.in_features, + attn.add_v_proj.out_features, + bias=(attn.add_v_proj.bias is not None), + gather_output=True) + attn.add_v_proj.weight.data = get_sharded_data(orig_v_proj.weight.data, 0) + if attn.add_v_proj.bias is not None: + attn.add_v_proj.bias.data = get_sharded_data(orig_v_proj.bias.data, 0) + del (orig_v_proj) + + orig_out = attn.to_out[0] + attn.to_out[0] = RowParallelLinear( + attn.to_out[0].in_features, + attn.to_out[0].out_features, + bias=(attn.to_out[0].bias is not None), + input_is_parallel=False) + attn.to_out[0].weight.data = get_sharded_data(orig_out.weight.data, 1) + if attn.to_out[0].bias is not None: + attn.to_out[0].bias.data = orig_out.bias.data.detach() + del (orig_out) + + orig_out = attn.to_add_out + attn.to_add_out = RowParallelLinear( + attn.to_add_out.in_features, + attn.to_add_out.out_features, + bias=(attn.to_add_out.bias is not None), + input_is_parallel=False) + attn.to_add_out.weight.data = get_sharded_data(orig_out.weight.data, 1) + if attn.to_add_out.bias is not None: + attn.to_add_out.bias.data = orig_out.bias.data.detach() + del (orig_out) + + return attn + + +def shard_attn_lite(block): + attn = block.attn + #attn.heads = 3 + + orig_q = attn.to_q + attn.to_q = ColumnParallelLinear( + attn.to_q.in_features, + attn.to_q.out_features, + bias=(attn.to_q.bias is not None), + gather_output=True) + attn.to_q.weight.data = get_sharded_data(orig_q.weight.data, 0) + if attn.to_q.bias is not None: + attn.to_q.bias.data = get_sharded_data(orig_q.bias.data, 0) + del (orig_q) + + orig_k = attn.to_k + attn.to_k = ColumnParallelLinear( + attn.to_k.in_features, + attn.to_k.out_features, + bias=(attn.to_k.bias is not None), + gather_output=True) + attn.to_k.weight.data = get_sharded_data(orig_k.weight.data, 0) + if attn.to_k.bias is not None: + attn.to_k.bias.data = get_sharded_data(orig_k.bias.data, 0) + del (orig_k) + + orig_v = attn.to_v + attn.to_v = ColumnParallelLinear( + attn.to_v.in_features, + attn.to_v.out_features, + bias=(attn.to_v.bias is not None), + gather_output=True) + attn.to_v.weight.data = get_sharded_data(orig_v.weight.data, 0) + if attn.to_v.bias is not None: + attn.to_v.bias.data = get_sharded_data(orig_v.bias.data, 0) + del (orig_v) + + orig_mlp = block.proj_mlp + block.proj_mlp = ColumnParallelLinear( + block.proj_mlp.in_features, + block.proj_mlp.out_features, + bias=(block.proj_mlp.bias is not None), + gather_output=False) + #print("proj_mlp in_features:", block.proj_mlp.in_features) + #print("proj_mlp out_features:", block.proj_mlp.out_features) + print("proj_mlp.bias.shape:", block.proj_mlp.bias.shape) + block.proj_mlp.weight.data = get_sharded_data(orig_mlp.weight.data, 0) + if block.proj_mlp.bias is not None: + block.proj_mlp.bias.data = get_sharded_data(orig_mlp.bias.data, 0) + del (orig_mlp) + + orig_out = block.proj_out + out_features = block.proj_out.out_features + bias = block.proj_out.bias + block.proj_out = RowParallelLinear( + 3072, + out_features, + bias=(bias is not None), + input_is_parallel=True) + block.proj_out.weight.data = get_sharded_data( + orig_out.weight.data[..., 0:3072], 1) + if block.proj_out.bias is not None: + block.proj_out.bias.data = orig_out.bias.data.detach() + + block.proj_out_2 = RowParallelLinear( + 12288, + out_features, + bias=False, + input_is_parallel=True) + block.proj_out_2.weight.data = get_sharded_data( + orig_out.weight.data[..., 3072:15360], 1) + del (orig_out) + + return attn + + +def shard_ff(ff: FeedForward) -> FeedForward: + orig_proj = ff.net[0].proj + ff.net[0].proj = ColumnParallelLinear( + ff.net[0].proj.in_features, + ff.net[0].proj.out_features, + bias=(ff.net[0].proj.bias is not None), + gather_output=False) + ff.net[0].proj.weight.data = get_sharded_data(orig_proj.weight.data, 0) + if ff.net[0].proj.bias is not None: + ff.net[0].proj.bias.data = get_sharded_data(orig_proj.bias.data, 0) + del (orig_proj) + orig_linear = ff.net[2] + ff.net[2] = RowParallelLinear( + ff.net[2].in_features, + ff.net[2].out_features, + bias=(ff.net[2].bias is not None), + input_is_parallel=True) + if ff.net[2].bias is not None: + ff.net[2].bias.data = orig_linear.bias.data.detach() + ff.net[2].weight.data = get_sharded_data(orig_linear.weight.data, 1) + del (orig_linear) + return ff + + +def init_transformer(transformer): + timestep_embedder: TimestepEmbedding = \ + transformer.time_text_embed.timestep_embedder + orig_linear_1 = timestep_embedder.linear_1 + timestep_embedder.linear_1 = ColumnParallelLinear( + timestep_embedder.linear_1.in_features, + timestep_embedder.linear_1.out_features, + bias=(timestep_embedder.linear_1.bias is not None), + gather_output=False) + timestep_embedder.linear_1.weight.data = \ + get_sharded_data(orig_linear_1.weight.data, 0) + if timestep_embedder.linear_1.bias is not None: + timestep_embedder.linear_1.bias.data = \ + get_sharded_data(orig_linear_1.bias.data, 0) + del (orig_linear_1) + orig_linear_2 = timestep_embedder.linear_2 + timestep_embedder.linear_2 = RowParallelLinear( + timestep_embedder.linear_2.in_features, + timestep_embedder.linear_2.out_features, + bias=(timestep_embedder.linear_2.bias is not None), + input_is_parallel=True) + if timestep_embedder.linear_2.bias is not None: + timestep_embedder.linear_2.bias.data = orig_linear_2.bias.data.detach() + timestep_embedder.linear_2.weight.data = \ + get_sharded_data(orig_linear_2.weight.data, 1) + del (orig_linear_2) + + guidance_embedder: TimestepEmbedding = \ + transformer.time_text_embed.guidance_embedder + orig_linear_1 = guidance_embedder.linear_1 + guidance_embedder.linear_1 = ColumnParallelLinear( + guidance_embedder.linear_1.in_features, + guidance_embedder.linear_1.out_features, + bias=(guidance_embedder.linear_1.bias is not None), + gather_output=False) + guidance_embedder.linear_1.weight.data = \ + get_sharded_data(orig_linear_1.weight.data, 0) + if guidance_embedder.linear_1.bias is not None: + guidance_embedder.linear_1.bias.data = \ + get_sharded_data(orig_linear_1.bias.data, 0) + del (orig_linear_1) + orig_linear_2 = guidance_embedder.linear_2 + guidance_embedder.linear_2 = RowParallelLinear( + guidance_embedder.linear_2.in_features, + guidance_embedder.linear_2.out_features, + bias=(guidance_embedder.linear_2.bias is not None), + input_is_parallel=True) + if guidance_embedder.linear_2.bias is not None: + guidance_embedder.linear_2.bias.data = orig_linear_2.bias.data.detach() + guidance_embedder.linear_2.weight.data = \ + get_sharded_data(orig_linear_2.weight.data, 1) + del (orig_linear_2) + + text_embedder: PixArtAlphaTextProjection = \ + transformer.time_text_embed.text_embedder + orig_linear_1 = text_embedder.linear_1 + text_embedder.linear_1 = ColumnParallelLinear( + text_embedder.linear_1.in_features, + text_embedder.linear_1.out_features, + bias=(text_embedder.linear_1.bias is not None), + gather_output=False) + text_embedder.linear_1.weight.data = \ + get_sharded_data(orig_linear_1.weight.data, 0) + if text_embedder.linear_1.bias is not None: + text_embedder.linear_1.bias.data = \ + get_sharded_data(orig_linear_1.bias.data, 0) + del (orig_linear_1) + orig_linear_2 = text_embedder.linear_2 + text_embedder.linear_2 = RowParallelLinear( + text_embedder.linear_2.in_features, + text_embedder.linear_2.out_features, + bias=(text_embedder.linear_2.bias is not None), + input_is_parallel=True) + if text_embedder.linear_2.bias is not None: + text_embedder.linear_2.bias.data = orig_linear_2.bias.data.detach() + text_embedder.linear_2.weight.data = \ + get_sharded_data(orig_linear_2.weight.data, 1) + del (orig_linear_2) + + for block_idx, block in enumerate(transformer.transformer_blocks): + block.attn = shard_attn(block.attn) + block.ff = shard_ff(block.ff) + block.ff_context = shard_ff(block.ff_context) + + for block_idx, block in enumerate(transformer.single_transformer_blocks): + newblock = TracingSingleTransformerBlock() + newblock.mlp_hidden_dim = block.mlp_hidden_dim + newblock.norm = block.norm + newblock.proj_mlp = block.proj_mlp + newblock.act_mlp = block.act_mlp + newblock.proj_out = block.proj_out + newblock.proj_out_2 = None + newblock.attn = block.attn + transformer.single_transformer_blocks[block_idx] = newblock + block = newblock + block.attn = shard_attn_lite(block) + From c1d6e2a202d73f6f1ee3271fc87201d5019f0063 Mon Sep 17 00:00:00 2001 From: Yahav Date: Wed, 1 Jan 2025 14:32:55 -0800 Subject: [PATCH 02/45] gitignore --- flux_serve/app/.Dockerfile.template.swp | Bin 0 -> 12288 bytes flux_serve/oci-image-build/cdk.out/cdk.out | 1 - .../flux-oci-image-inference-cdk.assets.json | 20 - ...flux-oci-image-inference-cdk.template.json | 1960 ------------- .../oci-image-build/cdk.out/manifest.json | 357 --- flux_serve/oci-image-build/cdk.out/tree.json | 2599 ----------------- 6 files changed, 4937 deletions(-) create mode 100644 flux_serve/app/.Dockerfile.template.swp delete mode 100644 flux_serve/oci-image-build/cdk.out/cdk.out delete mode 100644 flux_serve/oci-image-build/cdk.out/flux-oci-image-inference-cdk.assets.json delete mode 100644 flux_serve/oci-image-build/cdk.out/flux-oci-image-inference-cdk.template.json delete mode 100644 flux_serve/oci-image-build/cdk.out/manifest.json delete mode 100644 flux_serve/oci-image-build/cdk.out/tree.json diff --git a/flux_serve/app/.Dockerfile.template.swp b/flux_serve/app/.Dockerfile.template.swp new file mode 100644 index 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"constructInfo": { - "fqn": "aws-cdk-lib.CfnResource", - "version": "2.173.4" - } - } - }, - "constructInfo": { - "fqn": "constructs.Construct", - "version": "10.4.2" - } - }, - "BootstrapVersion": { - "id": "BootstrapVersion", - "path": "flux-oci-image-inference-cdk/BootstrapVersion", - "constructInfo": { - "fqn": "aws-cdk-lib.CfnParameter", - "version": "2.173.4" - } - }, - "CheckBootstrapVersion": { - "id": "CheckBootstrapVersion", - "path": "flux-oci-image-inference-cdk/CheckBootstrapVersion", - "constructInfo": { - "fqn": "aws-cdk-lib.CfnRule", - "version": "2.173.4" - } - } - }, - "constructInfo": { - "fqn": "aws-cdk-lib.Stack", - "version": "2.173.4" - } - }, - "Tree": { - "id": "Tree", - "path": "Tree", - "constructInfo": { - "fqn": "constructs.Construct", - "version": "10.4.2" - } - } - }, - "constructInfo": { - "fqn": "aws-cdk-lib.App", - "version": "2.173.4" - } - } -} \ No newline at end of file From aa995bd484746a1a244feff723db3a6f001d6ab7 Mon Sep 17 00:00:00 2001 From: Yahav Date: Wed, 1 Jan 2025 23:35:02 -0800 Subject: [PATCH 03/45] awscliconfig --- flux_serve/app/config | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 flux_serve/app/config diff --git a/flux_serve/app/config b/flux_serve/app/config new file mode 100644 index 0000000..539a043 --- /dev/null +++ b/flux_serve/app/config @@ -0,0 +1,4 @@ +[default] +retry_mode = adaptive +max_attempts = 10 +region = us-west-2 From 0100e29695d0da9123819b82805dc4165b4a5b10 Mon Sep 17 00:00:00 2001 From: Yahav Date: Wed, 1 Jan 2025 23:39:14 -0800 Subject: [PATCH 04/45] gitignore --- flux_serve/.gitignore | 2 + flux_serve/app/.Dockerfile.template.swp | Bin 12288 -> 0 bytes flux_serve/oci-image-build/README.md | 2 +- flux_serve/oci-image-build/pipeline-stack.js | 148 +---------------- flux_serve/oci-image-build/pipeline-stack.ts | 161 +------------------ 5 files changed, 8 insertions(+), 305 deletions(-) delete mode 100644 flux_serve/app/.Dockerfile.template.swp diff --git a/flux_serve/.gitignore b/flux_serve/.gitignore index 4c9908f..a6cae42 100644 --- a/flux_serve/.gitignore +++ b/flux_serve/.gitignore @@ -1,3 +1,5 @@ package-lock.json .DS_Store node_modules/* +oci-image-build/cdk.out/ +oci-image-build/node_modules/ diff --git a/flux_serve/app/.Dockerfile.template.swp b/flux_serve/app/.Dockerfile.template.swp deleted file mode 100644 index 48e6538384097f669d2d60a8819350181d13da43..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 12288 zcmeI2!A=xG5QZDhq6YQgtzAt>Bs~KxppbAtMMDBYAeazBNY6~oY-eX@+U}lZHzFRr z_!u6HkKnJ>ht(jdAlq`BwtFwPP+g(#t{Z-9jvmH!4SzhIH-Xz0unXy;z zi{$Oqr!0QQSXVY=J192>k2_&ppN?QAuFp;nvOMfL^yR)485AbZ1T?L%vZYh7ew-Ga zJqxA{zSP=(kcUk$u}Z;nfELohAm_A41c<;n1V&hWYI349x>>uyuZ(V;V^7;efCvx) zB0vO)01+SpM1TnVO9Y%AVlVJO!~H{D>(4_!&Hb-55dk7V1c(3;AOb{y2oM1xKm>>Y z5g-D8ApsdNHhO`vLsa?y|IzRNua_A6g8GQsM$MzHqrP2a>=Wt@>HxKZnnlf^YN%n< zvk}HVqrVTR*Ql4M(*JW54I)4UhyW2F0z`la5CI}U1pZe7_t)09`EBmAjjhMLSp*Qe zl()fHm1kUtFjbJbYEx<_5^!8)*2y&GqI+b2YP--;c~7`>2Vo=UV-W;=$EqZQNCe#l zA6$sHi=E$uu2ETH{W7Y_z=0KIjQ+Zp=wz2SoYQvR_jNO|Uh}T?RPKklfq5U32vVHe zlit*vk3oFykMRTU0QhkXn-?Yo>!r#%FFFyaTB(u@3xj8g?dHlK1>1lWqy-PN#h{og z!iqqeR&Kb+e`N3*r>xRwH6w*j>NB1^3vacil|HUU399qUYNX=0zzf}WVPV3n9h^46O!O)QYykvSmAHm_^b= zyR`wY>b!^N52K(5k)%F=pTP`^xGplpsMAcwClp+M Date: Thu, 2 Jan 2025 10:49:00 -0800 Subject: [PATCH 05/45] compile --- .../specs/amd-neuron-trn1-nodepool.yaml | 49 +++++++++++++++++++ flux_serve/specs/compile-flux-1024x576.yaml | 46 +++++++++++++++++ flux_serve/specs/flux-model-s3-storage.yaml | 33 +++++++++++++ 3 files changed, 128 insertions(+) create mode 100644 flux_serve/specs/amd-neuron-trn1-nodepool.yaml create mode 100644 flux_serve/specs/compile-flux-1024x576.yaml create mode 100644 flux_serve/specs/flux-model-s3-storage.yaml diff --git a/flux_serve/specs/amd-neuron-trn1-nodepool.yaml b/flux_serve/specs/amd-neuron-trn1-nodepool.yaml new file mode 100644 index 0000000..245ee15 --- /dev/null +++ b/flux_serve/specs/amd-neuron-trn1-nodepool.yaml @@ -0,0 +1,49 @@ +apiVersion: karpenter.sh/v1 +kind: NodePool +metadata: + name: amd-neuron-trn1 +spec: + template: + spec: + requirements: + - key: kubernetes.io/arch + operator: In + values: ["amd64"] + - key: karpenter.k8s.aws/instance-family + operator: In + values: ["trn1"] + - key: karpenter.sh/capacity-type + operator: In + values: ["on-demand"] + nodeClassRef: + group: karpenter.k8s.aws + kind: EC2NodeClass + name: amd-neuron + expireAfter: 720h # 30 * 24h = 720h + limits: + cpu: 1000 + disruption: + consolidationPolicy: WhenEmptyOrUnderutilized + consolidateAfter: 1m +--- +apiVersion: karpenter.k8s.aws/v1 +kind: EC2NodeClass +metadata: + name: amd-neuron +spec: + amiFamily: AL2 # Amazon Linux 2 + amiSelectorTerms: + - name: "amazon-eks-node-1.31-*" + role: "KarpenterNodeRole-flux-usw2" + subnetSelectorTerms: + - tags: + karpenter.sh/discovery: "flux-usw2" + securityGroupSelectorTerms: + - tags: + karpenter.sh/discovery: "flux-usw2" + blockDeviceMappings: + - deviceName: /dev/xvda + ebs: + volumeSize: 900Gi + volumeType: gp3 + encrypted: true diff --git a/flux_serve/specs/compile-flux-1024x576.yaml b/flux_serve/specs/compile-flux-1024x576.yaml new file mode 100644 index 0000000..fef3eb6 --- /dev/null +++ b/flux_serve/specs/compile-flux-1024x576.yaml @@ -0,0 +1,46 @@ +apiVersion: batch/v1 +kind: Job +metadata: + name: compile-1024x576 +spec: + template: + spec: + restartPolicy: OnFailure + nodeSelector: + karpenter.sh/nodepool: amd-neuron-trn1 + #serviceAccountName: appsimulator + schedulerName: my-scheduler + containers: + - name: app + image: 133573815653.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + imagePullPolicy: Always + volumeMounts: + - name: dshm + mountPath: /dev/shm + - name: s3-flux-pvc + mountPath: /model + command: ["sh", "-c", "while true; do sleep 3600; done"] + resources: + limits: + aws.amazon.com/neuron: 6 + requests: + aws.amazon.com/neuron: 6 + env: + - name: HEIGHT + value: "1024" + - name: WIDTH + value: "576" + - name: MODEL_ID + value: "black-forest-labs/FLUX.1-dev" + - name: HUGGINGFACE_TOKEN + valueFrom: + secretKeyRef: + name: hf-secrets + key: HUGGINGFACE_TOKEN + volumes: + - name: dshm + emptyDir: + medium: Memory + - name: s3-flux-pvc + persistentVolumeClaim: + claimName: s3-flux-pvc diff --git a/flux_serve/specs/flux-model-s3-storage.yaml b/flux_serve/specs/flux-model-s3-storage.yaml new file mode 100644 index 0000000..a40e583 --- /dev/null +++ b/flux_serve/specs/flux-model-s3-storage.yaml @@ -0,0 +1,33 @@ +apiVersion: v1 +kind: PersistentVolume +metadata: + name: s3-flux-pv +spec: + capacity: + storage: 1200Gi + accessModes: + - ReadWriteMany + storageClassName: "" + claimRef: + namespace: default + name: s3-flux-pvc + mountOptions: + - region us-west-2 + csi: + driver: s3.csi.aws.com + volumeHandle: s3-csi-driver-volume + volumeAttributes: + bucketName: black-forest-labs-flux1-dev-neuron +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: s3-flux-pvc +spec: + accessModes: + - ReadWriteMany # Supported options: ReadWriteMany / ReadOnlyMany + storageClassName: "" # Required for static provisioning + resources: + requests: + storage: 1200Gi + volumeName: s3-flux-pv From 79ee1f089e263a23ee5b1b9196ec0ddc450bd004 Mon Sep 17 00:00:00 2001 From: Yahav Date: Thu, 2 Jan 2025 11:36:32 -0800 Subject: [PATCH 06/45] compile1 --- flux_serve/README.md | 40 ++++++++++++++++++- .../specs/amd-neuron-trn1-nodepool.yaml | 6 ++- flux_serve/specs/compile-flux-1024x576.yaml | 2 + flux_serve/src/compile.sh | 11 ++--- flux_serve/src/decoder/compile.py | 3 +- flux_serve/src/inference.py | 3 +- flux_serve/src/text_encoder_1/compile.py | 3 +- flux_serve/src/text_encoder_2/compile.py | 3 +- flux_serve/src/transformer/compile.py | 3 +- 9 files changed, 61 insertions(+), 13 deletions(-) diff --git a/flux_serve/README.md b/flux_serve/README.md index b9c4ba3..fc72b67 100644 --- a/flux_serve/README.md +++ b/flux_serve/README.md @@ -18,6 +18,44 @@ Neuron device plugin, Neuron scheduler extension, Neuron scheduler ```bash export MODEL_GRAPH_BUCKET="black-forest-labs-flux1-dev-neuron" ``` +* [Deploy your HggingFace user secret as a k8s secret](https://kubernetes.io/docs/concepts/configuration/secret/) +```bash +echo -n 'hf_myhftoken' | base64 +``` +replace the value with the `HUGGINGFACE_TOKEN` and apply the secret into the cluster +```yaml +apiVersion: v1 +kind: Secret +type: Opaque +metadata: + name: hf-secrets + namespace: default +data: + HUGGINGFACE_TOKEN: encodedhfmyhftoken +``` + * [Enable Amazon S3 objects with Mountpoint for Amazon S3 CSI driver](https://docs.aws.amazon.com/eks/latest/userguide/s3-csi.html) * [Deploy the OCI image pipeline](./oci-image-build) -* +* [Deploy the Neuron device plugin and scheduler extention](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/containers/kubernetes-getting-started.html#deploy-neuron-device-plugin) +```bash +helm upgrade --install neuron-helm-chart oci://public.ecr.aws/neuron/neuron-helm-chart \ + --set "npd.enabled=false" +helm upgrade --install neuron-helm-chart oci://public.ecr.aws/neuron/neuron-helm-chart \ + --set "scheduler.enabled=true" \ + --set "npd.enabled=false" +``` +* Deploy the Karpenter `NodeClass` and `NodePool` that provisions `trn1` instances upon requests (`nodeSelector:karpenter.sh/nodepool: amd-neuron-trn1`) +```bash +kubectl -f specs/amd-neuron-trn1-nodepool.yaml +``` +* Deploy the S3 CSI driver storage PersistentVolume and PersistentVolumeClaim that stores the compiled Flux graphs. +Edit `bucketName` for the right bucket name created; `accessModes` set to `ReadWriteMany` because we demonstrate graph compilation (upload) and serving (download). +Note the `PersistentVolumeClaim` name; we will need it for the app deployment. +```bash +kubectl apply -f specs/flux-model-s3-storage.yaml +``` +* Compile the model for the requires shapes. We will demonstrate three shapes: 1024x576, 256x144, and 512x512 with `bfloat16` +```bash +kubectl -f specs/compile-flux-1024x576.yaml +``` + diff --git a/flux_serve/specs/amd-neuron-trn1-nodepool.yaml b/flux_serve/specs/amd-neuron-trn1-nodepool.yaml index 245ee15..2e7a23d 100644 --- a/flux_serve/specs/amd-neuron-trn1-nodepool.yaml +++ b/flux_serve/specs/amd-neuron-trn1-nodepool.yaml @@ -24,7 +24,7 @@ spec: cpu: 1000 disruption: consolidationPolicy: WhenEmptyOrUnderutilized - consolidateAfter: 1m + consolidateAfter: 10m --- apiVersion: karpenter.k8s.aws/v1 kind: EC2NodeClass @@ -33,7 +33,9 @@ metadata: spec: amiFamily: AL2 # Amazon Linux 2 amiSelectorTerms: - - name: "amazon-eks-node-1.31-*" + - id: "ami-0e73cfd34ee52b6a6" + - id: "ami-077b8064c2a1a3186" + - id: "ami-057dddc807f3f6644" role: "KarpenterNodeRole-flux-usw2" subnetSelectorTerms: - tags: diff --git a/flux_serve/specs/compile-flux-1024x576.yaml b/flux_serve/specs/compile-flux-1024x576.yaml index fef3eb6..568a14f 100644 --- a/flux_serve/specs/compile-flux-1024x576.yaml +++ b/flux_serve/specs/compile-flux-1024x576.yaml @@ -26,6 +26,8 @@ spec: requests: aws.amazon.com/neuron: 6 env: + - name: COMPILER_WORKDIR_ROOT + value: "/model" - name: HEIGHT value: "1024" - name: WIDTH diff --git a/flux_serve/src/compile.sh b/flux_serve/src/compile.sh index 40d4b2b..4e0d120 100755 --- a/flux_serve/src/compile.sh +++ b/flux_serve/src/compile.sh @@ -1,10 +1,11 @@ #!/bin/bash -find . -type d \( -name "compiled_model" -o -name "__pycache__" -o -name "compiler_workdir" \) -exec rm -rf {} + -myloc="$(dirname "$0")" +find $COMPILER_WORKDIR_ROOT -type d \( -name "compiled_model" -o -name "__pycache__" -o -name "compiler_workdir" \) -exec rm -rf {} + +myloc=$COMPILER_WORKDIR_ROOT python "$myloc/text_encoder_1/compile.py" python "$myloc/text_encoder_2/compile.py" -m 32 -python "$myloc/transformer/compile.py" -hh 512 -w 512 -m 32 -python "$myloc/decoder/compile.py" -hh 512 -w 512 -echo "done!" +python "$myloc/transformer/compile.py" -hh $HEIGHT -w $WIDTH -m 32 +python "$myloc/decoder/compile.py" -hh $HEIGHT -w $WIDTH +echo "done compiling; cleaning compiler_workdir" +find $COMPILER_WORKDIR_ROOT -type d \( -name "__pycache__" -o -name "compiler_workdir" \) -exec rm -rf {} + diff --git a/flux_serve/src/decoder/compile.py b/flux_serve/src/decoder/compile.py index 51d7aaa..2e7954b 100644 --- a/flux_serve/src/decoder/compile.py +++ b/flux_serve/src/decoder/compile.py @@ -6,7 +6,8 @@ from diffusers import FluxPipeline from model import TracingVAEDecoderWrapper -COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) +#COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) +COMPILER_WORKDIR_ROOT = os.environ['COMPILER_WORKDIR_ROOT'] DTYPE=torch.bfloat16 def trace_vae(height, width): diff --git a/flux_serve/src/inference.py b/flux_serve/src/inference.py index 57b4bc3..3df5755 100644 --- a/flux_serve/src/inference.py +++ b/flux_serve/src/inference.py @@ -9,7 +9,8 @@ from diffusers.models.modeling_outputs import Transformer2DModelOutput from typing import Any, Dict, Optional, Union -COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) +#COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) +COMPILER_WORKDIR_ROOT = os.environ['COMPILER_WORKDIR_ROOT'] TEXT_ENCODER_PATH = os.path.join( COMPILER_WORKDIR_ROOT, diff --git a/flux_serve/src/text_encoder_1/compile.py b/flux_serve/src/text_encoder_1/compile.py index 84065b3..1984a23 100644 --- a/flux_serve/src/text_encoder_1/compile.py +++ b/flux_serve/src/text_encoder_1/compile.py @@ -5,7 +5,8 @@ from diffusers import FluxPipeline from model import TracingCLIPTextEncoderWrapper -COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) +#COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) +COMPILER_WORKDIR_ROOT = os.environ['COMPILER_WORKDIR_ROOT'] DTYPE=torch.bfloat16 def trace_text_encoder(): diff --git a/flux_serve/src/text_encoder_2/compile.py b/flux_serve/src/text_encoder_2/compile.py index 74025f1..cbbb220 100644 --- a/flux_serve/src/text_encoder_2/compile.py +++ b/flux_serve/src/text_encoder_2/compile.py @@ -12,7 +12,8 @@ init_text_encoder_2, ) -COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) +#COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) +COMPILER_WORKDIR_ROOT = os.environ['COMPILER_WORKDIR_ROOT'] TP_DEGREE=8 DTYPE=torch.bfloat16 diff --git a/flux_serve/src/transformer/compile.py b/flux_serve/src/transformer/compile.py index c33b21b..abc0284 100644 --- a/flux_serve/src/transformer/compile.py +++ b/flux_serve/src/transformer/compile.py @@ -14,7 +14,8 @@ MyAttentionProcessor, init_transformer) -COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) +#COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) +COMPILER_WORKDIR_ROOT = os.environ['COMPILER_WORKDIR_ROOT'] TP_DEGREE=8 DTYPE=torch.bfloat16 From fc6dc5b979acb2980becaa4afba49ecf839112f6 Mon Sep 17 00:00:00 2001 From: Yahav Date: Thu, 2 Jan 2025 14:09:35 -0800 Subject: [PATCH 07/45] compile --- flux_serve/README.md | 9 ++------- flux_serve/app/Dockerfile.template | 2 +- flux_serve/{ => app}/src/README.md | 0 flux_serve/{ => app}/src/compile.sh | 0 flux_serve/{ => app}/src/decoder/compile.py | 0 flux_serve/{ => app}/src/decoder/model.py | 0 flux_serve/{ => app}/src/inference.py | 0 flux_serve/{ => app}/src/inference.sh | 0 flux_serve/{ => app}/src/text_encoder_1/compile.py | 0 flux_serve/{ => app}/src/text_encoder_1/model.py | 0 flux_serve/{ => app}/src/text_encoder_2/compile.py | 0 flux_serve/{ => app}/src/text_encoder_2/model.py | 0 flux_serve/{ => app}/src/transformer/compile.py | 0 flux_serve/{ => app}/src/transformer/model.py | 0 flux_serve/oci-image-build/README.md | 5 ----- flux_serve/oci-image-build/package-lock.json | 6 +++--- flux_serve/oci-image-build/pipeline-stack.js | 14 +++++++------- flux_serve/oci-image-build/pipeline-stack.ts | 8 ++++---- flux_serve/specs/compile-flux-1024x576.yaml | 6 +++--- flux_serve/specs/flux-model-s3-storage.yaml | 2 +- 20 files changed, 21 insertions(+), 31 deletions(-) rename flux_serve/{ => app}/src/README.md (100%) rename flux_serve/{ => app}/src/compile.sh (100%) rename flux_serve/{ => app}/src/decoder/compile.py (100%) rename flux_serve/{ => app}/src/decoder/model.py (100%) rename flux_serve/{ => app}/src/inference.py (100%) rename flux_serve/{ => app}/src/inference.sh (100%) rename flux_serve/{ => app}/src/text_encoder_1/compile.py (100%) rename flux_serve/{ => app}/src/text_encoder_1/model.py (100%) rename flux_serve/{ => app}/src/text_encoder_2/compile.py (100%) rename flux_serve/{ => app}/src/text_encoder_2/model.py (100%) rename flux_serve/{ => app}/src/transformer/compile.py (100%) rename flux_serve/{ => app}/src/transformer/model.py (100%) diff --git a/flux_serve/README.md b/flux_serve/README.md index fc72b67..20b373d 100644 --- a/flux_serve/README.md +++ b/flux_serve/README.md @@ -14,10 +14,6 @@ Neuron device plugin, Neuron scheduler extension, Neuron scheduler ## Walkthrough * [Create cluster with Karpenter node pools that provisions `trn1` instances](https://karpenter.sh/docs/getting-started/getting-started-with-karpenter/) -* Create an S3 bucket to store the model compiled graph and export the S3 bucket name as follow: -```bash -export MODEL_GRAPH_BUCKET="black-forest-labs-flux1-dev-neuron" -``` * [Deploy your HggingFace user secret as a k8s secret](https://kubernetes.io/docs/concepts/configuration/secret/) ```bash echo -n 'hf_myhftoken' | base64 @@ -33,8 +29,7 @@ metadata: data: HUGGINGFACE_TOKEN: encodedhfmyhftoken ``` - -* [Enable Amazon S3 objects with Mountpoint for Amazon S3 CSI driver](https://docs.aws.amazon.com/eks/latest/userguide/s3-csi.html) +* Create an S3 bucket to store the model compiled graph and [enable Amazon S3 objects with Mountpoint for Amazon S3 CSI driver](https://docs.aws.amazon.com/eks/latest/userguide/s3-csi.html) * [Deploy the OCI image pipeline](./oci-image-build) * [Deploy the Neuron device plugin and scheduler extention](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/containers/kubernetes-getting-started.html#deploy-neuron-device-plugin) ```bash @@ -56,6 +51,6 @@ kubectl apply -f specs/flux-model-s3-storage.yaml ``` * Compile the model for the requires shapes. We will demonstrate three shapes: 1024x576, 256x144, and 512x512 with `bfloat16` ```bash -kubectl -f specs/compile-flux-1024x576.yaml +kubectl apply -f specs/compile-flux-1024x576.yaml ``` diff --git a/flux_serve/app/Dockerfile.template b/flux_serve/app/Dockerfile.template index 3a22297..2d79b7e 100644 --- a/flux_serve/app/Dockerfile.template +++ b/flux_serve/app/Dockerfile.template @@ -18,4 +18,4 @@ RUN echo 'deb [signed-by=/etc/apt/keyrings/kubernetes-apt-keyring.gpg] https://p RUN apt-get update RUN apt-get install -y kubectl RUN kubectl version --client -COPY * / +COPY . . diff --git a/flux_serve/src/README.md b/flux_serve/app/src/README.md similarity index 100% rename from flux_serve/src/README.md rename to flux_serve/app/src/README.md diff --git a/flux_serve/src/compile.sh b/flux_serve/app/src/compile.sh similarity index 100% rename from flux_serve/src/compile.sh rename to flux_serve/app/src/compile.sh diff --git a/flux_serve/src/decoder/compile.py b/flux_serve/app/src/decoder/compile.py similarity index 100% rename from flux_serve/src/decoder/compile.py rename to flux_serve/app/src/decoder/compile.py diff --git a/flux_serve/src/decoder/model.py b/flux_serve/app/src/decoder/model.py similarity index 100% rename from flux_serve/src/decoder/model.py rename to flux_serve/app/src/decoder/model.py diff --git a/flux_serve/src/inference.py b/flux_serve/app/src/inference.py similarity index 100% rename from flux_serve/src/inference.py rename to flux_serve/app/src/inference.py diff --git a/flux_serve/src/inference.sh b/flux_serve/app/src/inference.sh similarity index 100% rename from flux_serve/src/inference.sh rename to flux_serve/app/src/inference.sh diff --git a/flux_serve/src/text_encoder_1/compile.py b/flux_serve/app/src/text_encoder_1/compile.py similarity index 100% rename from flux_serve/src/text_encoder_1/compile.py rename to flux_serve/app/src/text_encoder_1/compile.py diff --git a/flux_serve/src/text_encoder_1/model.py b/flux_serve/app/src/text_encoder_1/model.py similarity index 100% rename from flux_serve/src/text_encoder_1/model.py rename to flux_serve/app/src/text_encoder_1/model.py diff --git a/flux_serve/src/text_encoder_2/compile.py b/flux_serve/app/src/text_encoder_2/compile.py similarity index 100% rename from flux_serve/src/text_encoder_2/compile.py rename to flux_serve/app/src/text_encoder_2/compile.py diff --git a/flux_serve/src/text_encoder_2/model.py b/flux_serve/app/src/text_encoder_2/model.py similarity index 100% rename from flux_serve/src/text_encoder_2/model.py rename to flux_serve/app/src/text_encoder_2/model.py diff --git a/flux_serve/src/transformer/compile.py b/flux_serve/app/src/transformer/compile.py similarity index 100% rename from flux_serve/src/transformer/compile.py rename to flux_serve/app/src/transformer/compile.py diff --git a/flux_serve/src/transformer/model.py b/flux_serve/app/src/transformer/model.py similarity index 100% rename from flux_serve/src/transformer/model.py rename to flux_serve/app/src/transformer/model.py diff --git a/flux_serve/oci-image-build/README.md b/flux_serve/oci-image-build/README.md index 635bed0..4052d04 100644 --- a/flux_serve/oci-image-build/README.md +++ b/flux_serve/oci-image-build/README.md @@ -6,18 +6,13 @@ export AWS_ACCOUNT_ID=$(aws sts get-caller-identity --output text --query Account) export AWS_REGION=us-west-2 export BASE_IMAGE_AMD_XLA_TAG=2.5.1-neuronx-py310-sdk2.21.0-ubuntu22.04 -export BASE_IMAGE_AMD_CUD_TAG=2.3.0-gpu-py311-cu121-ubuntu20.04-ec2 -export BASE_IMAGE_ARM_CPU_TAG=2.3.0-cpu-py311-ubuntu20.04-ec2 export IMAGE_AMD_XLA_TAG=amd64-neuron -export IMAGE_AMD_CUD_TAG=amd64-cuda -export IMAGE_ARM_CPU_TAG=aarch64-cpu export BASE_REPO=model export BASE_TAG=multiarch-ubuntu export BASE_AMD_TAG=amd64 export GITHUB_BRANCH=master export GITHUB_USER=yahavb export GITHUB_REPO=aws-neuron-eks-samples -export MODEL_DIR=sd2_compile_dir export CF_STACK=flux-oci-image-inference-cdk ``` * Install needed packages diff --git a/flux_serve/oci-image-build/package-lock.json b/flux_serve/oci-image-build/package-lock.json index 813ddc3..825107e 100644 --- a/flux_serve/oci-image-build/package-lock.json +++ b/flux_serve/oci-image-build/package-lock.json @@ -125,9 +125,9 @@ "license": "MIT" }, "node_modules/@types/node": { - "version": "22.10.3", - "resolved": "https://registry.npmjs.org/@types/node/-/node-22.10.3.tgz", - "integrity": "sha512-DifAyw4BkrufCILvD3ucnuN8eydUfc/C1GlyrnI+LK6543w5/L3VeVgf05o3B4fqSXP1dKYLOZsKfutpxPzZrw==", + "version": "22.10.4", + "resolved": "https://registry.npmjs.org/@types/node/-/node-22.10.4.tgz", + "integrity": "sha512-99l6wv4HEzBQhvaU/UGoeBoCK61SCROQaCCGyQSgX2tEQ3rKkNZ2S7CEWnS/4s1LV+8ODdK21UeyR1fHP2mXug==", "license": "MIT", "peer": true, "dependencies": { diff --git a/flux_serve/oci-image-build/pipeline-stack.js b/flux_serve/oci-image-build/pipeline-stack.js index 7622aaf..f886219 100644 --- a/flux_serve/oci-image-build/pipeline-stack.js +++ b/flux_serve/oci-image-build/pipeline-stack.js @@ -56,15 +56,15 @@ class PipelineStack extends aws_cdk_lib_1.Stack { const GITHUB_USER = new aws_cdk_lib_1.CfnParameter(this, "GITHUBUSER", { type: "String" }); const GITHUB_REPO = new aws_cdk_lib_1.CfnParameter(this, "GITHUBREPO", { type: "String" }); const GITHUB_BRANCH = new aws_cdk_lib_1.CfnParameter(this, "GITHUBBRANCH", { type: "String" }); - // uncomment when you test the stack and dont want to manually delete the ecr registry - const base_registry = new ecr.Repository(this, `base_repo`, { - repositoryName: BASE_REPO.valueAsString, - imageScanOnPush: true - }); - //const base_registry = ecr.Repository.fromRepositoryName(this,`base_repo`,BASE_REPO.valueAsString) + /* uncomment when you test the stack and dont want to manually delete the ecr registry + const base_registry = new ecr.Repository(this,`base_repo`,{ + repositoryName:BASE_REPO.valueAsString, + imageScanOnPush: true + });*/ + const base_registry = ecr.Repository.fromRepositoryName(this, `base_repo`, BASE_REPO.valueAsString); //create a roleARN for codebuild const buildRole = new iam.Role(this, 'BaseCodeBuildDeployRole', { - roleName: 'hwagnisticBaseCodeBuildDeployRole', + roleName: 'fluxneuronBaseCodeBuildDeployRole', assumedBy: new iam.ServicePrincipal('codebuild.amazonaws.com'), }); buildRole.addToPolicy(new iam.PolicyStatement({ diff --git a/flux_serve/oci-image-build/pipeline-stack.ts b/flux_serve/oci-image-build/pipeline-stack.ts index cea5897..c6a4d6b 100644 --- a/flux_serve/oci-image-build/pipeline-stack.ts +++ b/flux_serve/oci-image-build/pipeline-stack.ts @@ -24,17 +24,17 @@ export class PipelineStack extends Stack { const GITHUB_USER = new CfnParameter(this,"GITHUBUSER",{type:"String"}); const GITHUB_REPO = new CfnParameter(this,"GITHUBREPO",{type:"String"}); const GITHUB_BRANCH = new CfnParameter(this,"GITHUBBRANCH",{type:"String"}); - // uncomment when you test the stack and dont want to manually delete the ecr registry + /* uncomment when you test the stack and dont want to manually delete the ecr registry const base_registry = new ecr.Repository(this,`base_repo`,{ repositoryName:BASE_REPO.valueAsString, imageScanOnPush: true - }); + });*/ - //const base_registry = ecr.Repository.fromRepositoryName(this,`base_repo`,BASE_REPO.valueAsString) + const base_registry = ecr.Repository.fromRepositoryName(this,`base_repo`,BASE_REPO.valueAsString) //create a roleARN for codebuild const buildRole = new iam.Role(this, 'BaseCodeBuildDeployRole',{ - roleName: 'hwagnisticBaseCodeBuildDeployRole', + roleName: 'fluxneuronBaseCodeBuildDeployRole', assumedBy: new iam.ServicePrincipal('codebuild.amazonaws.com'), }); diff --git a/flux_serve/specs/compile-flux-1024x576.yaml b/flux_serve/specs/compile-flux-1024x576.yaml index 568a14f..1b82533 100644 --- a/flux_serve/specs/compile-flux-1024x576.yaml +++ b/flux_serve/specs/compile-flux-1024x576.yaml @@ -12,7 +12,7 @@ spec: schedulerName: my-scheduler containers: - name: app - image: 133573815653.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron imagePullPolicy: Always volumeMounts: - name: dshm @@ -22,9 +22,9 @@ spec: command: ["sh", "-c", "while true; do sleep 3600; done"] resources: limits: - aws.amazon.com/neuron: 6 + aws.amazon.com/neuron: 8 requests: - aws.amazon.com/neuron: 6 + aws.amazon.com/neuron: 8 env: - name: COMPILER_WORKDIR_ROOT value: "/model" diff --git a/flux_serve/specs/flux-model-s3-storage.yaml b/flux_serve/specs/flux-model-s3-storage.yaml index a40e583..cf59de1 100644 --- a/flux_serve/specs/flux-model-s3-storage.yaml +++ b/flux_serve/specs/flux-model-s3-storage.yaml @@ -17,7 +17,7 @@ spec: driver: s3.csi.aws.com volumeHandle: s3-csi-driver-volume volumeAttributes: - bucketName: black-forest-labs-flux1-dev-neuron + bucketName: flux1-dev-neuron --- apiVersion: v1 kind: PersistentVolumeClaim From c4f3b8eaf6057f757ebfcf45da7cfd9844770a70 Mon Sep 17 00:00:00 2001 From: Yahav Date: Thu, 2 Jan 2025 17:24:53 -0800 Subject: [PATCH 08/45] compile --- flux_serve/app/src/compile.sh | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/flux_serve/app/src/compile.sh b/flux_serve/app/src/compile.sh index 4e0d120..8ac3208 100755 --- a/flux_serve/app/src/compile.sh +++ b/flux_serve/app/src/compile.sh @@ -1,11 +1,10 @@ #!/bin/bash -find $COMPILER_WORKDIR_ROOT -type d \( -name "compiled_model" -o -name "__pycache__" -o -name "compiler_workdir" \) -exec rm -rf {} + +find . -type d \( -name "compiled_model" -o -name "__pycache__" -o -name "compiler_workdir" \) -exec rm -rf {} + -myloc=$COMPILER_WORKDIR_ROOT -python "$myloc/text_encoder_1/compile.py" -python "$myloc/text_encoder_2/compile.py" -m 32 -python "$myloc/transformer/compile.py" -hh $HEIGHT -w $WIDTH -m 32 -python "$myloc/decoder/compile.py" -hh $HEIGHT -w $WIDTH +python "./text_encoder_1/compile.py" +python "./text_encoder_2/compile.py" -m 32 +python "./transformer/compile.py" -hh $HEIGHT -w $WIDTH -m 32 +python "./decoder/compile.py" -hh $HEIGHT -w $WIDTH echo "done compiling; cleaning compiler_workdir" -find $COMPILER_WORKDIR_ROOT -type d \( -name "__pycache__" -o -name "compiler_workdir" \) -exec rm -rf {} + +find . -type d \( -name "__pycache__" -o -name "compiler_workdir" \) -exec rm -rf {} + From 6e559b657eb09bdf02674d613df59cad51e3fcfc Mon Sep 17 00:00:00 2001 From: Yahav Date: Thu, 2 Jan 2025 21:10:51 -0800 Subject: [PATCH 09/45] compile --- flux_serve/app/src/compile.sh | 2 +- flux_serve/app/src/decoder/compile.py | 3 +-- flux_serve/app/src/text_encoder_1/compile.py | 3 +-- flux_serve/app/src/text_encoder_2/compile.py | 3 +-- flux_serve/app/src/transformer/compile.py | 3 +-- 5 files changed, 5 insertions(+), 9 deletions(-) diff --git a/flux_serve/app/src/compile.sh b/flux_serve/app/src/compile.sh index 8ac3208..863cc9e 100755 --- a/flux_serve/app/src/compile.sh +++ b/flux_serve/app/src/compile.sh @@ -1,4 +1,4 @@ -#!/bin/bash +#!/bin/bash -x find . -type d \( -name "compiled_model" -o -name "__pycache__" -o -name "compiler_workdir" \) -exec rm -rf {} + diff --git a/flux_serve/app/src/decoder/compile.py b/flux_serve/app/src/decoder/compile.py index 2e7954b..51d7aaa 100644 --- a/flux_serve/app/src/decoder/compile.py +++ b/flux_serve/app/src/decoder/compile.py @@ -6,8 +6,7 @@ from diffusers import FluxPipeline from model import TracingVAEDecoderWrapper -#COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) -COMPILER_WORKDIR_ROOT = os.environ['COMPILER_WORKDIR_ROOT'] +COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) DTYPE=torch.bfloat16 def trace_vae(height, width): diff --git a/flux_serve/app/src/text_encoder_1/compile.py b/flux_serve/app/src/text_encoder_1/compile.py index 1984a23..84065b3 100644 --- a/flux_serve/app/src/text_encoder_1/compile.py +++ b/flux_serve/app/src/text_encoder_1/compile.py @@ -5,8 +5,7 @@ from diffusers import FluxPipeline from model import TracingCLIPTextEncoderWrapper -#COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) -COMPILER_WORKDIR_ROOT = os.environ['COMPILER_WORKDIR_ROOT'] +COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) DTYPE=torch.bfloat16 def trace_text_encoder(): diff --git a/flux_serve/app/src/text_encoder_2/compile.py b/flux_serve/app/src/text_encoder_2/compile.py index cbbb220..74025f1 100644 --- a/flux_serve/app/src/text_encoder_2/compile.py +++ b/flux_serve/app/src/text_encoder_2/compile.py @@ -12,8 +12,7 @@ init_text_encoder_2, ) -#COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) -COMPILER_WORKDIR_ROOT = os.environ['COMPILER_WORKDIR_ROOT'] +COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) TP_DEGREE=8 DTYPE=torch.bfloat16 diff --git a/flux_serve/app/src/transformer/compile.py b/flux_serve/app/src/transformer/compile.py index abc0284..c33b21b 100644 --- a/flux_serve/app/src/transformer/compile.py +++ b/flux_serve/app/src/transformer/compile.py @@ -14,8 +14,7 @@ MyAttentionProcessor, init_transformer) -#COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) -COMPILER_WORKDIR_ROOT = os.environ['COMPILER_WORKDIR_ROOT'] +COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) TP_DEGREE=8 DTYPE=torch.bfloat16 From 753d89182e30427ea9bfa22d6e63688a16e7e985 Mon Sep 17 00:00:00 2001 From: Yahav Date: Thu, 2 Jan 2025 21:42:35 -0800 Subject: [PATCH 10/45] compile --- flux_serve/app/src/decoder/compile.py | 3 +++ flux_serve/app/src/text_encoder_1/compile.py | 3 +++ flux_serve/app/src/text_encoder_2/compile.py | 3 +++ flux_serve/app/src/transformer/compile.py | 4 ++++ 4 files changed, 13 insertions(+) diff --git a/flux_serve/app/src/decoder/compile.py b/flux_serve/app/src/decoder/compile.py index 51d7aaa..69daf52 100644 --- a/flux_serve/app/src/decoder/compile.py +++ b/flux_serve/app/src/decoder/compile.py @@ -5,6 +5,9 @@ import torch_neuronx from diffusers import FluxPipeline from model import TracingVAEDecoderWrapper +from huggingface_hub import login +hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() +login(hf_token,add_to_git_credential=True) COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) DTYPE=torch.bfloat16 diff --git a/flux_serve/app/src/text_encoder_1/compile.py b/flux_serve/app/src/text_encoder_1/compile.py index 84065b3..b8f7856 100644 --- a/flux_serve/app/src/text_encoder_1/compile.py +++ b/flux_serve/app/src/text_encoder_1/compile.py @@ -4,6 +4,9 @@ import torch_neuronx from diffusers import FluxPipeline from model import TracingCLIPTextEncoderWrapper +from huggingface_hub import login +hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() +login(hf_token,add_to_git_credential=True) COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) DTYPE=torch.bfloat16 diff --git a/flux_serve/app/src/text_encoder_2/compile.py b/flux_serve/app/src/text_encoder_2/compile.py index 74025f1..e5b579d 100644 --- a/flux_serve/app/src/text_encoder_2/compile.py +++ b/flux_serve/app/src/text_encoder_2/compile.py @@ -11,6 +11,9 @@ TracingT5TextEncoderWrapper, init_text_encoder_2, ) +from huggingface_hub import login +hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() +login(hf_token,add_to_git_credential=True) COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) TP_DEGREE=8 diff --git a/flux_serve/app/src/transformer/compile.py b/flux_serve/app/src/transformer/compile.py index c33b21b..9a73465 100644 --- a/flux_serve/app/src/transformer/compile.py +++ b/flux_serve/app/src/transformer/compile.py @@ -14,6 +14,10 @@ MyAttentionProcessor, init_transformer) +from huggingface_hub import login +hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() +login(hf_token,add_to_git_credential=True) + COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) TP_DEGREE=8 DTYPE=torch.bfloat16 From 1460663d219f86a22fb01862d463245a75f63c21 Mon Sep 17 00:00:00 2001 From: Yahav Date: Thu, 2 Jan 2025 22:46:49 -0800 Subject: [PATCH 11/45] compile2 --- flux_serve/app/src/decoder/compile.py | 6 +++++- flux_serve/app/src/text_encoder_1/compile.py | 6 +++++- flux_serve/app/src/text_encoder_2/compile.py | 6 +++++- flux_serve/app/src/transformer/compile.py | 6 +++++- 4 files changed, 20 insertions(+), 4 deletions(-) diff --git a/flux_serve/app/src/decoder/compile.py b/flux_serve/app/src/decoder/compile.py index 69daf52..d8ef5c4 100644 --- a/flux_serve/app/src/decoder/compile.py +++ b/flux_serve/app/src/decoder/compile.py @@ -7,7 +7,11 @@ from model import TracingVAEDecoderWrapper from huggingface_hub import login hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() -login(hf_token,add_to_git_credential=True) +try: + user_info = whoami() + print(f"Already logged in as {user_info['name']}") +except: + login(hf_token,add_to_git_credential=True) COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) DTYPE=torch.bfloat16 diff --git a/flux_serve/app/src/text_encoder_1/compile.py b/flux_serve/app/src/text_encoder_1/compile.py index b8f7856..ef9a3f6 100644 --- a/flux_serve/app/src/text_encoder_1/compile.py +++ b/flux_serve/app/src/text_encoder_1/compile.py @@ -6,7 +6,11 @@ from model import TracingCLIPTextEncoderWrapper from huggingface_hub import login hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() -login(hf_token,add_to_git_credential=True) +try: + user_info = whoami() + print(f"Already logged in as {user_info['name']}") +except: + login(hf_token,add_to_git_credential=True) COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) DTYPE=torch.bfloat16 diff --git a/flux_serve/app/src/text_encoder_2/compile.py b/flux_serve/app/src/text_encoder_2/compile.py index e5b579d..48aae10 100644 --- a/flux_serve/app/src/text_encoder_2/compile.py +++ b/flux_serve/app/src/text_encoder_2/compile.py @@ -13,7 +13,11 @@ ) from huggingface_hub import login hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() -login(hf_token,add_to_git_credential=True) +try: + user_info = whoami() + print(f"Already logged in as {user_info['name']}") +except: + login(hf_token,add_to_git_credential=True) COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) TP_DEGREE=8 diff --git a/flux_serve/app/src/transformer/compile.py b/flux_serve/app/src/transformer/compile.py index 9a73465..a22d012 100644 --- a/flux_serve/app/src/transformer/compile.py +++ b/flux_serve/app/src/transformer/compile.py @@ -16,7 +16,11 @@ from huggingface_hub import login hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() -login(hf_token,add_to_git_credential=True) +try: + user_info = whoami() + print(f"Already logged in as {user_info['name']}") +except: + login(hf_token,add_to_git_credential=True) COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) TP_DEGREE=8 From 2122a8f0d5ee717e50454647a4216705e8c75d5e Mon Sep 17 00:00:00 2001 From: Yahav Date: Thu, 2 Jan 2025 23:17:58 -0800 Subject: [PATCH 12/45] compile10 --- flux_serve/app/src/decoder/compile.py | 1 + flux_serve/app/src/inference.py | 8 ++++++++ flux_serve/app/src/text_encoder_1/compile.py | 1 + flux_serve/app/src/text_encoder_2/compile.py | 1 + flux_serve/app/src/transformer/compile.py | 1 + 5 files changed, 12 insertions(+) diff --git a/flux_serve/app/src/decoder/compile.py b/flux_serve/app/src/decoder/compile.py index d8ef5c4..3d5cd7c 100644 --- a/flux_serve/app/src/decoder/compile.py +++ b/flux_serve/app/src/decoder/compile.py @@ -6,6 +6,7 @@ from diffusers import FluxPipeline from model import TracingVAEDecoderWrapper from huggingface_hub import login +from huggingface_hub import whoami hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() try: user_info = whoami() diff --git a/flux_serve/app/src/inference.py b/flux_serve/app/src/inference.py index 3df5755..8a23989 100644 --- a/flux_serve/app/src/inference.py +++ b/flux_serve/app/src/inference.py @@ -8,6 +8,14 @@ from diffusers import FluxPipeline from diffusers.models.modeling_outputs import Transformer2DModelOutput from typing import Any, Dict, Optional, Union +from huggingface_hub import login +from huggingface_hub import whoami +hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() +try: + user_info = whoami() + print(f"Already logged in as {user_info['name']}") +except: + login(hf_token,add_to_git_credential=True) #COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) COMPILER_WORKDIR_ROOT = os.environ['COMPILER_WORKDIR_ROOT'] diff --git a/flux_serve/app/src/text_encoder_1/compile.py b/flux_serve/app/src/text_encoder_1/compile.py index ef9a3f6..ccb732a 100644 --- a/flux_serve/app/src/text_encoder_1/compile.py +++ b/flux_serve/app/src/text_encoder_1/compile.py @@ -5,6 +5,7 @@ from diffusers import FluxPipeline from model import TracingCLIPTextEncoderWrapper from huggingface_hub import login +from huggingface_hub import whoami hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() try: user_info = whoami() diff --git a/flux_serve/app/src/text_encoder_2/compile.py b/flux_serve/app/src/text_encoder_2/compile.py index 48aae10..9d2fa10 100644 --- a/flux_serve/app/src/text_encoder_2/compile.py +++ b/flux_serve/app/src/text_encoder_2/compile.py @@ -12,6 +12,7 @@ init_text_encoder_2, ) from huggingface_hub import login +from huggingface_hub import whoami hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() try: user_info = whoami() diff --git a/flux_serve/app/src/transformer/compile.py b/flux_serve/app/src/transformer/compile.py index a22d012..69db269 100644 --- a/flux_serve/app/src/transformer/compile.py +++ b/flux_serve/app/src/transformer/compile.py @@ -15,6 +15,7 @@ init_transformer) from huggingface_hub import login +from huggingface_hub import whoami hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() try: user_info = whoami() From b0c03ba2c1bdccd5ede8ecc4654d33d1431ea68a Mon Sep 17 00:00:00 2001 From: Yahav Date: Fri, 3 Jan 2025 10:11:50 -0800 Subject: [PATCH 13/45] compile11 --- flux_serve/app/src/inference.sh | 4 +--- flux_serve/app/src/transformer/model.py | 15 +-------------- flux_serve/specs/compile-flux-1024x576.yaml | 13 ++++++++++--- flux_serve/specs/flux-model-s3-storage.yaml | 2 +- 4 files changed, 13 insertions(+), 21 deletions(-) diff --git a/flux_serve/app/src/inference.sh b/flux_serve/app/src/inference.sh index 51c4f60..4c3e7db 100755 --- a/flux_serve/app/src/inference.sh +++ b/flux_serve/app/src/inference.sh @@ -1,5 +1,3 @@ #!/bin/bash -myloc="$(dirname "$0")" - -python "$myloc/inference.py" -p "A cat holding a sign that says hello world" -hh 512 -w 512 -m 32 -n 50 +python "./inference.py" -p "A cat holding a sign that says hello world" -hh $HEIGHT -w $WIDTH -m 32 -n 50 diff --git a/flux_serve/app/src/transformer/model.py b/flux_serve/app/src/transformer/model.py index f7ef7e4..ce9f306 100644 --- a/flux_serve/app/src/transformer/model.py +++ b/flux_serve/app/src/transformer/model.py @@ -15,20 +15,15 @@ class MyAttentionProcessor(AttnProcessor): def __call__(self, attn, hidden_states,encoder_hidden_states=None,cross_attention_kwargs=None,**kwargs): if hidden_states is not None: - # If norm_q has shape [128], we assume n_heads * 128 = hidden_states.shape[-1]. - n_heads = 24 # For example + n_heads = 24 head_dim = 128 batch, seq_len, dim = hidden_states.shape assert dim == n_heads * head_dim, f"Dim mismatch: got {dim}, expected {n_heads*head_dim}" - # 1) [B, T, 3072] -> [B, T, n_heads, head_dim] = [B, T, 24, 128] hidden_states = hidden_states.view(batch, seq_len, n_heads, head_dim) - # 2) Apply LN per-head hidden_states = attn.norm_q(hidden_states) - # 3) Flatten back hidden_states = hidden_states.view(batch, seq_len, dim) return hidden_states, encoder_hidden_states - #return hidden_states class TracingTransformerEmbedderWrapper(nn.Module): def __init__( @@ -154,10 +149,7 @@ def forward( hidden_states = residual + hidden_states if hidden_states.dtype == torch.float16: hidden_states = hidden_states.clip(-65504, 65504) - print("norm_hidden_states.shape =", norm_hidden_states.shape) out = self.proj_mlp(norm_hidden_states) - print("out.shape =", out.shape) - print("self.proj_mlp.bias.shape =", self.proj_mlp.bias.shape) return hidden_states @@ -182,8 +174,6 @@ def get_sharded_data(data, dim): def shard_attn(attn: Attention): #attn.heads = 3 - print("shard_attn -> attn.to_q.in_features =", attn.to_q.in_features) - print("shard_attn -> attn.norm_q.weight.shape =", attn.norm_q.weight.shape) orig_q = attn.to_q attn.to_q = ColumnParallelLinear( @@ -319,9 +309,6 @@ def shard_attn_lite(block): block.proj_mlp.out_features, bias=(block.proj_mlp.bias is not None), gather_output=False) - #print("proj_mlp in_features:", block.proj_mlp.in_features) - #print("proj_mlp out_features:", block.proj_mlp.out_features) - print("proj_mlp.bias.shape:", block.proj_mlp.bias.shape) block.proj_mlp.weight.data = get_sharded_data(orig_mlp.weight.data, 0) if block.proj_mlp.bias is not None: block.proj_mlp.bias.data = get_sharded_data(orig_mlp.bias.data, 0) diff --git a/flux_serve/specs/compile-flux-1024x576.yaml b/flux_serve/specs/compile-flux-1024x576.yaml index 1b82533..2f640e6 100644 --- a/flux_serve/specs/compile-flux-1024x576.yaml +++ b/flux_serve/specs/compile-flux-1024x576.yaml @@ -19,15 +19,22 @@ spec: mountPath: /dev/shm - name: s3-flux-pvc mountPath: /model - command: ["sh", "-c", "while true; do sleep 3600; done"] + command: + - /bin/bash + - "-exc" + - | + set -x + mkdir -p /model/1024x576 + cd /src + ./compile.sh + cp -r * /model/1024x576 + while true; do sleep 3600; done resources: limits: aws.amazon.com/neuron: 8 requests: aws.amazon.com/neuron: 8 env: - - name: COMPILER_WORKDIR_ROOT - value: "/model" - name: HEIGHT value: "1024" - name: WIDTH diff --git a/flux_serve/specs/flux-model-s3-storage.yaml b/flux_serve/specs/flux-model-s3-storage.yaml index cf59de1..0d4471a 100644 --- a/flux_serve/specs/flux-model-s3-storage.yaml +++ b/flux_serve/specs/flux-model-s3-storage.yaml @@ -12,7 +12,7 @@ spec: namespace: default name: s3-flux-pvc mountOptions: - - region us-west-2 + - region=us-west-2 csi: driver: s3.csi.aws.com volumeHandle: s3-csi-driver-volume From 8d2da43e191ac0ed09a2bc9c3b8111f1e504f797 Mon Sep 17 00:00:00 2001 From: Yahav Date: Fri, 3 Jan 2025 20:46:59 -0800 Subject: [PATCH 14/45] compile12 --- flux_serve/README.md | 17 ++ flux_serve/app/benchmark-flux.py | 266 ++++++++++++++++++++ flux_serve/specs/compile-flux-1024x576.yaml | 3 + flux_serve/specs/flux-neuron-gradio.yaml | 53 ++++ flux_serve/specs/flux-neuron-ingress.yaml | 34 +++ 5 files changed, 373 insertions(+) create mode 100644 flux_serve/app/benchmark-flux.py create mode 100644 flux_serve/specs/flux-neuron-gradio.yaml create mode 100644 flux_serve/specs/flux-neuron-ingress.yaml diff --git a/flux_serve/README.md b/flux_serve/README.md index 20b373d..3184f14 100644 --- a/flux_serve/README.md +++ b/flux_serve/README.md @@ -31,6 +31,7 @@ data: ``` * Create an S3 bucket to store the model compiled graph and [enable Amazon S3 objects with Mountpoint for Amazon S3 CSI driver](https://docs.aws.amazon.com/eks/latest/userguide/s3-csi.html) * [Deploy the OCI image pipeline](./oci-image-build) +* [Deploy AWS Load Balancer controller](https://docs.aws.amazon.com/eks/latest/userguide/aws-load-balancer-controller.html) to enable public ingress access to the inference pods; export the k8s deployment as yaml and enforce nodeSelector to the non-neuron instances to avoid IMDS v1 limitation. * [Deploy the Neuron device plugin and scheduler extention](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/containers/kubernetes-getting-started.html#deploy-neuron-device-plugin) ```bash helm upgrade --install neuron-helm-chart oci://public.ecr.aws/neuron/neuron-helm-chart \ @@ -52,5 +53,21 @@ kubectl apply -f specs/flux-model-s3-storage.yaml * Compile the model for the requires shapes. We will demonstrate three shapes: 1024x576, 256x144, and 512x512 with `bfloat16` ```bash kubectl apply -f specs/compile-flux-1024x576.yaml +kubectl apply -f specs/compile-flux-256x144.yaml +kubectl apply -f specs/compile-flux-512x512.yaml +``` +Note the three pending Jobs that Karpenter seeks to fulfill. Current setup requires `aws.amazon.com/neuron: 8` which is half od `trn1.32xlarge` so expect two `trn1.xlarge` to be launched. + +* Deploy the Flux serving backend that loads the model from HuggingFace and uses the preloaded neuron model graph from S3 and standby for inference requests. The backend includes Deployment Pods and Services that route inference requests to the Pods so each model-api shapes scales horizontly. +```bash +kubectl apply -f specs/flux-neuron-1024x576-model-api.yaml +kubectl apply -f specs/flux-neuron-256x144-model-api.yaml +kubectl apply -f specs/flux-neuron-512x512-model-api.yaml +``` +Note the three pending Deployment Pods that Karpenter seeks to fulfill. Current setup requires `aws.amazon.com/neuron: 8` which is half od `trn1.32xlarge` so expect two `trn1.xlarge` to be launched. + +* Deploy the Flux serving frontend that includes Gradio app. +```bash +kubectl apply -f specs/flux-neuron-gradio.yaml ``` diff --git a/flux_serve/app/benchmark-flux.py b/flux_serve/app/benchmark-flux.py new file mode 100644 index 0000000..f1f5e4f --- /dev/null +++ b/flux_serve/app/benchmark-flux.py @@ -0,0 +1,266 @@ +import math +import time +import argparse +import torch +import torch.nn as nn +import torch_neuronx +import neuronx_distributed +import os +import gradio as gr +from fastapi import FastAPI +from huggingface_hub import login + +from diffusers import FluxPipeline +from diffusers.models.modeling_outputs import Transformer2DModelOutput +from typing import Any, Dict, Optional, Union + + +prompt= "A cat holding a sign that says hello world" +num_inference_steps=20 + +nodepool=os.environ['NODEPOOL'] +model_id=os.environ['MODEL_ID'] +device=os.environ["DEVICE"] +pod_name=os.environ['POD_NAME'] +hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() +height=int(os.environ['HEIGHT']) +width=int(os.environ['WIDTH']) +max_sequence_length=int(os.environ['MAX_SEQ_LEN']) +guidance_scale=float(os.environ['GUIDANCE_SCALE']) + +login(hf_token,add_to_git_credential=True) + +COMPILER_WORKDIR_ROOT=os.environ['COMPILER_WORKDIR_ROOT'] + +TEXT_ENCODER_PATH = os.path.join( + COMPILER_WORKDIR_ROOT, + 'text_encoder_1/compiled_model/model.pt') +TEXT_ENCODER_2_PATH = os.path.join( + COMPILER_WORKDIR_ROOT, + 'text_encoder_2/compiled_model/model.pt') +VAE_DECODER_PATH = os.path.join( + COMPILER_WORKDIR_ROOT, + 'decoder/compiled_model/model.pt') + +EMBEDDERS_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'transformer/compiled_model/embedders') +OUT_LAYERS_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'transformer/compiled_model/out_layers') +SINGLE_TRANSFORMER_BLOCKS_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'transformer/compiled_model/single_transformer_blocks') +TRANSFORMER_BLOCKS_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'transformer/compiled_model/transformer_blocks') + + +class NeuronFluxTransformer2DModel(nn.Module): + def __init__( + self, + config, + x_embedder, + context_embedder + ): + super().__init__() + with torch_neuronx.experimental.neuron_cores_context(start_nc=4, + nc_count=8): + self.embedders_model = \ + neuronx_distributed.trace.parallel_model_load(EMBEDDERS_DIR) + self.transformer_blocks_model = \ + neuronx_distributed.trace.parallel_model_load( + TRANSFORMER_BLOCKS_DIR) + self.single_transformer_blocks_model = \ + neuronx_distributed.trace.parallel_model_load( + SINGLE_TRANSFORMER_BLOCKS_DIR) + self.out_layers_model = \ + neuronx_distributed.trace.parallel_model_load( + OUT_LAYERS_DIR) + self.config = config + self.x_embedder = x_embedder + self.context_embedder = context_embedder + self.device = torch.device("cpu") + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.Tensor = None, + pooled_projections: torch.Tensor = None, + timestep: torch.LongTensor = None, + img_ids: torch.Tensor = None, + txt_ids: torch.Tensor = None, + guidance: torch.Tensor = None, + joint_attention_kwargs: Optional[Dict[str, Any]] = None, + return_dict: bool = False, + ) -> Union[torch.FloatTensor, Transformer2DModelOutput]: + + hidden_states = self.x_embedder(hidden_states) + + hidden_states, temb, image_rotary_emb = self.embedders_model( + hidden_states, + timestep, + guidance, + pooled_projections, + txt_ids, + img_ids + ) + + encoder_hidden_states = self.context_embedder(encoder_hidden_states) + + image_rotary_emb = image_rotary_emb.type(torch.bfloat16) + + encoder_hidden_states, hidden_states = self.transformer_blocks_model( + hidden_states, + encoder_hidden_states, + temb, + image_rotary_emb + ) + + hidden_states = torch.cat([encoder_hidden_states, hidden_states], + dim=1) + + hidden_states = self.single_transformer_blocks_model( + hidden_states, + temb, + image_rotary_emb + ) + + hidden_states = hidden_states.to(torch.bfloat16) + + return self.out_layers_model( + hidden_states, + encoder_hidden_states, + temb + ) + + +class NeuronFluxCLIPTextEncoderModel(nn.Module): + def __init__(self, dtype, encoder): + super().__init__() + self.dtype = dtype + self.encoder = encoder + self.device = torch.device("cpu") + + def forward(self, emb, output_hidden_states): + output = self.encoder(emb) + output = CLIPEncoderOutput(output) + return output + + +class CLIPEncoderOutput(): + def __init__(self, dictionary): + self.pooler_output = dictionary["pooler_output"] + + +class NeuronFluxT5TextEncoderModel(nn.Module): + def __init__(self, dtype, encoder): + super().__init__() + self.dtype = dtype + self.encoder = encoder + self.device = torch.device("cpu") + + def forward(self, emb, output_hidden_states): + return torch.unsqueeze(self.encoder(emb)["last_hidden_state"], 1) + + +def load_model( + prompt, + height, + width, + max_sequence_length, + num_inference_steps): + pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=torch.bfloat16) + with torch_neuronx.experimental.neuron_cores_context(start_nc=0): + pipe.text_encoder = NeuronFluxCLIPTextEncoderModel( + pipe.text_encoder.dtype, + torch.jit.load(TEXT_ENCODER_PATH)) + with torch_neuronx.experimental.neuron_cores_context(start_nc=8): + pipe.text_encoder_2 = NeuronFluxT5TextEncoderModel( + pipe.text_encoder_2.dtype, + torch.jit.load(TEXT_ENCODER_2_PATH)) + pipe.transformer = NeuronFluxTransformer2DModel( + pipe.transformer.config, + pipe.transformer.x_embedder, + pipe.transformer.context_embedder) + with torch_neuronx.experimental.neuron_cores_context(start_nc=8): + pipe.vae.decoder = torch.jit.load(VAE_DECODER_PATH) + + return pipe + +def benchmark(n_runs, test_name, model, model_inputs): + if not isinstance(model_inputs, tuple): + model_inputs = model_inputs + + warmup_run = model(**model_inputs) + + latency_collector = LatencyCollector() + + for _ in range(n_runs): + latency_collector.pre_hook() + res = model(**model_inputs) + image=res.images[0] + image.save(os.path.join(COMPILER_WORKDIR_ROOT, "flux-dev.png")) + latency_collector.hook() + + p0_latency_ms = latency_collector.percentile(0) * 1000 + p50_latency_ms = latency_collector.percentile(50) * 1000 + p90_latency_ms = latency_collector.percentile(90) * 1000 + p95_latency_ms = latency_collector.percentile(95) * 1000 + p99_latency_ms = latency_collector.percentile(99) * 1000 + p100_latency_ms = latency_collector.percentile(100) * 1000 + + report_dict = dict() + report_dict["Latency P0"] = f'{p0_latency_ms:.1f}' + report_dict["Latency P50"]=f'{p50_latency_ms:.1f}' + report_dict["Latency P90"]=f'{p90_latency_ms:.1f}' + report_dict["Latency P95"]=f'{p95_latency_ms:.1f}' + report_dict["Latency P99"]=f'{p99_latency_ms:.1f}' + report_dict["Latency P100"]=f'{p100_latency_ms:.1f}' + + report = f'RESULT FOR {test_name}:' + for key, value in report_dict.items(): + report += f' {key}={value}' + print(report) + return report + +class LatencyCollector: + def __init__(self): + self.start = None + self.latency_list = [] + + def pre_hook(self, *args): + self.start = time.time() + + def hook(self, *args): + self.latency_list.append(time.time() - self.start) + + def percentile(self, percent): + latency_list = self.latency_list + pos_float = len(latency_list) * percent / 100 + max_pos = len(latency_list) - 1 + pos_floor = min(math.floor(pos_float), max_pos) + pos_ceil = min(math.ceil(pos_float), max_pos) + latency_list = sorted(latency_list) + return latency_list[pos_ceil] if pos_float - pos_floor > 0.5 else latency_list[pos_floor] + +def text2img(prompt,num_inference_steps): + start_time = time.time() + model_args={'prompt':prompt,'height':height,'width':width,'max_sequence_length':max_sequence_length,'num_inference_steps': int(num_inference_steps),'guidance_scale':guidance_scale} + image = model(**model_args).images[0] + total_time = time.time()-start_time + return image, str(total_time) + + +model=load_model( + prompt, + height, + width, + max_sequence_length, + num_inference_steps +) +model_inputs={'prompt':prompt,'height':height,'width':width,'max_sequence_length':max_sequence_length,'num_inference_steps': num_inference_steps,'guidance_scale':guidance_scale} +test_name="flux1-dev-50runs on "+nodepool+";num_inference_steps:"+num_inference_steps +benchmark(50,test_name,model,model_inputs) diff --git a/flux_serve/specs/compile-flux-1024x576.yaml b/flux_serve/specs/compile-flux-1024x576.yaml index 2f640e6..2e2f6bc 100644 --- a/flux_serve/specs/compile-flux-1024x576.yaml +++ b/flux_serve/specs/compile-flux-1024x576.yaml @@ -28,6 +28,7 @@ spec: cd /src ./compile.sh cp -r * /model/1024x576 + python /benchmark-flux.py while true; do sleep 3600; done resources: limits: @@ -35,6 +36,8 @@ spec: requests: aws.amazon.com/neuron: 8 env: + - name: COMPILER_WORKDIR_ROOT + value: "/model/1024x576" - name: HEIGHT value: "1024" - name: WIDTH diff --git a/flux_serve/specs/flux-neuron-gradio.yaml b/flux_serve/specs/flux-neuron-gradio.yaml new file mode 100644 index 0000000..e7ef5e8 --- /dev/null +++ b/flux_serve/specs/flux-neuron-gradio.yaml @@ -0,0 +1,53 @@ +apiVersion: v1 +kind: Service +metadata: + name: flux-neuron-gradio +spec: + selector: + app: flux-neuron-gradio + ports: + - protocol: TCP + port: 8000 + targetPort: 8000 + type: ClusterIP +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + labels: + app: flux-neuron-gradio + name: flux-neuron-gradio +spec: + selector: + matchLabels: + app: flux-neuron-gradio + template: + metadata: + labels: + app: flux-neuron-gradio + spec: + nodeSelector: + alpha.eksctl.io/nodegroup-name: flux-usw2-ng + #serviceAccountName: appsimulator + containers: + - name: app + image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + imagePullPolicy: Always + #command: ["sh", "-c", "while true; do sleep 3600; done"] + command: ["sh", "-c", "uvicorn flux_gradio:app --host=0.0.0.0"] + ports: + - containerPort: 8000 + protocol: TCP + readinessProbe: + httpGet: + path: /readiness + port: 8000 + initialDelaySeconds: 60 + periodSeconds: 10 + env: + - name: MODEL_ID + value: "black-forest-labs/FLUX.1-dev" + - name: POD_NAME + valueFrom: + fieldRef: + fieldPath: metadata.name diff --git a/flux_serve/specs/flux-neuron-ingress.yaml b/flux_serve/specs/flux-neuron-ingress.yaml new file mode 100644 index 0000000..b27e4f0 --- /dev/null +++ b/flux_serve/specs/flux-neuron-ingress.yaml @@ -0,0 +1,34 @@ +apiVersion: networking.k8s.io/v1 +kind: Ingress +metadata: + name: flux-neuron + annotations: + kubernetes.io/ingress.class: alb + alb.ingress.kubernetes.io/scheme: internet-facing + alb.ingress.kubernetes.io/target-type: ip + alb.ingress.kubernetes.io/healthcheck-path: /health + alb.ingress.kubernetes.io/healthcheck-interval-seconds: '10' + alb.ingress.kubernetes.io/healthcheck-timeout-seconds: '9' + alb.ingress.kubernetes.io/healthy-threshold-count: '2' + alb.ingress.kubernetes.io/unhealthy-threshold-count: '10' + alb.ingress.kubernetes.io/success-codes: '200-301' + alb.ingress.kubernetes.io/listen-ports: '[{"HTTP":80}]' + alb.ingress.kubernetes.io/backend-protocol: HTTP + alb.ingress.kubernetes.io/target-group-attributes: stickiness.enabled=true,stickiness.lb_cookie.duration_seconds=10 + alb.ingress.kubernetes.io/load-balancer-name: flux-neuron + alb.ingress.kubernetes.io/actions.weighted-routing: > + {"type":"forward","forwardConfig":{"targetGroups":[{"serviceName":"flux-neuron-gradio","servicePort":8000,"weight":100}],"targetGroupStickinessConfig":{"enabled":true,"durationSeconds":200}}} + labels: + app: flux-neuron-gradio +spec: + ingressClassName: alb + rules: + - http: + paths: + - path: / + pathType: Prefix + backend: + service: + name: weighted-routing + port: + name: use-annotation From 0bbab64082a3213c45748f534ef3addf11a37f7f Mon Sep 17 00:00:00 2001 From: Yahav Date: Fri, 3 Jan 2025 23:49:22 -0800 Subject: [PATCH 15/45] compile13 --- flux_serve/app/benchmark-flux.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/flux_serve/app/benchmark-flux.py b/flux_serve/app/benchmark-flux.py index f1f5e4f..b592a99 100644 --- a/flux_serve/app/benchmark-flux.py +++ b/flux_serve/app/benchmark-flux.py @@ -20,8 +20,6 @@ nodepool=os.environ['NODEPOOL'] model_id=os.environ['MODEL_ID'] -device=os.environ["DEVICE"] -pod_name=os.environ['POD_NAME'] hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() height=int(os.environ['HEIGHT']) width=int(os.environ['WIDTH']) From e64de7096e5bd6eb4b2478f45a6c8ed192e58563 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sat, 4 Jan 2025 00:13:44 -0800 Subject: [PATCH 16/45] compile14 --- flux_serve/app/benchmark-flux.py | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/flux_serve/app/benchmark-flux.py b/flux_serve/app/benchmark-flux.py index b592a99..4f958a1 100644 --- a/flux_serve/app/benchmark-flux.py +++ b/flux_serve/app/benchmark-flux.py @@ -40,6 +40,9 @@ COMPILER_WORKDIR_ROOT, 'decoder/compiled_model/model.pt') +TEXT_ENCODER_2_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'text_encoder_2/compiled_model/text_encoder_2') EMBEDDERS_DIR = os.path.join( COMPILER_WORKDIR_ROOT, 'transformer/compiled_model/embedders') @@ -168,23 +171,22 @@ def load_model( width, max_sequence_length, num_inference_steps): - pipe = FluxPipeline.from_pretrained( - "black-forest-labs/FLUX.1-dev", - torch_dtype=torch.bfloat16) - with torch_neuronx.experimental.neuron_cores_context(start_nc=0): + with torch_neuronx.experimental.neuron_cores_context(start_nc=8): + pipe = CustomFluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=torch.bfloat16) + + with torch_neuronx.experimental.neuron_cores_context(start_nc=8): pipe.text_encoder = NeuronFluxCLIPTextEncoderModel( pipe.text_encoder.dtype, torch.jit.load(TEXT_ENCODER_PATH)) - with torch_neuronx.experimental.neuron_cores_context(start_nc=8): - pipe.text_encoder_2 = NeuronFluxT5TextEncoderModel( - pipe.text_encoder_2.dtype, - torch.jit.load(TEXT_ENCODER_2_PATH)) - pipe.transformer = NeuronFluxTransformer2DModel( - pipe.transformer.config, - pipe.transformer.x_embedder, - pipe.transformer.context_embedder) + with torch_neuronx.experimental.neuron_cores_context(start_nc=8): pipe.vae.decoder = torch.jit.load(VAE_DECODER_PATH) + + with torch_neuronx.experimental.neuron_cores_context(start_nc=0, nc_count=8): + sharded_text_encoder_2 = neuronx_distributed.trace.parallel_model_load(TEXT_ENCODER_2_DIR) + pipe.text_encoder_2 = TextEncoder2Wrapper(sharded_text_encoder_2) return pipe From c74af831fc352e7ea60c637b708fa1477ba65d76 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sat, 4 Jan 2025 11:02:36 -0800 Subject: [PATCH 17/45] complie15 --- flux_serve/app/benchmark-flux.py | 86 ++++++++++++++++---------------- 1 file changed, 43 insertions(+), 43 deletions(-) diff --git a/flux_serve/app/benchmark-flux.py b/flux_serve/app/benchmark-flux.py index 4f958a1..9de60b3 100644 --- a/flux_serve/app/benchmark-flux.py +++ b/flux_serve/app/benchmark-flux.py @@ -6,17 +6,12 @@ import torch_neuronx import neuronx_distributed import os -import gradio as gr -from fastapi import FastAPI -from huggingface_hub import login from diffusers import FluxPipeline from diffusers.models.modeling_outputs import Transformer2DModelOutput from typing import Any, Dict, Optional, Union - - -prompt= "A cat holding a sign that says hello world" -num_inference_steps=20 +from huggingface_hub import login +from huggingface_hub import whoami nodepool=os.environ['NODEPOOL'] model_id=os.environ['MODEL_ID'] @@ -26,9 +21,17 @@ max_sequence_length=int(os.environ['MAX_SEQ_LEN']) guidance_scale=float(os.environ['GUIDANCE_SCALE']) -login(hf_token,add_to_git_credential=True) +prompt= "A cat holding a sign that says hello world" +num_inference_steps=20 -COMPILER_WORKDIR_ROOT=os.environ['COMPILER_WORKDIR_ROOT'] +hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() +try: + user_info = whoami() + print(f"Already logged in as {user_info['name']}") +except: + login(hf_token,add_to_git_credential=True) + +COMPILER_WORKDIR_ROOT = os.environ['COMPILER_WORKDIR_ROOT'] TEXT_ENCODER_PATH = os.path.join( COMPILER_WORKDIR_ROOT, @@ -56,6 +59,23 @@ COMPILER_WORKDIR_ROOT, 'transformer/compiled_model/transformer_blocks') +class CustomFluxPipeline(FluxPipeline): + @property + def _execution_device(self): + return torch.device("cpu") + +class TextEncoder2Wrapper(nn.Module): + def __init__(self, sharded_model,dtype=torch.bfloat16): + super().__init__() + self.sharded_model = sharded_model + self.dtype = dtype + + def forward(self, input_ids, output_hidden_states=False, **kwargs): + attention_mask = (input_ids != 0).long() + output = self.sharded_model(input_ids,attention_mask) + last_hidden_state = output[0] + processed_output = last_hidden_state + return (processed_output,) class NeuronFluxTransformer2DModel(nn.Module): def __init__( @@ -65,19 +85,12 @@ def __init__( context_embedder ): super().__init__() - with torch_neuronx.experimental.neuron_cores_context(start_nc=4, - nc_count=8): - self.embedders_model = \ - neuronx_distributed.trace.parallel_model_load(EMBEDDERS_DIR) - self.transformer_blocks_model = \ - neuronx_distributed.trace.parallel_model_load( - TRANSFORMER_BLOCKS_DIR) - self.single_transformer_blocks_model = \ - neuronx_distributed.trace.parallel_model_load( - SINGLE_TRANSFORMER_BLOCKS_DIR) - self.out_layers_model = \ - neuronx_distributed.trace.parallel_model_load( - OUT_LAYERS_DIR) + with torch_neuronx.experimental.neuron_cores_context(start_nc=0, nc_count=8): + self.embedders_model = neuronx_distributed.trace.parallel_model_load(EMBEDDERS_DIR) + self.out_layers_model = neuronx_distributed.trace.parallel_model_load(OUT_LAYERS_DIR) + self.transformer_blocks_model = neuronx_distributed.trace.parallel_model_load(TRANSFORMER_BLOCKS_DIR) + self.single_transformer_blocks_model = neuronx_distributed.trace.parallel_model_load(SINGLE_TRANSFORMER_BLOCKS_DIR) + self.config = config self.x_embedder = x_embedder self.context_embedder = context_embedder @@ -153,18 +166,6 @@ class CLIPEncoderOutput(): def __init__(self, dictionary): self.pooler_output = dictionary["pooler_output"] - -class NeuronFluxT5TextEncoderModel(nn.Module): - def __init__(self, dtype, encoder): - super().__init__() - self.dtype = dtype - self.encoder = encoder - self.device = torch.device("cpu") - - def forward(self, emb, output_hidden_states): - return torch.unsqueeze(self.encoder(emb)["last_hidden_state"], 1) - - def load_model( prompt, height, @@ -188,6 +189,11 @@ def load_model( sharded_text_encoder_2 = neuronx_distributed.trace.parallel_model_load(TEXT_ENCODER_2_DIR) pipe.text_encoder_2 = TextEncoder2Wrapper(sharded_text_encoder_2) + pipe.transformer = NeuronFluxTransformer2DModel( + pipe.transformer.config, + pipe.transformer.x_embedder, + pipe.transformer.context_embedder) + return pipe def benchmark(n_runs, test_name, model, model_inputs): @@ -202,7 +208,7 @@ def benchmark(n_runs, test_name, model, model_inputs): latency_collector.pre_hook() res = model(**model_inputs) image=res.images[0] - image.save(os.path.join(COMPILER_WORKDIR_ROOT, "flux-dev.png")) + #image.save(os.path.join("/tmp", "flux-dev.png")) latency_collector.hook() p0_latency_ms = latency_collector.percentile(0) * 1000 @@ -254,13 +260,7 @@ def text2img(prompt,num_inference_steps): return image, str(total_time) -model=load_model( - prompt, - height, - width, - max_sequence_length, - num_inference_steps -) +model=load_model(prompt,height,width,max_sequence_length,num_inference_steps) model_inputs={'prompt':prompt,'height':height,'width':width,'max_sequence_length':max_sequence_length,'num_inference_steps': num_inference_steps,'guidance_scale':guidance_scale} -test_name="flux1-dev-50runs on "+nodepool+";num_inference_steps:"+num_inference_steps +test_name=f"flux1-dev-50runs on {nodepool};num_inference_steps:{num_inference_steps}" benchmark(50,test_name,model,model_inputs) From 741eb71e90f6a7d085c1f33fed8d28ac65ad5e48 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sat, 4 Jan 2025 11:34:42 -0800 Subject: [PATCH 18/45] compile15 --- flux_serve/app/benchmark-flux.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flux_serve/app/benchmark-flux.py b/flux_serve/app/benchmark-flux.py index 9de60b3..9dcda93 100644 --- a/flux_serve/app/benchmark-flux.py +++ b/flux_serve/app/benchmark-flux.py @@ -262,5 +262,5 @@ def text2img(prompt,num_inference_steps): model=load_model(prompt,height,width,max_sequence_length,num_inference_steps) model_inputs={'prompt':prompt,'height':height,'width':width,'max_sequence_length':max_sequence_length,'num_inference_steps': num_inference_steps,'guidance_scale':guidance_scale} -test_name=f"flux1-dev-50runs on {nodepool};num_inference_steps:{num_inference_steps}" +test_name=f"flux1-dev-50runs with dim {height}x{width} on {nodepool};num_inference_steps:{num_inference_steps}" benchmark(50,test_name,model,model_inputs) From 3fda9a2ae9144fe20a88027b1327e7b3d574d0bf Mon Sep 17 00:00:00 2001 From: Yahav Date: Sat, 4 Jan 2025 11:48:21 -0800 Subject: [PATCH 19/45] flux_model_api.py --- flux_serve/app/flux_model_api.py | 30 ++++++++++++++++++------------ 1 file changed, 18 insertions(+), 12 deletions(-) diff --git a/flux_serve/app/flux_model_api.py b/flux_serve/app/flux_model_api.py index 8f424ea..70857ed 100644 --- a/flux_serve/app/flux_model_api.py +++ b/flux_serve/app/flux_model_api.py @@ -55,13 +55,13 @@ def cw_pub_metric(metric_name,metric_value,metric_unit): TEXT_ENCODER_PATH = os.path.join( COMPILER_WORKDIR_ROOT, 'text_encoder_1/compiled_model/model.pt') -TEXT_ENCODER_2_PATH = os.path.join( - COMPILER_WORKDIR_ROOT, - 'text_encoder_2/compiled_model/model.pt') VAE_DECODER_PATH = os.path.join( COMPILER_WORKDIR_ROOT, 'decoder/compiled_model/model.pt') +TEXT_ENCODER_2_DIR = os.path.join( + COMPILER_WORKDIR_ROOT, + 'text_encoder_2/compiled_model/text_encoder_2') EMBEDDERS_DIR = os.path.join( COMPILER_WORKDIR_ROOT, 'transformer/compiled_model/embedders') @@ -193,25 +193,31 @@ def __init__(self, dtype, encoder): def forward(self, emb, output_hidden_states): return torch.unsqueeze(self.encoder(emb)["last_hidden_state"], 1) + + # Load the model pipeline def load_model(): - pipe = FluxPipeline.from_pretrained( - "black-forest-labs/FLUX.1-dev", - torch_dtype=DTYPE) - with torch_neuronx.experimental.neuron_cores_context(start_nc=0): + with torch_neuronx.experimental.neuron_cores_context(start_nc=8): + pipe = CustomFluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=torch.bfloat16) + + with torch_neuronx.experimental.neuron_cores_context(start_nc=8): pipe.text_encoder = NeuronFluxCLIPTextEncoderModel( pipe.text_encoder.dtype, torch.jit.load(TEXT_ENCODER_PATH)) + with torch_neuronx.experimental.neuron_cores_context(start_nc=8): - pipe.text_encoder_2 = NeuronFluxT5TextEncoderModel( - pipe.text_encoder_2.dtype, - torch.jit.load(TEXT_ENCODER_2_PATH)) + pipe.vae.decoder = torch.jit.load(VAE_DECODER_PATH) + + with torch_neuronx.experimental.neuron_cores_context(start_nc=0, nc_count=8): + sharded_text_encoder_2 = neuronx_distributed.trace.parallel_model_load(TEXT_ENCODER_2_DIR) + pipe.text_encoder_2 = TextEncoder2Wrapper(sharded_text_encoder_2) + pipe.transformer = NeuronFluxTransformer2DModel( pipe.transformer.config, pipe.transformer.x_embedder, pipe.transformer.context_embedder) - with torch_neuronx.experimental.neuron_cores_context(start_nc=8): - pipe.vae.decoder = torch.jit.load(VAE_DECODER_PATH) return pipe model = load_model() From 3d355e2d72199d2887cdd94ca848b792ef51b950 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sat, 4 Jan 2025 12:00:57 -0800 Subject: [PATCH 20/45] flux_model_api.py1 --- flux_serve/app/flux_model_api.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/flux_serve/app/flux_model_api.py b/flux_serve/app/flux_model_api.py index 70857ed..a4a8770 100644 --- a/flux_serve/app/flux_model_api.py +++ b/flux_serve/app/flux_model_api.py @@ -78,6 +78,11 @@ def cw_pub_metric(metric_name,metric_value,metric_unit): # Login to Hugging Face login(hf_token, add_to_git_credential=True) +class CustomFluxPipeline(FluxPipeline): + @property + def _execution_device(self): + return torch.device("cpu") + class GenerateImageRequest(BaseModel): prompt: str num_inference_steps: int From eeaa8cfb1741a5cecfba054f7a7975bb8b14e58e Mon Sep 17 00:00:00 2001 From: Yahav Date: Sat, 4 Jan 2025 12:27:23 -0800 Subject: [PATCH 21/45] flux_model_api.py1 --- flux_serve/app/flux_model_api.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/flux_serve/app/flux_model_api.py b/flux_serve/app/flux_model_api.py index a4a8770..cf0604d 100644 --- a/flux_serve/app/flux_model_api.py +++ b/flux_serve/app/flux_model_api.py @@ -83,6 +83,19 @@ class CustomFluxPipeline(FluxPipeline): def _execution_device(self): return torch.device("cpu") +class TextEncoder2Wrapper(nn.Module): + def __init__(self, sharded_model,dtype=torch.bfloat16): + super().__init__() + self.sharded_model = sharded_model + self.dtype = dtype + + def forward(self, input_ids, output_hidden_states=False, **kwargs): + attention_mask = (input_ids != 0).long() + output = self.sharded_model(input_ids,attention_mask) + last_hidden_state = output[0] + processed_output = last_hidden_state + return (processed_output,) + class GenerateImageRequest(BaseModel): prompt: str num_inference_steps: int From 86f3b377d10f7e130c97c1e85baf8c83e69fa585 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 5 Jan 2025 00:18:58 -0800 Subject: [PATCH 22/45] recompilewithtextenc2shard --- flux_serve/README.md | 9 +++ flux_serve/app/src/README.md | 2 +- flux_serve/app/src/compile.sh | 3 +- flux_serve/app/src/decoder/compile.py | 2 +- flux_serve/app/src/inference.py | 62 +++++++++++-------- flux_serve/app/src/text_encoder_1/compile.py | 2 +- flux_serve/app/src/text_encoder_1/model.py | 1 + flux_serve/app/src/text_encoder_2/compile.py | 63 ++++++++++++-------- flux_serve/app/src/text_encoder_2/model.py | 40 +++---------- flux_serve/app/src/transformer/compile.py | 53 +++++++--------- flux_serve/app/src/transformer/model.py | 54 +++++++---------- flux_serve/specs/compile-flux-1024x576.yaml | 8 ++- 12 files changed, 144 insertions(+), 155 deletions(-) diff --git a/flux_serve/README.md b/flux_serve/README.md index 3184f14..398ec86 100644 --- a/flux_serve/README.md +++ b/flux_serve/README.md @@ -69,5 +69,14 @@ Note the three pending Deployment Pods that Karpenter seeks to fulfill. Current * Deploy the Flux serving frontend that includes Gradio app. ```bash kubectl apply -f specs/flux-neuron-gradio.yaml +kubectl apply -f specs/flux-neuron-ingress.yaml ``` +Discover the model serving endpoint by: +```bash +kubectl get ingress +NAME CLASS HOSTS ADDRESS PORTS AGE +flux-neuron alb * flux-neuron-658286526.us-west-2.elb.amazonaws.com 80 7h20m +``` + +Use [flux-neuron-658286526.us-west-2.elb.amazonaws.com/serve/](flux-neuron-658286526.us-west-2.elb.amazonaws.com/serve/) diff --git a/flux_serve/app/src/README.md b/flux_serve/app/src/README.md index 7e7f6a6..f796700 100644 --- a/flux_serve/app/src/README.md +++ b/flux_serve/app/src/README.md @@ -1,4 +1,4 @@ -# Compile Flux +# flux-enigma clean previous compilation ```bash diff --git a/flux_serve/app/src/compile.sh b/flux_serve/app/src/compile.sh index 863cc9e..75cf813 100755 --- a/flux_serve/app/src/compile.sh +++ b/flux_serve/app/src/compile.sh @@ -1,5 +1,3 @@ -#!/bin/bash -x - find . -type d \( -name "compiled_model" -o -name "__pycache__" -o -name "compiler_workdir" \) -exec rm -rf {} + python "./text_encoder_1/compile.py" @@ -8,3 +6,4 @@ python "./transformer/compile.py" -hh $HEIGHT -w $WIDTH -m 32 python "./decoder/compile.py" -hh $HEIGHT -w $WIDTH echo "done compiling; cleaning compiler_workdir" find . -type d \( -name "__pycache__" -o -name "compiler_workdir" \) -exec rm -rf {} + + diff --git a/flux_serve/app/src/decoder/compile.py b/flux_serve/app/src/decoder/compile.py index 3d5cd7c..0ee3830 100644 --- a/flux_serve/app/src/decoder/compile.py +++ b/flux_serve/app/src/decoder/compile.py @@ -26,7 +26,7 @@ def trace_vae(height, width): del pipe latents = torch.rand([1, 16, height // 8, width // 8], - dtype=DTYPE) + dtype=torch.bfloat16) decoder_neuron = torch_neuronx.trace( decoder, diff --git a/flux_serve/app/src/inference.py b/flux_serve/app/src/inference.py index 8a23989..7530000 100644 --- a/flux_serve/app/src/inference.py +++ b/flux_serve/app/src/inference.py @@ -8,17 +8,8 @@ from diffusers import FluxPipeline from diffusers.models.modeling_outputs import Transformer2DModelOutput from typing import Any, Dict, Optional, Union -from huggingface_hub import login -from huggingface_hub import whoami -hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() -try: - user_info = whoami() - print(f"Already logged in as {user_info['name']}") -except: - login(hf_token,add_to_git_credential=True) - -#COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) -COMPILER_WORKDIR_ROOT = os.environ['COMPILER_WORKDIR_ROOT'] + +COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) TEXT_ENCODER_PATH = os.path.join( COMPILER_WORKDIR_ROOT, @@ -33,6 +24,7 @@ TEXT_ENCODER_2_DIR = os.path.join( COMPILER_WORKDIR_ROOT, 'text_encoder_2/compiled_model/text_encoder_2') + EMBEDDERS_DIR = os.path.join( COMPILER_WORKDIR_ROOT, 'transformer/compiled_model/embedders') @@ -64,6 +56,7 @@ def forward(self, input_ids, output_hidden_states=False, **kwargs): processed_output = last_hidden_state return (processed_output,) + class NeuronFluxTransformer2DModel(nn.Module): def __init__( self, @@ -72,12 +65,19 @@ def __init__( context_embedder ): super().__init__() - with torch_neuronx.experimental.neuron_cores_context(start_nc=0, nc_count=8): - self.embedders_model = neuronx_distributed.trace.parallel_model_load(EMBEDDERS_DIR) - self.out_layers_model = neuronx_distributed.trace.parallel_model_load(OUT_LAYERS_DIR) - self.transformer_blocks_model = neuronx_distributed.trace.parallel_model_load(TRANSFORMER_BLOCKS_DIR) - self.single_transformer_blocks_model = neuronx_distributed.trace.parallel_model_load(SINGLE_TRANSFORMER_BLOCKS_DIR) - + with torch_neuronx.experimental.neuron_cores_context(start_nc=0, + nc_count=8): + self.embedders_model = \ + neuronx_distributed.trace.parallel_model_load(EMBEDDERS_DIR) + self.transformer_blocks_model = \ + neuronx_distributed.trace.parallel_model_load( + TRANSFORMER_BLOCKS_DIR) + self.single_transformer_blocks_model = \ + neuronx_distributed.trace.parallel_model_load( + SINGLE_TRANSFORMER_BLOCKS_DIR) + self.out_layers_model = \ + neuronx_distributed.trace.parallel_model_load( + OUT_LAYERS_DIR) self.config = config self.x_embedder = x_embedder self.context_embedder = context_embedder @@ -153,30 +153,40 @@ class CLIPEncoderOutput(): def __init__(self, dictionary): self.pooler_output = dictionary["pooler_output"] + +class NeuronFluxT5TextEncoderModel(nn.Module): + def __init__(self, dtype, encoder): + super().__init__() + self.dtype = dtype + self.encoder = encoder + self.device = torch.device("cpu") + + def forward(self, emb, output_hidden_states): + return torch.unsqueeze(self.encoder(emb)["last_hidden_state"], 1) + + def run_inference( prompt, height, width, max_sequence_length, num_inference_steps): - with torch_neuronx.experimental.neuron_cores_context(start_nc=8): pipe = CustomFluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16) with torch_neuronx.experimental.neuron_cores_context(start_nc=8): - pipe.text_encoder = NeuronFluxCLIPTextEncoderModel( + pipe.text_encoder = NeuronFluxCLIPTextEncoderModel( pipe.text_encoder.dtype, torch.jit.load(TEXT_ENCODER_PATH)) with torch_neuronx.experimental.neuron_cores_context(start_nc=8): - pipe.vae.decoder = torch.jit.load(VAE_DECODER_PATH) + pipe.vae.decoder = torch.jit.load(VAE_DECODER_PATH) with torch_neuronx.experimental.neuron_cores_context(start_nc=0, nc_count=8): - sharded_text_encoder_2 = neuronx_distributed.trace.parallel_model_load(TEXT_ENCODER_2_DIR) - pipe.text_encoder_2 = TextEncoder2Wrapper(sharded_text_encoder_2) - + sharded_text_encoder_2 = neuronx_distributed.trace.parallel_model_load(TEXT_ENCODER_2_DIR) + pipe.text_encoder_2 = TextEncoder2Wrapper(sharded_text_encoder_2) pipe.transformer = NeuronFluxTransformer2DModel( pipe.transformer.config, @@ -207,21 +217,21 @@ def run_inference( "-hh", "--height", type=int, - default=256, + default=1024, help="height of images to be generated by compilation of this model" ) parser.add_argument( "-w", "--width", type=int, - default=256, + default=1024, help="width of images to be generated by compilation of this model" ) parser.add_argument( "-m", "--max_sequence_length", type=int, - default=32, + default=512, help="maximum sequence length for the text embeddings" ) parser.add_argument( diff --git a/flux_serve/app/src/text_encoder_1/compile.py b/flux_serve/app/src/text_encoder_1/compile.py index ccb732a..871f388 100644 --- a/flux_serve/app/src/text_encoder_1/compile.py +++ b/flux_serve/app/src/text_encoder_1/compile.py @@ -19,7 +19,7 @@ def trace_text_encoder(): pipe = FluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", - torch_dtype=DTYPE) + torch_dtype=torch.bfloat16) text_encoder = copy.deepcopy(pipe.text_encoder) del pipe diff --git a/flux_serve/app/src/text_encoder_1/model.py b/flux_serve/app/src/text_encoder_1/model.py index c046b5c..13151c4 100644 --- a/flux_serve/app/src/text_encoder_1/model.py +++ b/flux_serve/app/src/text_encoder_1/model.py @@ -31,3 +31,4 @@ def forward( output_hidden_states=output_hidden_states, return_dict=True, ) + diff --git a/flux_serve/app/src/text_encoder_2/compile.py b/flux_serve/app/src/text_encoder_2/compile.py index 9d2fa10..15e11c4 100644 --- a/flux_serve/app/src/text_encoder_2/compile.py +++ b/flux_serve/app/src/text_encoder_2/compile.py @@ -3,10 +3,10 @@ import os import torch import torch_neuronx -import neuronx_distributed from diffusers import FluxPipeline +from model import TracingT5TextEncoderWrapper +import neuronx_distributed from transformers import T5EncoderModel - from model import ( TracingT5TextEncoderWrapper, init_text_encoder_2, @@ -25,36 +25,15 @@ DTYPE=torch.bfloat16 def build_text_encoder_2(): - """ - Build the T5 text encoder model, shard it, and wrap it in our tracing class. - Returns the final model + any (optional) kwargs in a dict, i.e. (model, {}). - This pattern is required by neuronx_distributed.trace.parallel_model_trace. - """ - # Load pipeline and copy the text_encoder_2 from your flux pipeline - pipe = FluxPipeline.from_pretrained( - "black-forest-labs/FLUX.1-dev", - torch_dtype=DTYPE - ) - # text_encoder_2 is presumably a T5EncoderModel or custom T5-based module + pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev",torch_dtype=DTYPE) text_encoder_2 = copy.deepcopy(pipe.text_encoder_2) del pipe - # Optionally, if text_encoder_2 is not already a T5EncoderModel, you can - # modify the approach. But presumably, it's a T5EncoderModel or T5-like module. - # Shard the T5 model parameters across tp_degree ranks: init_text_encoder_2(text_encoder_2) - - # Wrap it in a tracing module wrapper = TracingT5TextEncoderWrapper(text_encoder_2) - - # Return the model plus optional kwargs (unused here) return wrapper, {} - def trace_text_encoder_2(max_sequence_length=512): - """ - Trace the T5 text encoder with parallel model trace. - """ input_ids = torch.zeros((1, max_sequence_length), dtype=torch.int64) attention_mask = torch.ones((1, max_sequence_length), dtype=torch.int64) @@ -79,14 +58,46 @@ def trace_text_encoder_2(max_sequence_length=512): del model +''' +def trace_text_encoder_2(max_sequence_length): + pipe = FluxPipeline.from_pretrained( + "black-forest-labs/FLUX.1-dev", + torch_dtype=torch.bfloat16) + text_encoder_2 = copy.deepcopy(pipe.text_encoder_2) + del pipe + + text_encoder_2 = TracingT5TextEncoderWrapper(text_encoder_2) + + emb = torch.zeros((1, max_sequence_length), dtype=torch.int64) + + text_encoder_2_neuron = torch_neuronx.trace( + text_encoder_2.neuron_text_encoder, + emb, + compiler_workdir=os.path.join(COMPILER_WORKDIR_ROOT, + 'compiler_workdir'), + compiler_args=["--enable-fast-loading-neuron-binaries"] + ) + + torch_neuronx.async_load(text_encoder_2_neuron) + + compiled_model_path = os.path.join(COMPILER_WORKDIR_ROOT, 'compiled_model') + if not os.path.exists(compiled_model_path): + os.mkdir(compiled_model_path) + text_encoder_2_filename = os.path.join(COMPILER_WORKDIR_ROOT, + 'compiled_model/model.pt') + torch.jit.save(text_encoder_2_neuron, text_encoder_2_filename) + + del text_encoder_2 + del text_encoder_2_neuron +''' -if __name__ == "__main__": +if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument( "-m", "--max_sequence_length", type=int, - default=32, + default=512, help="maximum sequence length for the text embeddings" ) args = parser.parse_args() diff --git a/flux_serve/app/src/text_encoder_2/model.py b/flux_serve/app/src/text_encoder_2/model.py index 3916ec5..c63caed 100644 --- a/flux_serve/app/src/text_encoder_2/model.py +++ b/flux_serve/app/src/text_encoder_2/model.py @@ -1,5 +1,6 @@ import torch import torch.nn as nn + from transformers.modeling_outputs import BaseModelOutput from typing import Optional, Union, Tuple @@ -8,13 +9,9 @@ ColumnParallelLinear, RowParallelLinear, ) - DTYPE=torch.bfloat16 def get_sharded_data(data: torch.Tensor, dim: int) -> torch.Tensor: - """ - Utility function to slice a parameter across tensor-parallel ranks. - """ tp_rank = parallel_state.get_tensor_model_parallel_rank() tp_size = parallel_state.get_tensor_model_parallel_size() per_partition_size = data.shape[dim] // tp_size @@ -35,10 +32,6 @@ def get_sharded_data(data: torch.Tensor, dim: int) -> torch.Tensor: raise ValueError("Partition dimension must be 0 or 1.") def shard_t5_attention(t5_attention): - """ - Shard the T5 self-attention q, k, v, o projections to ColumnParallel & RowParallel. - This is analogous to shard_attn() in transformer/model.py but adapted for T5. - """ # Shard q orig_q = t5_attention.q t5_attention.q = ColumnParallelLinear( @@ -93,16 +86,6 @@ def shard_t5_attention(t5_attention): del orig_o def shard_t5_ff(ff_block): - """ - Shard the T5 feed-forward block (DenseReluDense or DenseGatedActDense). - Depending on the HF version, T5 may implement FF as: - - T5DenseReluDense, with attributes: ff_block.wi, ff_block.wo - - T5DenseGatedActDense, with attributes: ff_block.wi_0, ff_block.wi_1, ff_block.wo - We'll shard them similarly: - - ColumnParallel for the 'wi_*' inputs - - RowParallel for the 'wo' output - """ - # Helper function for ColumnParallel def make_column_parallel(orig_layer): from neuronx_distributed.parallel_layers.layers import ColumnParallelLinear @@ -132,9 +115,6 @@ def make_row_parallel(orig_layer): new_layer.bias.data = orig_layer.bias.data.detach() return new_layer - # ------------------------------------------------------------- - # 1) T5DenseReluDense -> ff_block.wi, ff_block.wo - # ------------------------------------------------------------- if hasattr(ff_block, "wi") and hasattr(ff_block, "wo"): orig_wi = ff_block.wi ff_block.wi = make_column_parallel(orig_wi) @@ -144,9 +124,6 @@ def make_row_parallel(orig_layer): ff_block.wo = make_row_parallel(orig_wo) del orig_wo - # ------------------------------------------------------------- - # 2) T5DenseGatedActDense -> ff_block.wi_0, ff_block.wi_1, ff_block.wo - # ------------------------------------------------------------- elif hasattr(ff_block, "wi_0") and hasattr(ff_block, "wi_1") and hasattr(ff_block, "wo"): orig_wi_0 = ff_block.wi_0 ff_block.wi_0 = make_column_parallel(orig_wi_0) @@ -167,10 +144,6 @@ def make_row_parallel(orig_layer): ) def init_text_encoder_2(t5_encoder): - """ - Loop over T5Blocks in t5_encoder to shard the self-attention and FF modules. - Typically, T5EncoderModel has model.encoder.block[i].layer[0/1]. - """ encoder_stack = t5_encoder.encoder # T5Stack for block in encoder_stack.block: # block.layer[0] => T5LayerSelfAttention @@ -180,11 +153,8 @@ def init_text_encoder_2(t5_encoder): ff = block.layer[1].DenseReluDense shard_t5_ff(ff) + class TracingT5TextEncoderWrapper(nn.Module): - """ - Wrap the T5 encoder in a forward that returns a stable tuple or BaseModelOutput, - suitable for compilation and serving on Neuron devices. - """ def __init__(self, text_encoder): super().__init__() self.neuron_text_encoder = text_encoder @@ -202,8 +172,10 @@ def forward( output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = False, ) -> Union[Tuple[torch.FloatTensor], BaseModelOutput]: + return_dict = return_dict if return_dict is not None \ + else self.config.use_return_dict - return self.neuron_text_encoder( + encoder_outputs = self.neuron_text_encoder( input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, @@ -213,3 +185,5 @@ def forward( return_dict=return_dict, ) + return encoder_outputs + diff --git a/flux_serve/app/src/transformer/compile.py b/flux_serve/app/src/transformer/compile.py index 69db269..21fcd18 100644 --- a/flux_serve/app/src/transformer/compile.py +++ b/flux_serve/app/src/transformer/compile.py @@ -11,9 +11,7 @@ TracingTransformerBlockWrapper, TracingSingleTransformerBlockWrapper, TracingTransformerOutLayerWrapper, - MyAttentionProcessor, init_transformer) - from huggingface_hub import login from huggingface_hub import whoami hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() @@ -25,12 +23,12 @@ COMPILER_WORKDIR_ROOT = os.path.dirname(__file__) TP_DEGREE=8 -DTYPE=torch.bfloat16 + def trace_transformer_embedders(): pipe = FluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", - torch_dtype=DTYPE) + torch_dtype=torch.bfloat16) transformer: FluxTransformer2DModel = copy.deepcopy(pipe.transformer) del pipe init_transformer(transformer) @@ -44,10 +42,7 @@ def trace_transformer_embedders(): def trace_transformer_blocks(): pipe = FluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", - torch_dtype=DTYPE) - for block in pipe.transformer.transformer_blocks: - block.attn.processor = MyAttentionProcessor() - + torch_dtype=torch.bfloat16) transformer: FluxTransformer2DModel = copy.deepcopy(pipe.transformer) del pipe init_transformer(transformer) @@ -60,9 +55,7 @@ def trace_transformer_blocks(): def trace_single_transformer_blocks(): pipe = FluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", - torch_dtype=DTYPE) - for block in pipe.transformer.single_transformer_blocks: - block.attn.processor = MyAttentionProcessor() + torch_dtype=torch.bfloat16) transformer: FluxTransformer2DModel = copy.deepcopy(pipe.transformer) del pipe init_transformer(transformer) @@ -75,7 +68,7 @@ def trace_single_transformer_blocks(): def trace_transformer_out_layers(): pipe = FluxPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", - torch_dtype=DTYPE) + torch_dtype=torch.bfloat16) transformer: FluxTransformer2DModel = copy.deepcopy(pipe.transformer) del pipe init_transformer(transformer) @@ -87,12 +80,12 @@ def trace_transformer_out_layers(): def trace_transformer(height, width, max_sequence_length): hidden_states = torch.rand([1, height * width // 256, 3072], - dtype=DTYPE) - timestep = torch.rand([1], dtype=DTYPE) + dtype=torch.bfloat16) + timestep = torch.rand([1], dtype=torch.bfloat16) guidance = torch.rand([1], dtype=torch.float32) - pooled_projections = torch.rand([1, 768], dtype=DTYPE) - txt_ids = torch.rand([1, max_sequence_length, 3], dtype=DTYPE) - img_ids = torch.rand([1, height * width // 256, 3], dtype=DTYPE) + pooled_projections = torch.rand([1, 768], dtype=torch.bfloat16) + txt_ids = torch.rand([1, max_sequence_length, 3], dtype=torch.bfloat16) + img_ids = torch.rand([1, height * width // 256, 3], dtype=torch.bfloat16) sample_inputs = hidden_states, timestep, guidance, pooled_projections, \ txt_ids, img_ids @@ -117,13 +110,13 @@ def trace_transformer(height, width, max_sequence_length): del model hidden_states = torch.rand([1, height * width // 256, 3072], - dtype=DTYPE) + dtype=torch.bfloat16) encoder_hidden_states = torch.rand([1, max_sequence_length, 3072], - dtype=DTYPE) - temb = torch.rand([1, 3072], dtype=DTYPE) + dtype=torch.bfloat16) + temb = torch.rand([1, 3072], dtype=torch.bfloat16) image_rotary_emb = torch.rand( [1, 1, height * width // 256 + max_sequence_length, 64, 2, 2], - dtype=DTYPE) + dtype=torch.bfloat16) sample_inputs = hidden_states, encoder_hidden_states, \ temb, image_rotary_emb @@ -146,11 +139,11 @@ def trace_transformer(height, width, max_sequence_length): hidden_states = torch.rand( [1, height * width // 256 + max_sequence_length, 3072], - dtype=DTYPE) - temb = torch.rand([1, 3072], dtype=DTYPE) + dtype=torch.bfloat16) + temb = torch.rand([1, 3072], dtype=torch.bfloat16) image_rotary_emb = torch.rand( [1, 1, height * width // 256 + max_sequence_length, 64, 2, 2], - dtype=DTYPE) + dtype=torch.bfloat16) sample_inputs = hidden_states, temb, image_rotary_emb model = neuronx_distributed.trace.parallel_model_trace( @@ -172,10 +165,10 @@ def trace_transformer(height, width, max_sequence_length): hidden_states = torch.rand( [1, height * width // 256 + max_sequence_length, 3072], - dtype=DTYPE) + dtype=torch.bfloat16) encoder_hidden_states = torch.rand([1, max_sequence_length, 3072], - dtype=DTYPE) - temb = torch.rand([1, 3072], dtype=DTYPE) + dtype=torch.bfloat16) + temb = torch.rand([1, 3072], dtype=torch.bfloat16) sample_inputs = hidden_states, encoder_hidden_states, temb model = neuronx_distributed.trace.parallel_model_trace( @@ -202,21 +195,21 @@ def trace_transformer(height, width, max_sequence_length): "-hh", "--height", type=int, - default=256, + default=1024, help="height of images to be generated by compilation of this model" ) parser.add_argument( "-w", "--width", type=int, - default=256, + default=1024, help="width of images to be generated by compilation of this model" ) parser.add_argument( "-m", "--max_sequence_length", type=int, - default=32, + default=512, help="maximum sequence length for the text embeddings" ) args = parser.parse_args() diff --git a/flux_serve/app/src/transformer/model.py b/flux_serve/app/src/transformer/model.py index ce9f306..3689de4 100644 --- a/flux_serve/app/src/transformer/model.py +++ b/flux_serve/app/src/transformer/model.py @@ -8,23 +8,8 @@ from neuronx_distributed.parallel_layers.layers \ import ColumnParallelLinear, RowParallelLinear -from diffusers.models.attention_processor import AttnProcessor - DTYPE=torch.bfloat16 -class MyAttentionProcessor(AttnProcessor): - def __call__(self, attn, hidden_states,encoder_hidden_states=None,cross_attention_kwargs=None,**kwargs): - if hidden_states is not None: - n_heads = 24 - head_dim = 128 - batch, seq_len, dim = hidden_states.shape - assert dim == n_heads * head_dim, f"Dim mismatch: got {dim}, expected {n_heads*head_dim}" - - hidden_states = hidden_states.view(batch, seq_len, n_heads, head_dim) - hidden_states = attn.norm_q(hidden_states) - hidden_states = hidden_states.view(batch, seq_len, dim) - return hidden_states, encoder_hidden_states - class TracingTransformerEmbedderWrapper(nn.Module): def __init__( self, @@ -141,15 +126,17 @@ def forward( norm_hidden_states, gate = self.norm(hidden_states, emb=temb) mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states)) - #attn_output = self.attn(hidden_states=norm_hidden_states,image_rotary_emb=image_rotary_emb,) - attn_output, cross_output = self.attn(hidden_states=norm_hidden_states, image_rotary_emb=image_rotary_emb) + attn_output = self.attn( + hidden_states=norm_hidden_states, + image_rotary_emb=image_rotary_emb, + ) gate = gate.unsqueeze(1) hidden_states = gate * (self.proj_out(attn_output) + self.proj_out_2(mlp_hidden_states)) hidden_states = residual + hidden_states if hidden_states.dtype == torch.float16: hidden_states = hidden_states.clip(-65504, 65504) - out = self.proj_mlp(norm_hidden_states) + return hidden_states @@ -160,12 +147,12 @@ def get_sharded_data(data, dim): if dim == 0: return data[ per_partition_size * tp_rank: per_partition_size * (tp_rank + 1) - ].clone().to(DTYPE) + ].clone().to(torch.bfloat16) elif dim == 1: return data[:, per_partition_size * tp_rank: per_partition_size * (tp_rank + 1) - ].clone().to(DTYPE) + ].clone().to(torch.bfloat16) else: raise Exception( f"Partiton value of 0,1 are supported, found {dim}." @@ -173,14 +160,14 @@ def get_sharded_data(data, dim): def shard_attn(attn: Attention): - #attn.heads = 3 + attn.heads = 3 orig_q = attn.to_q attn.to_q = ColumnParallelLinear( attn.to_q.in_features, attn.to_q.out_features, bias=(attn.to_q.bias is not None), - gather_output=True) + gather_output=False) attn.to_q.weight.data = get_sharded_data(orig_q.weight.data, 0) if attn.to_q.bias is not None: attn.to_q.bias.data = get_sharded_data(orig_q.bias.data, 0) @@ -191,7 +178,7 @@ def shard_attn(attn: Attention): attn.to_k.in_features, attn.to_k.out_features, bias=(attn.to_k.bias is not None), - gather_output=True) + gather_output=False) attn.to_k.weight.data = get_sharded_data(orig_k.weight.data, 0) if attn.to_k.bias is not None: attn.to_k.bias.data = get_sharded_data(orig_k.bias.data, 0) @@ -202,7 +189,7 @@ def shard_attn(attn: Attention): attn.to_v.in_features, attn.to_v.out_features, bias=(attn.to_v.bias is not None), - gather_output=True) + gather_output=False) attn.to_v.weight.data = get_sharded_data(orig_v.weight.data, 0) if attn.to_v.bias is not None: attn.to_v.bias.data = get_sharded_data(orig_v.bias.data, 0) @@ -213,7 +200,7 @@ def shard_attn(attn: Attention): attn.add_q_proj.in_features, attn.add_q_proj.out_features, bias=(attn.add_q_proj.bias is not None), - gather_output=True) + gather_output=False) attn.add_q_proj.weight.data = get_sharded_data(orig_q_proj.weight.data, 0) if attn.add_q_proj.bias is not None: attn.add_q_proj.bias.data = get_sharded_data(orig_q_proj.bias.data, 0) @@ -224,7 +211,7 @@ def shard_attn(attn: Attention): attn.add_k_proj.in_features, attn.add_k_proj.out_features, bias=(attn.add_k_proj.bias is not None), - gather_output=True) + gather_output=False) attn.add_k_proj.weight.data = get_sharded_data(orig_k_proj.weight.data, 0) if attn.add_k_proj.bias is not None: attn.add_k_proj.bias.data = get_sharded_data(orig_k_proj.bias.data, 0) @@ -235,7 +222,7 @@ def shard_attn(attn: Attention): attn.add_v_proj.in_features, attn.add_v_proj.out_features, bias=(attn.add_v_proj.bias is not None), - gather_output=True) + gather_output=False) attn.add_v_proj.weight.data = get_sharded_data(orig_v_proj.weight.data, 0) if attn.add_v_proj.bias is not None: attn.add_v_proj.bias.data = get_sharded_data(orig_v_proj.bias.data, 0) @@ -246,7 +233,7 @@ def shard_attn(attn: Attention): attn.to_out[0].in_features, attn.to_out[0].out_features, bias=(attn.to_out[0].bias is not None), - input_is_parallel=False) + input_is_parallel=True) attn.to_out[0].weight.data = get_sharded_data(orig_out.weight.data, 1) if attn.to_out[0].bias is not None: attn.to_out[0].bias.data = orig_out.bias.data.detach() @@ -257,25 +244,24 @@ def shard_attn(attn: Attention): attn.to_add_out.in_features, attn.to_add_out.out_features, bias=(attn.to_add_out.bias is not None), - input_is_parallel=False) + input_is_parallel=True) attn.to_add_out.weight.data = get_sharded_data(orig_out.weight.data, 1) if attn.to_add_out.bias is not None: attn.to_add_out.bias.data = orig_out.bias.data.detach() del (orig_out) - return attn def shard_attn_lite(block): attn = block.attn - #attn.heads = 3 + attn.heads = 3 orig_q = attn.to_q attn.to_q = ColumnParallelLinear( attn.to_q.in_features, attn.to_q.out_features, bias=(attn.to_q.bias is not None), - gather_output=True) + gather_output=False) attn.to_q.weight.data = get_sharded_data(orig_q.weight.data, 0) if attn.to_q.bias is not None: attn.to_q.bias.data = get_sharded_data(orig_q.bias.data, 0) @@ -286,7 +272,7 @@ def shard_attn_lite(block): attn.to_k.in_features, attn.to_k.out_features, bias=(attn.to_k.bias is not None), - gather_output=True) + gather_output=False) attn.to_k.weight.data = get_sharded_data(orig_k.weight.data, 0) if attn.to_k.bias is not None: attn.to_k.bias.data = get_sharded_data(orig_k.bias.data, 0) @@ -297,7 +283,7 @@ def shard_attn_lite(block): attn.to_v.in_features, attn.to_v.out_features, bias=(attn.to_v.bias is not None), - gather_output=True) + gather_output=False) attn.to_v.weight.data = get_sharded_data(orig_v.weight.data, 0) if attn.to_v.bias is not None: attn.to_v.bias.data = get_sharded_data(orig_v.bias.data, 0) diff --git a/flux_serve/specs/compile-flux-1024x576.yaml b/flux_serve/specs/compile-flux-1024x576.yaml index 2e2f6bc..600cf59 100644 --- a/flux_serve/specs/compile-flux-1024x576.yaml +++ b/flux_serve/specs/compile-flux-1024x576.yaml @@ -29,19 +29,25 @@ spec: ./compile.sh cp -r * /model/1024x576 python /benchmark-flux.py - while true; do sleep 3600; done + #while true; do sleep 3600; done resources: limits: aws.amazon.com/neuron: 8 requests: aws.amazon.com/neuron: 8 env: + - name: NODEPOOL + value: "amd-neuron-trn1" - name: COMPILER_WORKDIR_ROOT value: "/model/1024x576" - name: HEIGHT value: "1024" - name: WIDTH value: "576" + - name: MAX_SEQ_LEN + value: "32" + - name: GUIDANCE_SCALE + value: "3.5" - name: MODEL_ID value: "black-forest-labs/FLUX.1-dev" - name: HUGGINGFACE_TOKEN From 9745f23d97b32594cbf1ef79e310128b200a263b Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 5 Jan 2025 10:34:14 -0800 Subject: [PATCH 23/45] model-warm-up-with-benchmark --- flux_serve/app/flux_model_api.py | 61 +++++++++++++++++++++++++++++++- 1 file changed, 60 insertions(+), 1 deletion(-) diff --git a/flux_serve/app/flux_model_api.py b/flux_serve/app/flux_model_api.py index cf0604d..2bf6d3f 100644 --- a/flux_serve/app/flux_model_api.py +++ b/flux_serve/app/flux_model_api.py @@ -211,7 +211,60 @@ def __init__(self, dtype, encoder): def forward(self, emb, output_hidden_states): return torch.unsqueeze(self.encoder(emb)["last_hidden_state"], 1) - +def benchmark(n_runs, test_name, model, model_inputs): + if not isinstance(model_inputs, tuple): + model_inputs = model_inputs + + warmup_run = model(**model_inputs) + + latency_collector = LatencyCollector() + + for _ in range(n_runs): + latency_collector.pre_hook() + res = model(**model_inputs) + image=res.images[0] + #image.save(os.path.join("/tmp", "flux-dev.png")) + latency_collector.hook() + p0_latency_ms = latency_collector.percentile(0) * 1000 + p50_latency_ms = latency_collector.percentile(50) * 1000 + p90_latency_ms = latency_collector.percentile(90) * 1000 + p95_latency_ms = latency_collector.percentile(95) * 1000 + p99_latency_ms = latency_collector.percentile(99) * 1000 + p100_latency_ms = latency_collector.percentile(100) * 1000 + + report_dict = dict() + report_dict["Latency P0"] = f'{p0_latency_ms:.1f}' + report_dict["Latency P50"]=f'{p50_latency_ms:.1f}' + report_dict["Latency P90"]=f'{p90_latency_ms:.1f}' + report_dict["Latency P95"]=f'{p95_latency_ms:.1f}' + report_dict["Latency P99"]=f'{p99_latency_ms:.1f}' + report_dict["Latency P100"]=f'{p100_latency_ms:.1f}' + + report = f'RESULT FOR {test_name}:' + for key, value in report_dict.items(): + report += f' {key}={value}' + print(report) + return report + +class LatencyCollector: + def __init__(self): + self.start = None + self.latency_list = [] + + def pre_hook(self, *args): + self.start = time.time() + + def hook(self, *args): + self.latency_list.append(time.time() - self.start) + + def percentile(self, percent): + latency_list = self.latency_list + pos_float = len(latency_list) * percent / 100 + max_pos = len(latency_list) - 1 + pos_floor = min(math.floor(pos_float), max_pos) + pos_ceil = min(math.ceil(pos_float), max_pos) + latency_list = sorted(latency_list) + return latency_list[pos_ceil] if pos_float - pos_floor > 0.5 else latency_list[pos_floor] # Load the model pipeline def load_model(): @@ -239,6 +292,12 @@ def load_model(): return pipe model = load_model() +#Warmup +prompt= "A cat holding a sign that says hello world" +num_inference_steps=10 +model_inputs={'prompt':prompt,'height':height,'width':width,'max_sequence_length':max_sequence_length,'num_inference_steps': num_inference_steps,'guidance_scale':guidance_scale} +test_name=f"flux1-dev-50runs with dim {height}x{width} on {nodepool};num_inference_steps:{num_inference_steps}" +benchmark(50,test_name,model,model_inputs) # Define the image generation endpoint @app.post("/generate", response_model=GenerateImageResponse) From cf32b28830f040d279fbf6173d5bc2ec561a7e13 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 5 Jan 2025 15:28:40 -0800 Subject: [PATCH 24/45] flux_gradio.py --- flux_serve/app/flux_gradio.py | 84 ++++++++++++++++++++++++----------- 1 file changed, 59 insertions(+), 25 deletions(-) diff --git a/flux_serve/app/flux_gradio.py b/flux_serve/app/flux_gradio.py index 131ef9e..36696cb 100644 --- a/flux_serve/app/flux_gradio.py +++ b/flux_serve/app/flux_gradio.py @@ -80,7 +80,7 @@ def healthy(): @app.get("/readiness") def ready(): return {"message": "Service is ready"} - +''' with gr.Blocks() as interface: gr.Markdown(f"# {model_id} Image Generation App") gr.Markdown("Enter a prompt and specify the number of inference steps to generate images in different shapes.") @@ -116,28 +116,62 @@ def ready(): outputs=image_components + exec_time_components, api_name="generate_images" ) -app = gr.mount_gradio_app(app, interface, path="/serve") -''' -interface = gr.Interface( - fn=call_model_api, - inputs=[ - gr.Textbox(label="Prompt", lines=1, placeholder="Enter your prompt here..."), - gr.Number( - label="Inference Steps", - value=10, - precision=0, - info="Enter the number of inference steps; higher number takes more time but produces better image" - ) - ], - outputs=[ - gr.Image(label=f"Image from {models[0]['name']}", height=models[0]['height'], width=models[0]['width']), - gr.Textbox(label=f"Execution Time ({models[0]['name']})"), - gr.Image(label=f"Image from {models[1]['name']}", height=models[1]['height'], width=models[1]['width']), - gr.Textbox(label=f"Execution Time ({models[1]['name']})"), - gr.Image(label=f"Image from {models[2]['name']}", height=models[2]['height'], width=models[2]['width']), - gr.Textbox(label=f"Execution Time ({models[2]['name']})"), - ], - description="Enter a prompt and specify the number of inference steps to generate images using the model pipeline." -) -app = gr.mount_gradio_app(app,interface, path="/serve") ''' +with gr.Blocks() as interface: + gr.Markdown(f"# {model_id} Image Generation App") + gr.Markdown("Enter a prompt and specify the number of inference steps to generate images in different shapes.") + + with gr.Row(): + with gr.Column(scale=1): + prompt = gr.Textbox( + label="Prompt", + lines=1, + placeholder="Enter your prompt here...", + elem_id="prompt-box" + ) + inference_steps = gr.Number( + label="Inference Steps", + value=10, + precision=0, + info="Enter the number of inference steps; higher number takes more time but produces better image", + elem_id="steps-number" + ) + generate_button = gr.Button("Generate Images", variant="primary") + + with gr.Column(scale=2): + image_components = [] + exec_time_components = [] + + with gr.Row(): + for idx, model in enumerate(models): + with gr.Column(): + # Title + gr.Markdown(f"**{model['name']}**") + + # Scale down the image + preview_height = int(model['height'] / 2) + preview_width = int(model['width'] / 2) + + img = gr.Image( + label="", + height=preview_height, + width=preview_width, + interactive=False + ) + # Use Markdown for simpler smaller text + exec_time = gr.Markdown(value="") + + image_components.append(img) + exec_time_components.append(exec_time) + + # callback for the button + generate_button.click( + fn=call_model_api, + inputs=[prompt, inference_steps], + outputs=image_components + exec_time_components, + api_name="generate_images" + ) + +app = gr.mount_gradio_app(app, interface, path="/serve") + +app = gr.mount_gradio_app(app, interface, path="/serve") From 5c8dcca9a431214ff77facecd04d7838eeefa003 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 5 Jan 2025 17:14:38 -0800 Subject: [PATCH 25/45] sample-readme --- README.md | 1 + flux_serve/README.md | 15 ++- flux_serve/app/benchmark-flux.py | 2 +- .../specs/amd-neuron-inf2-nodepool.yaml | 51 ++++++++++ flux_serve/specs/compile-flux-1024x576.yaml | 2 +- flux_serve/specs/compile-flux-256x144.yaml | 64 +++++++++++++ flux_serve/specs/compile-flux-512x512.yaml | 64 +++++++++++++ .../specs/flux-neuron-1024x576-model-api.yaml | 93 +++++++++++++++++++ .../specs/flux-neuron-256x144-model-api.yaml | 93 +++++++++++++++++++ .../specs/flux-neuron-512x512-model-api.yaml | 93 +++++++++++++++++++ flux_serve/specs/flux-sa.yaml | 7 ++ flux_serve/specs/load.yaml | 31 +++++++ 12 files changed, 505 insertions(+), 11 deletions(-) create mode 100644 flux_serve/specs/amd-neuron-inf2-nodepool.yaml create mode 100644 flux_serve/specs/compile-flux-256x144.yaml create mode 100644 flux_serve/specs/compile-flux-512x512.yaml create mode 100644 flux_serve/specs/flux-neuron-1024x576-model-api.yaml create mode 100644 flux_serve/specs/flux-neuron-256x144-model-api.yaml create mode 100644 flux_serve/specs/flux-neuron-512x512-model-api.yaml create mode 100644 flux_serve/specs/flux-sa.yaml create mode 100644 flux_serve/specs/load.yaml diff --git a/README.md b/README.md index 8ded88a..3eb23d9 100644 --- a/README.md +++ b/README.md @@ -19,6 +19,7 @@ Samples are organized by use case below: | Link | Description | Instance Type | | --- | --- | --- | | [SD inference](sd_hf_serve) | SD Inference workflow for creating an inference endpoint forwarded by ALB LoadBalancer powered by Karpenter's NodePool | Inf2 | +| [Flux inference](flux_serve) | FLUX.1 dev Inference workflow for creating an inference endpoint forwarded by ALB LoadBalancer powered by Karpenter's NodePool and S3 mountpoints | Trn1/Inf2 | ## Getting Help diff --git a/flux_serve/README.md b/flux_serve/README.md index 398ec86..f7816a5 100644 --- a/flux_serve/README.md +++ b/flux_serve/README.md @@ -1,16 +1,14 @@ # Compile and serve ultra large vision transformers like Flux on Neuron devices at scale -challenges in deploying large models for serving: -1/large model graph traces load - caching S3 buckets on EKS nodes with CSI drivers -https://docs.aws.amazon.com/eks/latest/userguide/s3-csi.html +[FLUX.1 dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) is a 12 billion parameter rectified flow transformer capable of generating images from text. This example illustrates how to optimize its usage on Neuron devices that power EC2 Inferentia (Inf2) and Trainium (Trn1) instances deployed on EKS. Since the FLUX.1 dev model cannot fit on a single Neuron device, neuronx_distributed splits the model transformer into `N=8` chunks along a dimension. This ensures that each device holds only `1/N` chunk of the tensor. By performing partial chunk computation, the model generates partial outputs from all devices, maintaining accuracy and producing high-quality images. However, loading the traced model graph before serving significantly impacts the model loading time. This depends on the `tp_degree`, `N`, and the produced model graph traces `*.pt` need to be loaded during deployment and integrated into the OCI image, which can lead to prolonged load times. To address this, we cache the model traces (in the range of 10 GB) in [S3 Mountpoints](https://docs.aws.amazon.com/eks/latest/userguide/s3-csi.html), decoupling the OCI model load time from the model traces load time. -Using DLCs to simplify OCI image build - easy to streamline with automation +Next, we determine the compute type we intend to use. The initial step involves loading the model, executing a single inference, and assessing the image quality based on the number of inference steps. Subsequently, we determine the minimum inference latency with `Trn1` and `Inf2` under the specified acceptable quality parameters, currently set to `num_inference_steps`. Finally, we load each deployment unit, such as Flux on Trn1 8 Neuron devices or Flux on Inf2 6 Neuron devices, until it reaches its breaking point where the latency exceeds the acceptable thresholds. -https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/monitoring-tools.html#monitoring-tools are availible out of the box with container insights +The rest of this post offers a code sample walkthrough that explains the optimization strategies we adopted to streamline the deployment process using S3 CSI Driver and [Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md). These strategies aim to reduce the cost of inference while enhancing model serving throughput. + + +TBD - https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/monitoring-tools.html#monitoring-tools are availible out of the box with container insights -cost effectiveness requires to allocate the minimal required cores. The device plugin and myscheduler through Helm simplify deployment -https://awsdocs-neuron.readthedocs-hosted.com/en/latest/containers/kubernetes-getting-started.html -Neuron device plugin, Neuron scheduler extension, Neuron scheduler ## Walkthrough * [Create cluster with Karpenter node pools that provisions `trn1` instances](https://karpenter.sh/docs/getting-started/getting-started-with-karpenter/) @@ -57,7 +55,6 @@ kubectl apply -f specs/compile-flux-256x144.yaml kubectl apply -f specs/compile-flux-512x512.yaml ``` Note the three pending Jobs that Karpenter seeks to fulfill. Current setup requires `aws.amazon.com/neuron: 8` which is half od `trn1.32xlarge` so expect two `trn1.xlarge` to be launched. - * Deploy the Flux serving backend that loads the model from HuggingFace and uses the preloaded neuron model graph from S3 and standby for inference requests. The backend includes Deployment Pods and Services that route inference requests to the Pods so each model-api shapes scales horizontly. ```bash kubectl apply -f specs/flux-neuron-1024x576-model-api.yaml diff --git a/flux_serve/app/benchmark-flux.py b/flux_serve/app/benchmark-flux.py index 9dcda93..6be481f 100644 --- a/flux_serve/app/benchmark-flux.py +++ b/flux_serve/app/benchmark-flux.py @@ -22,7 +22,7 @@ guidance_scale=float(os.environ['GUIDANCE_SCALE']) prompt= "A cat holding a sign that says hello world" -num_inference_steps=20 +num_inference_steps=10 hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() try: diff --git a/flux_serve/specs/amd-neuron-inf2-nodepool.yaml b/flux_serve/specs/amd-neuron-inf2-nodepool.yaml new file mode 100644 index 0000000..840259f --- /dev/null +++ b/flux_serve/specs/amd-neuron-inf2-nodepool.yaml @@ -0,0 +1,51 @@ +apiVersion: karpenter.sh/v1 +kind: NodePool +metadata: + name: amd-neuron-inf2 +spec: + template: + spec: + requirements: + - key: kubernetes.io/arch + operator: In + values: ["amd64"] + - key: karpenter.k8s.aws/instance-family + operator: In + values: ["inf2"] + - key: karpenter.sh/capacity-type + operator: In + values: ["on-demand"] + nodeClassRef: + group: karpenter.k8s.aws + kind: EC2NodeClass + name: amd-neuron + expireAfter: 720h # 30 * 24h = 720h + limits: + cpu: 1000 + disruption: + consolidationPolicy: WhenEmptyOrUnderutilized + consolidateAfter: 10m +--- +apiVersion: karpenter.k8s.aws/v1 +kind: EC2NodeClass +metadata: + name: amd-neuron +spec: + amiFamily: AL2 # Amazon Linux 2 + amiSelectorTerms: + - id: "ami-0e73cfd34ee52b6a6" + - id: "ami-077b8064c2a1a3186" + - id: "ami-057dddc807f3f6644" + role: "KarpenterNodeRole-flux-usw2" + subnetSelectorTerms: + - tags: + karpenter.sh/discovery: "flux-usw2" + securityGroupSelectorTerms: + - tags: + karpenter.sh/discovery: "flux-usw2" + blockDeviceMappings: + - deviceName: /dev/xvda + ebs: + volumeSize: 900Gi + volumeType: gp3 + encrypted: true diff --git a/flux_serve/specs/compile-flux-1024x576.yaml b/flux_serve/specs/compile-flux-1024x576.yaml index 600cf59..1055b01 100644 --- a/flux_serve/specs/compile-flux-1024x576.yaml +++ b/flux_serve/specs/compile-flux-1024x576.yaml @@ -29,7 +29,7 @@ spec: ./compile.sh cp -r * /model/1024x576 python /benchmark-flux.py - #while true; do sleep 3600; done + while true; do sleep 3600; done resources: limits: aws.amazon.com/neuron: 8 diff --git a/flux_serve/specs/compile-flux-256x144.yaml b/flux_serve/specs/compile-flux-256x144.yaml new file mode 100644 index 0000000..aff6374 --- /dev/null +++ b/flux_serve/specs/compile-flux-256x144.yaml @@ -0,0 +1,64 @@ +apiVersion: batch/v1 +kind: Job +metadata: + name: compile-256x144 +spec: + template: + spec: + restartPolicy: OnFailure + nodeSelector: + karpenter.sh/nodepool: amd-neuron-trn1 + #serviceAccountName: appsimulator + schedulerName: my-scheduler + containers: + - name: app + image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + imagePullPolicy: Always + volumeMounts: + - name: dshm + mountPath: /dev/shm + - name: s3-flux-pvc + mountPath: /model + command: + - /bin/bash + - "-exc" + - | + set -x + mkdir -p /model/256x144 + cd /src + ./compile.sh + cp -r * /model/256x144 + python /benchmark-flux.py + while true; do sleep 3600; done + resources: + limits: + aws.amazon.com/neuron: 8 + requests: + aws.amazon.com/neuron: 8 + env: + - name: NODEPOOL + value: "amd-neuron-trn1" + - name: COMPILER_WORKDIR_ROOT + value: "/model/256x144" + - name: HEIGHT + value: "256" + - name: WIDTH + value: "144" + - name: MAX_SEQ_LEN + value: "32" + - name: GUIDANCE_SCALE + value: "3.5" + - name: MODEL_ID + value: "black-forest-labs/FLUX.1-dev" + - name: HUGGINGFACE_TOKEN + valueFrom: + secretKeyRef: + name: hf-secrets + key: HUGGINGFACE_TOKEN + volumes: + - name: dshm + emptyDir: + medium: Memory + - name: s3-flux-pvc + persistentVolumeClaim: + claimName: s3-flux-pvc diff --git a/flux_serve/specs/compile-flux-512x512.yaml b/flux_serve/specs/compile-flux-512x512.yaml new file mode 100644 index 0000000..29c8220 --- /dev/null +++ b/flux_serve/specs/compile-flux-512x512.yaml @@ -0,0 +1,64 @@ +apiVersion: batch/v1 +kind: Job +metadata: + name: compile-512x512 +spec: + template: + spec: + restartPolicy: OnFailure + nodeSelector: + karpenter.sh/nodepool: amd-neuron-trn1 + #serviceAccountName: appsimulator + schedulerName: my-scheduler + containers: + - name: app + image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + imagePullPolicy: Always + volumeMounts: + - name: dshm + mountPath: /dev/shm + - name: s3-flux-pvc + mountPath: /model + command: + - /bin/bash + - "-exc" + - | + set -x + #mkdir -p /model/512x512 + #cd /src + #./compile.sh + #cp -r * /model/512x512 + #python /benchmark-flux.py + while true; do sleep 3600; done + resources: + limits: + aws.amazon.com/neuron: 6 + requests: + aws.amazon.com/neuron: 6 + env: + - name: NODEPOOL + value: "amd-neuron-trn1" + - name: COMPILER_WORKDIR_ROOT + value: "/model/512x512" + - name: HEIGHT + value: "512" + - name: WIDTH + value: "512" + - name: MAX_SEQ_LEN + value: "32" + - name: GUIDANCE_SCALE + value: "3.5" + - name: MODEL_ID + value: "black-forest-labs/FLUX.1-dev" + - name: HUGGINGFACE_TOKEN + valueFrom: + secretKeyRef: + name: hf-secrets + key: HUGGINGFACE_TOKEN + volumes: + - name: dshm + emptyDir: + medium: Memory + - name: s3-flux-pvc + persistentVolumeClaim: + claimName: s3-flux-pvc diff --git a/flux_serve/specs/flux-neuron-1024x576-model-api.yaml b/flux_serve/specs/flux-neuron-1024x576-model-api.yaml new file mode 100644 index 0000000..9ae6c4b --- /dev/null +++ b/flux_serve/specs/flux-neuron-1024x576-model-api.yaml @@ -0,0 +1,93 @@ +apiVersion: v1 +kind: Service +metadata: + name: flux-neuron-1024x576-model-api +spec: + selector: + app: flux-neuron-1024x576-model-api + ports: + - protocol: TCP + port: 8000 + targetPort: 8000 + type: ClusterIP +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + labels: + app: flux-neuron-1024x576-model-api + name: flux-neuron-1024x576-model-api +spec: + selector: + matchLabels: + app: flux-neuron-1024x576-model-api + template: + metadata: + labels: + app: flux-neuron-1024x576-model-api + spec: + nodeSelector: + karpenter.sh/nodepool: amd-neuron-inf2 + serviceAccountName: flux-serviceaccount + schedulerName: my-scheduler + containers: + - name: app + image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + imagePullPolicy: Always + volumeMounts: + - name: dshm + mountPath: /dev/shm + - name: s3-flux-pvc + mountPath: /model + command: ["sh", "-c", "uvicorn flux_model_api:app --host=0.0.0.0"] + resources: + requests: + aws.amazon.com/neuron: 6 + limits: + aws.amazon.com/neuron: 6 + ports: + - containerPort: 8000 + protocol: TCP + readinessProbe: + httpGet: + path: /readiness + port: 8000 + initialDelaySeconds: 60 + periodSeconds: 10 + env: + - name: APP + value: "flux1.1-dev-1024x576-inf2" + - name: NODEPOOL + value: "amd-neuron-inf2" + - name: DEVICE + value: "xla" + - name: MODEL_ID + value: "black-forest-labs/FLUX.1-dev" + - name: COMPILER_WORKDIR_ROOT + value: "/model/1024x576" + - name: HEIGHT + value: "1024" + - name: WIDTH + value: "576" + - name: MAX_SEQ_LEN + value: "32" + - name: GUIDANCE_SCALE + value: "3.5" + - name: HUGGINGFACE_TOKEN + valueFrom: + secretKeyRef: + name: hf-secrets + key: HUGGINGFACE_TOKEN + - name: POD_NAME + valueFrom: + fieldRef: + fieldPath: metadata.name + volumes: + - name: workdir + emptyDir: {} + - name: dshm + emptyDir: + medium: Memory + - name: s3-flux-pvc + persistentVolumeClaim: + claimName: s3-flux-pvc diff --git a/flux_serve/specs/flux-neuron-256x144-model-api.yaml b/flux_serve/specs/flux-neuron-256x144-model-api.yaml new file mode 100644 index 0000000..1f80515 --- /dev/null +++ b/flux_serve/specs/flux-neuron-256x144-model-api.yaml @@ -0,0 +1,93 @@ +apiVersion: v1 +kind: Service +metadata: + name: flux-neuron-256x144-model-api +spec: + selector: + app: flux-neuron-256x144-model-api + ports: + - protocol: TCP + port: 8000 + targetPort: 8000 + type: ClusterIP +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + labels: + app: flux-neuron-256x144-model-api + name: flux-neuron-256x144-model-api +spec: + selector: + matchLabels: + app: flux-neuron-256x144-model-api + template: + metadata: + labels: + app: flux-neuron-256x144-model-api + spec: + nodeSelector: + karpenter.sh/nodepool: amd-neuron-inf2 + serviceAccountName: flux-serviceaccount + schedulerName: my-scheduler + containers: + - name: app + image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + imagePullPolicy: Always + volumeMounts: + - name: dshm + mountPath: /dev/shm + - name: s3-flux-pvc + mountPath: /model + command: ["sh", "-c", "uvicorn flux_model_api:app --host=0.0.0.0"] + resources: + requests: + aws.amazon.com/neuron: 6 + limits: + aws.amazon.com/neuron: 6 + ports: + - containerPort: 8000 + protocol: TCP + readinessProbe: + httpGet: + path: /readiness + port: 8000 + initialDelaySeconds: 60 + periodSeconds: 10 + env: + - name: APP + value: "flux1.1-dev-256x144-inf2" + - name: NODEPOOL + value: "amd-neuron-inf2" + - name: DEVICE + value: "xla" + - name: MODEL_ID + value: "black-forest-labs/FLUX.1-dev" + - name: COMPILER_WORKDIR_ROOT + value: "/model/256x144" + - name: HEIGHT + value: "256" + - name: WIDTH + value: "144" + - name: MAX_SEQ_LEN + value: "32" + - name: GUIDANCE_SCALE + value: "3.5" + - name: HUGGINGFACE_TOKEN + valueFrom: + secretKeyRef: + name: hf-secrets + key: HUGGINGFACE_TOKEN + - name: POD_NAME + valueFrom: + fieldRef: + fieldPath: metadata.name + volumes: + - name: workdir + emptyDir: {} + - name: dshm + emptyDir: + medium: Memory + - name: s3-flux-pvc + persistentVolumeClaim: + claimName: s3-flux-pvc diff --git a/flux_serve/specs/flux-neuron-512x512-model-api.yaml b/flux_serve/specs/flux-neuron-512x512-model-api.yaml new file mode 100644 index 0000000..ed2935e --- /dev/null +++ b/flux_serve/specs/flux-neuron-512x512-model-api.yaml @@ -0,0 +1,93 @@ +apiVersion: v1 +kind: Service +metadata: + name: flux-neuron-512x512-model-api +spec: + selector: + app: flux-neuron-512x512-model-api + ports: + - protocol: TCP + port: 8000 + targetPort: 8000 + type: ClusterIP +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + labels: + app: flux-neuron-512x512-model-api + name: flux-neuron-512x512-model-api +spec: + selector: + matchLabels: + app: flux-neuron-512x512-model-api + template: + metadata: + labels: + app: flux-neuron-512x512-model-api + spec: + nodeSelector: + karpenter.sh/nodepool: amd-neuron-inf2 + serviceAccountName: flux-serviceaccount + schedulerName: my-scheduler + containers: + - name: app + image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + imagePullPolicy: Always + volumeMounts: + - name: dshm + mountPath: /dev/shm + - name: s3-flux-pvc + mountPath: /model + command: ["sh", "-c", "uvicorn flux_model_api:app --host=0.0.0.0"] + resources: + requests: + aws.amazon.com/neuron: 6 + limits: + aws.amazon.com/neuron: 6 + ports: + - containerPort: 8000 + protocol: TCP + readinessProbe: + httpGet: + path: /readiness + port: 8000 + initialDelaySeconds: 60 + periodSeconds: 10 + env: + - name: APP + value: "flux1.1-dev-512x512-inf2" + - name: NODEPOOL + value: "amd-neuron-inf2" + - name: DEVICE + value: "xla" + - name: MODEL_ID + value: "black-forest-labs/FLUX.1-dev" + - name: COMPILER_WORKDIR_ROOT + value: "/model/512x512" + - name: HEIGHT + value: "512" + - name: WIDTH + value: "512" + - name: MAX_SEQ_LEN + value: "32" + - name: GUIDANCE_SCALE + value: "3.5" + - name: HUGGINGFACE_TOKEN + valueFrom: + secretKeyRef: + name: hf-secrets + key: HUGGINGFACE_TOKEN + - name: POD_NAME + valueFrom: + fieldRef: + fieldPath: metadata.name + volumes: + - name: workdir + emptyDir: {} + - name: dshm + emptyDir: + medium: Memory + - name: s3-flux-pvc + persistentVolumeClaim: + claimName: s3-flux-pvc diff --git a/flux_serve/specs/flux-sa.yaml b/flux_serve/specs/flux-sa.yaml new file mode 100644 index 0000000..9f711b9 --- /dev/null +++ b/flux_serve/specs/flux-sa.yaml @@ -0,0 +1,7 @@ +apiVersion: v1 +kind: ServiceAccount +metadata: + name: flux-serviceaccount + namespace: default + annotations: + eks.amazonaws.com/role-arn: arn:aws:iam::920372998901:role/KarpenterNodeRole-flux-usw2 diff --git a/flux_serve/specs/load.yaml b/flux_serve/specs/load.yaml new file mode 100644 index 0000000..ae8463b --- /dev/null +++ b/flux_serve/specs/load.yaml @@ -0,0 +1,31 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + labels: + app: load + name: load +spec: + selector: + matchLabels: + app: load + template: + metadata: + labels: + app: load + spec: + nodeSelector: + alpha.eksctl.io/nodegroup-name: flux-usw2-ng + containers: + - name: load + image: public.ecr.aws/docker/library/python + imagePullPolicy: Always + command: + - /bin/bash + - -c + - -x + - | + SLEEP_TIME=10 + while true; do + curl -X POST -H "Content-Type: application/json" -d '{"prompt": "A majestic mountainscape in a surreal style","num_inference_steps": 10}' http://$FLUX_NEURON_256X144_MODEL_API_SERVICE_HOST:8000/generate + sleep $SLEEP_TIME + done From eb41df8f32e0d3a076161096222634777d194f35 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 5 Jan 2025 17:55:46 -0800 Subject: [PATCH 26/45] README --- flux_serve/README.md | 24 +++++++++++++++++++++-- flux_serve/figures/flux-quality-test.png | Bin 0 -> 738241 bytes 2 files changed, 22 insertions(+), 2 deletions(-) create mode 100644 flux_serve/figures/flux-quality-test.png diff --git a/flux_serve/README.md b/flux_serve/README.md index f7816a5..4a8a6ce 100644 --- a/flux_serve/README.md +++ b/flux_serve/README.md @@ -54,14 +54,14 @@ kubectl apply -f specs/compile-flux-1024x576.yaml kubectl apply -f specs/compile-flux-256x144.yaml kubectl apply -f specs/compile-flux-512x512.yaml ``` -Note the three pending Jobs that Karpenter seeks to fulfill. Current setup requires `aws.amazon.com/neuron: 8` which is half od `trn1.32xlarge` so expect two `trn1.xlarge` to be launched. +Note the three pending Jobs that Karpenter seeks to fulfill. Current setup requires `aws.amazon.com/neuron: 8` which is half of `trn1.32xlarge` so expect two `trn1.xlarge` to be launched. * Deploy the Flux serving backend that loads the model from HuggingFace and uses the preloaded neuron model graph from S3 and standby for inference requests. The backend includes Deployment Pods and Services that route inference requests to the Pods so each model-api shapes scales horizontly. ```bash kubectl apply -f specs/flux-neuron-1024x576-model-api.yaml kubectl apply -f specs/flux-neuron-256x144-model-api.yaml kubectl apply -f specs/flux-neuron-512x512-model-api.yaml ``` -Note the three pending Deployment Pods that Karpenter seeks to fulfill. Current setup requires `aws.amazon.com/neuron: 8` which is half od `trn1.32xlarge` so expect two `trn1.xlarge` to be launched. +Note the three pending Deployment Pods that Karpenter seeks to fulfill. Current setup requires either `aws.amazon.com/neuron: 8` which is half od `trn1.32xlarge` or `aws.amazon.com/neuron: 6` which is a full `inf2.24xlarge` instance. * Deploy the Flux serving frontend that includes Gradio app. ```bash @@ -76,4 +76,24 @@ flux-neuron alb * flux-neuron-658286526.us-west-2.elb.amazonaws.com ``` Use [flux-neuron-658286526.us-west-2.elb.amazonaws.com/serve/](flux-neuron-658286526.us-west-2.elb.amazonaws.com/serve/) +![Figure 1-Quality tests](./figures/flux-quality-test.png) +*Figure 1-Quality tests after deploying the model on Inf2 instances (Similar results were shown on Trn1 instances)* +We benchmarked the Flux serving latency on Trn1 and Inf2 by configuring the `nodeSelector` with the [Trn1 Karpenter nodepool](./specs/amd-neuron-trn1-nodepool.yaml) and the [Inf2 Karpenter nodepool](./specs/amd-neuron-inf2-nodepool.yaml) and launched the benchmark script [./app/benchmark-flux.py](./app/benchmark-flux.py) that is already executed post [compile job](./specs/compile-flux-256x144.yaml). Below are the results pulled by `kubectl logs ...`: +``` +RESULT FOR flux1-dev-50runs with dim 1024x576 on amd-neuron-trn1;num_inference_steps:10: Latency P0=3118.2 Latency P50=3129.3 Latency P90=3141.3 Latency P95=3147.2 Latency P99=3162.2 Latency P100=3162.2 + +RESULT FOR flux1-dev-50runs with dim 256x144 on amd-neuron-trn1;num_inference_steps:10: Latency P0=585.6 Latency P50=588.1 Latency P90=592.2 Latency P95=592.5 Latency P99=597.8 Latency P100=597.8 + +RESULT FOR flux1-dev-50runs with dim 1024x576 on amd-neuron-inf2;num_inference_steps:10: Latency P0=9040.2 Latency P50=9080.8 Latency P90=9115.6 Latency P95=9120.6 Latency P99=9123.7 Latency P100=9123.7 + +RESULT FOR flux1-dev-50runs with dim 256x144 on amd-neuron-inf2;num_inference_steps:10: Latency P0=3067.9 Latency P50=3075.7 Latency P90=3079.8 Latency P95=3081.2 Latency P99=3088.5 Latency P100=3088.5 +``` + +| **Dimension** | **Platform** | **Latency P0** | **Latency P50** | **Latency P90** | **Latency P95** | **Latency P99** | **Latency P100** | +|---------------|-------------:|---------------:|---------------:|---------------:|---------------:|---------------:|----------------:| +| 1024x576 | Trn1 | 3118.2 | 3129.3 | 3141.3 | 3147.2 | 3162.2 | 3162.2 | +| 256x144 | Trn1 | 585.6 | 588.1 | 592.2 | 592.5 | 597.8 | 597.8 | +| 1024x576 | Inf2 | 9040.2 | 9080.8 | 9115.6 | 9120.6 | 9123.7 | 9123.7 | +| 256x144 | Inf2 | 3067.9 | 3075.7 | 3079.8 | 3081.2 | 3088.5 | 3088.5 | +*Table 1-Latency benchmark between 8 Trn1 NDs and 6 Inf2 NDs with 10 inference_steps for 50 iterations* diff --git a/flux_serve/figures/flux-quality-test.png b/flux_serve/figures/flux-quality-test.png new file mode 100644 index 0000000000000000000000000000000000000000..a40ce49eadd2a1b887075c63e16063507e75113b GIT binary patch literal 738241 zcma%i1y~hZ+cw}PHmD$}k|N#Rjg)kEw=|nhDUk*#=|;LcrKG#NySw2Z&Ux$q&g=Ib zt_$`sd)CaXwVw6FecvlUMoQ!b;#)*0D5w`=q95d-pb$`@px`hO;D9se60donpq@*Z 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@@ RESULT FOR flux1-dev-50runs with dim 256x144 on amd-neuron-inf2;num_inference_st | 256x144 | Trn1 | 585.6 | 588.1 | 592.2 | 592.5 | 597.8 | 597.8 | | 1024x576 | Inf2 | 9040.2 | 9080.8 | 9115.6 | 9120.6 | 9123.7 | 9123.7 | | 256x144 | Inf2 | 3067.9 | 3075.7 | 3079.8 | 3081.2 | 3088.5 | 3088.5 | + *Table 1-Latency benchmark between 8 Trn1 NDs and 6 Inf2 NDs with 10 inference_steps for 50 iterations* From ef1e1d8baf293545c625d1e22caad87a219db55e Mon Sep 17 00:00:00 2001 From: Yahav Date: Mon, 6 Jan 2025 20:34:23 -0800 Subject: [PATCH 28/45] benchmark --- flux_serve/README.md | 6 ++---- flux_serve/specs/load.yaml | 2 +- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/flux_serve/README.md b/flux_serve/README.md index 39bb351..82fec30 100644 --- a/flux_serve/README.md +++ b/flux_serve/README.md @@ -2,16 +2,14 @@ [FLUX.1 dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) is a 12 billion parameter rectified flow transformer capable of generating images from text. This example illustrates how to optimize its usage on Neuron devices that power EC2 Inferentia (Inf2) and Trainium (Trn1) instances deployed on EKS. Since the FLUX.1 dev model cannot fit on a single Neuron device, neuronx_distributed splits the model transformer into `N=8` chunks along a dimension. This ensures that each device holds only `1/N` chunk of the tensor. By performing partial chunk computation, the model generates partial outputs from all devices, maintaining accuracy and producing high-quality images. However, loading the traced model graph before serving significantly impacts the model loading time. This depends on the `tp_degree`, `N`, and the produced model graph traces `*.pt` need to be loaded during deployment and integrated into the OCI image, which can lead to prolonged load times. To address this, we cache the model traces (in the range of 10 GB) in [S3 Mountpoints](https://docs.aws.amazon.com/eks/latest/userguide/s3-csi.html), decoupling the OCI model load time from the model traces load time. -Next, we determine the compute type we intend to use. The initial step involves loading the model, executing a single inference, and assessing the image quality based on the number of inference steps. Subsequently, we determine the minimum inference latency with `Trn1` and `Inf2` under the specified acceptable quality parameters, currently set to `num_inference_steps`. Finally, we load each deployment unit, such as Flux on Trn1 8 Neuron devices or Flux on Inf2 6 Neuron devices, until it reaches its breaking point where the latency exceeds the acceptable thresholds. +Next, we determine the compute type we intend to use. The initial step involves loading the model, executing a single inference, and assessing the image quality based on the number of inference steps. Subsequently, we determine the minimum inference latency with `Trn1` and `Inf2` under the specified acceptable quality parameters, currently set to `num_inference_steps`. Finally, we load each deployment unit, such as Flux on Trn1 8 Neuron devices or Flux on Inf2 6 Neuron devices, until it reaches its breaking point where the latency exceeds the acceptable thresholds. We measure the Neuron cores utilization using the built-in [Neuron monitoring tools](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/monitoring-tools.html#monitoring-tools) that are included in Container Insights on Amazon EKS. The rest of this post offers a code sample walkthrough that explains the optimization strategies we adopted to streamline the deployment process using S3 CSI Driver and [Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md). These strategies aim to reduce the cost of inference while enhancing model serving throughput. -TBD - https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/monitoring-tools.html#monitoring-tools are availible out of the box with container insights - - ## Walkthrough * [Create cluster with Karpenter node pools that provisions `trn1` instances](https://karpenter.sh/docs/getting-started/getting-started-with-karpenter/) +* [Setup Container Insights on Amazon EKS and Kubernetes](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/Container-Insights-setup-EKS-quickstart.html) * [Deploy your HggingFace user secret as a k8s secret](https://kubernetes.io/docs/concepts/configuration/secret/) ```bash echo -n 'hf_myhftoken' | base64 diff --git a/flux_serve/specs/load.yaml b/flux_serve/specs/load.yaml index ae8463b..d845383 100644 --- a/flux_serve/specs/load.yaml +++ b/flux_serve/specs/load.yaml @@ -24,7 +24,7 @@ spec: - -c - -x - | - SLEEP_TIME=10 + SLEEP_TIME=2 while true; do curl -X POST -H "Content-Type: application/json" -d '{"prompt": "A majestic mountainscape in a surreal style","num_inference_steps": 10}' http://$FLUX_NEURON_256X144_MODEL_API_SERVICE_HOST:8000/generate sleep $SLEEP_TIME From 43296e1e282fc38cd41417b3ca1fcf6d9625776b Mon Sep 17 00:00:00 2001 From: Yahav Date: Mon, 6 Jan 2025 20:35:56 -0800 Subject: [PATCH 29/45] benchmark --- flux_serve/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flux_serve/README.md b/flux_serve/README.md index 82fec30..8b33e86 100644 --- a/flux_serve/README.md +++ b/flux_serve/README.md @@ -2,7 +2,7 @@ [FLUX.1 dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) is a 12 billion parameter rectified flow transformer capable of generating images from text. This example illustrates how to optimize its usage on Neuron devices that power EC2 Inferentia (Inf2) and Trainium (Trn1) instances deployed on EKS. Since the FLUX.1 dev model cannot fit on a single Neuron device, neuronx_distributed splits the model transformer into `N=8` chunks along a dimension. This ensures that each device holds only `1/N` chunk of the tensor. By performing partial chunk computation, the model generates partial outputs from all devices, maintaining accuracy and producing high-quality images. However, loading the traced model graph before serving significantly impacts the model loading time. This depends on the `tp_degree`, `N`, and the produced model graph traces `*.pt` need to be loaded during deployment and integrated into the OCI image, which can lead to prolonged load times. To address this, we cache the model traces (in the range of 10 GB) in [S3 Mountpoints](https://docs.aws.amazon.com/eks/latest/userguide/s3-csi.html), decoupling the OCI model load time from the model traces load time. -Next, we determine the compute type we intend to use. The initial step involves loading the model, executing a single inference, and assessing the image quality based on the number of inference steps. Subsequently, we determine the minimum inference latency with `Trn1` and `Inf2` under the specified acceptable quality parameters, currently set to `num_inference_steps`. Finally, we load each deployment unit, such as Flux on Trn1 8 Neuron devices or Flux on Inf2 6 Neuron devices, until it reaches its breaking point where the latency exceeds the acceptable thresholds. We measure the Neuron cores utilization using the built-in [Neuron monitoring tools](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/monitoring-tools.html#monitoring-tools) that are included in Container Insights on Amazon EKS. +Next, we determine the compute type we intend to use. The initial step involves loading the model, executing a single inference, and assessing the image quality based on the number of inference steps. Subsequently, we determine the minimum inference latency with `Trn1` and `Inf2` under the specified acceptable quality parameters, currently set to `num_inference_steps`. Finally, we load each deployment unit, such as Flux on Trn1 8 Neuron devices or Flux on Inf2 6 Neuron devices, until it reaches its breaking point where the latency exceeds the acceptable thresholds. We measure the Neuron core utilization using the built-in [Neuron monitoring tools](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/monitoring-tools.html#monitoring-tools) that are included in Container Insights on Amazon EKS. The rest of this post offers a code sample walkthrough that explains the optimization strategies we adopted to streamline the deployment process using S3 CSI Driver and [Deep Learning Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md). These strategies aim to reduce the cost of inference while enhancing model serving throughput. From 6017ed5ef613a92aeb8c347a311f995e731d7f77 Mon Sep 17 00:00:00 2001 From: Yahav Date: Fri, 25 Apr 2025 17:38:00 -0700 Subject: [PATCH 30/45] mllma --- flux_serve/app/mllama-offline.py | 113 +++++++++++++++++++++++++++ flux_serve/oci-image-build/README.md | 2 +- 2 files changed, 114 insertions(+), 1 deletion(-) create mode 100644 flux_serve/app/mllama-offline.py diff --git a/flux_serve/app/mllama-offline.py b/flux_serve/app/mllama-offline.py new file mode 100644 index 0000000..6f657b5 --- /dev/null +++ b/flux_serve/app/mllama-offline.py @@ -0,0 +1,113 @@ +import math +import time +import torch +import os +import sys +import yaml +import requests +from PIL import Image +from vllm import LLM, SamplingParams, TextPrompt +from neuronx_distributed_inference.models.mllama.utils import add_instruct +from huggingface_hub import create_repo,upload_folder,login,snapshot_download + +hf_token = os.environ['HUGGINGFACE_TOKEN'].strip() +repo_id=os.environ['MODEL_ID'] +os.environ['NEURON_COMPILED_ARTIFACTS']=repo_id +os.environ['VLLM_NEURON_FRAMEWORK']='neuronx-distributed-inference' +login(hf_token,add_to_git_credential=True) + +config_path = "/vllm_config.yaml" +with open(config_path, 'r') as f: + model_vllm_config_yaml = f.read() + +model_vllm_config = yaml.safe_load(model_vllm_config_yaml) + +class LatencyCollector: + def __init__(self): + self.latency_list = [] + + def record(self, latency_sec): + self.latency_list.append(latency_sec) + + def percentile(self, percent): + if not self.latency_list: + return 0.0 + latency_list = sorted(self.latency_list) + pos_float = len(latency_list) * percent / 100 + max_pos = len(latency_list) - 1 + pos_floor = min(math.floor(pos_float), max_pos) + pos_ceil = min(math.ceil(pos_float), max_pos) + return latency_list[pos_ceil] if pos_float - pos_floor > 0.5 else latency_list[pos_floor] + + def report(self, test_name="Batch Inference"): + print(f"\nšŸ“Š LATENCY REPORT for {test_name}") + for p in [0, 50, 90, 95, 99, 100]: + value = self.percentile(p) * 1000 + print(f"Latency P{p}: {value:.2f} ms") + + +def get_image(image_url): + image = Image.open(requests.get(image_url, stream=True).raw) + return image + +# Model Inputs +PROMPTS = ["What is in this image? Tell me a story", + "What is the recipe of mayonnaise in two sentences?" , + "Describe this image", + "What is the capital of Italy famous for?", + ] +IMAGES = [get_image("https://github.com/meta-llama/llama-models/blob/main/models/resources/dog.jpg?raw=true"), + torch.empty((0,0)), + get_image("https://awsdocs-neuron.readthedocs-hosted.com/en/latest/_images/nxd-inference-block-diagram.jpg"), + torch.empty((0,0)), + ] +SAMPLING_PARAMS = [dict(top_k=1, temperature=1.0, top_p=1.0, max_tokens=256), + dict(top_k=1, temperature=0.9, top_p=1.0, max_tokens=256), + dict(top_k=10, temperature=0.9, top_p=0.5, max_tokens=512), + dict(top_k=10, temperature=0.75, top_p=0.5, max_tokens=1024), + ] + + +def get_VLLM_mllama_model_inputs(prompt, single_image, sampling_params): + input_image = single_image + has_image = torch.tensor([1]) + if isinstance(single_image, torch.Tensor) and single_image.numel() == 0: + has_image = torch.tensor([0]) + + instruct_prompt = add_instruct(prompt, has_image) + inputs = TextPrompt(prompt=instruct_prompt) + inputs["multi_modal_data"] = {"image": input_image} + # Create a sampling params object. + sampling_params = SamplingParams(**sampling_params) + return inputs, sampling_params + +def print_outputs(outputs): + # Print the outputs. + for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") + + +llm_model = LLM(**model_vllm_config) +latency_collector = LatencyCollector() + +assert len(PROMPTS) == len(IMAGES) == len(SAMPLING_PARAMS), \ +f"""Text, image prompts and sampling parameters should have the same batch size, + got {len(PROMPTS)}, {len(IMAGES)}, and {len(SAMPLING_PARAMS)}""" + +batched_inputs = [] +batched_sample_params = [] +for i in range(1,21): + for pmpt, img, params in zip(PROMPTS, IMAGES, SAMPLING_PARAMS): + inputs, sampling_params = get_VLLM_mllama_model_inputs(pmpt, img, params) + # test batch-size = 1 + start_time = time.time() + outputs = llm_model.generate(inputs, sampling_params) + latency_sec = time.time() - start_time + latency_collector.record(latency_sec) + print_outputs(outputs) + batched_inputs.append(inputs) + batched_sample_params.append(sampling_params) + +latency_collector.report("MLLAMA") diff --git a/flux_serve/oci-image-build/README.md b/flux_serve/oci-image-build/README.md index 4052d04..a6f87cf 100644 --- a/flux_serve/oci-image-build/README.md +++ b/flux_serve/oci-image-build/README.md @@ -5,7 +5,7 @@ ```bash export AWS_ACCOUNT_ID=$(aws sts get-caller-identity --output text --query Account) export AWS_REGION=us-west-2 -export BASE_IMAGE_AMD_XLA_TAG=2.5.1-neuronx-py310-sdk2.21.0-ubuntu22.04 +export BASE_IMAGE_AMD_XLA_TAG=2.5.1-neuronx-py310-sdk2.22.0-ubuntu22.04 export IMAGE_AMD_XLA_TAG=amd64-neuron export BASE_REPO=model export BASE_TAG=multiarch-ubuntu From 93ef8f513ab942bfd82b1ad5aa902812eaf0a2a1 Mon Sep 17 00:00:00 2001 From: Yahav Date: Fri, 25 Apr 2025 18:17:33 -0700 Subject: [PATCH 31/45] vllm_model_api.py --- flux_serve/app/vllm_model_api.py | 172 +++++++++++++++++++++++++++++++ 1 file changed, 172 insertions(+) create mode 100644 flux_serve/app/vllm_model_api.py diff --git a/flux_serve/app/vllm_model_api.py b/flux_serve/app/vllm_model_api.py new file mode 100644 index 0000000..4a42087 --- /dev/null +++ b/flux_serve/app/vllm_model_api.py @@ -0,0 +1,172 @@ +import traceback +import math +import boto3 +import time +import argparse +import torch +import torch.nn as nn +#import torch_neuronx +#import neuronx_distributed +import os +from fastapi import FastAPI, HTTPException +from pydantic import BaseModel, Field +from typing import Any, Dict, Optional, Union +from huggingface_hub import login +from starlette.responses import StreamingResponse +import base64 +from vllm import LLM, SamplingParams +from sentence_transformers import SentenceTransformer +import yaml + +cw_namespace='hw-agnostic-infer' +default_max_new_tokens=50 +cloudwatch = boto3.client('cloudwatch', region_name='us-west-2') +sampling_params = SamplingParams(temperature=0.7,top_k=50,top_p=0.9,max_tokens=128,) + +app_name=os.environ['APP'] +nodepool=os.environ['NODEPOOL'] +pod_name = os.environ['POD_NAME'] +hf_token = os.environ['HUGGINGFACE_TOKEN'].strip() + +with open("/vllm_config.yaml", "r") as file: + vllm_config=yaml.safe_load(file) + +login(hf_token, add_to_git_credential=True) + +def gentext(prompt,max_new_tokens): + start_time = time.time() + outputs = model.generate(prompt,sampling_params) + response = outputs[0].outputs[0].text + total_time = time.time()-start_time + return str(response), float(total_time) + +def cw_pub_metric(metric_name,metric_value,metric_unit): + response = cloudwatch.put_metric_data( + Namespace=cw_namespace, + MetricData=[ + { + 'MetricName':metric_name, + 'Value':metric_value, + 'Unit':metric_unit, + }, + ] + ) + print(f"in pub_deployment_counter - response:{response}") + return response + +login(hf_token, add_to_git_credential=True) + +# TBD change to text from image +def benchmark(n_runs, test_name,model,prompt,max_new_tokens): + warmup_run = model.generate(prompt,sampling_params) + latency_collector = LatencyCollector() + + for _ in range(n_runs): + latency_collector.pre_hook() + res = model.generate(prompt,sampling_params) + latency_collector.hook() + + p0_latency_ms = latency_collector.percentile(0) * 1000 + p50_latency_ms = latency_collector.percentile(50) * 1000 + p90_latency_ms = latency_collector.percentile(90) * 1000 + p95_latency_ms = latency_collector.percentile(95) * 1000 + p99_latency_ms = latency_collector.percentile(99) * 1000 + p100_latency_ms = latency_collector.percentile(100) * 1000 + + report_dict = dict() + report_dict["Latency P0"] = f'{p0_latency_ms:.1f}' + report_dict["Latency P50"]=f'{p50_latency_ms:.1f}' + report_dict["Latency P90"]=f'{p90_latency_ms:.1f}' + report_dict["Latency P95"]=f'{p95_latency_ms:.1f}' + report_dict["Latency P99"]=f'{p99_latency_ms:.1f}' + report_dict["Latency P100"]=f'{p100_latency_ms:.1f}' + + report = f'RESULT FOR {test_name}:' + for key, value in report_dict.items(): + report += f' {key}={value}' + print(report) + return report + +class LatencyCollector: + def __init__(self): + self.start = None + self.latency_list = [] + + def pre_hook(self, *args): + self.start = time.time() + + def hook(self, *args): + self.latency_list.append(time.time() - self.start) + + def percentile(self, percent): + latency_list = self.latency_list + pos_float = len(latency_list) * percent / 100 + max_pos = len(latency_list) - 1 + pos_floor = min(math.floor(pos_float), max_pos) + pos_ceil = min(math.ceil(pos_float), max_pos) + latency_list = sorted(latency_list) + return latency_list[pos_ceil] if pos_float - pos_floor > 0.5 else latency_list[pos_floor] + +class GenerateRequest(BaseModel): + max_new_tokens: int + prompt: str + +class GenerateBenchmarkRequest(BaseModel): + n_runs: int + max_new_tokens: int + prompt: str + +class GenerateResponse(BaseModel): + text: str = Field(..., description="Base64-encoded text") + execution_time: float + +class GenerateBenchmarkResponse(BaseModel): + report: str = Field(..., description="Benchmark report") + +def load_model(): + model = LLM(**vllm_config) + return model + +model = load_model() +prompt= "What model are you?" +benchmark(10,"warmup",model,prompt,default_max_new_tokens) +app = FastAPI() + +@app.post("/benchmark",response_model=GenerateBenchmarkResponse) +def generate_benchmark_report(request: GenerateBenchmarkRequest): + print(f'DEBUG: GenerateBenchmarkRequest:{request}') + try: + with torch.no_grad(): + test_name=f'benchmark:{app_name} on {nodepool} with {request.max_new_tokens} output tokens' + response_report=benchmark(request.n_runs,test_name,model,request.prompt,request.max_new_tokens) + report_base64 = base64.b64encode(response_report.encode()).decode() + return GenerateBenchmarkResponse(report=report_base64) + except Exception as e: + traceback.print_exc() + raise HTTPException(status_code=500, detail=f"{e}") + +@app.post("/generate", response_model=GenerateResponse) +def generate_text_post(request: GenerateRequest): + try: + with torch.no_grad(): + response_text,total_time=gentext(request.prompt,request.max_new_tokens) + counter_metric=app_name+'-counter' + cw_pub_metric(counter_metric,1,'Count') + counter_metric=nodepool + cw_pub_metric(counter_metric,1,'Count') + latency_metric=app_name+'-latency' + cw_pub_metric(latency_metric,total_time,'Seconds') + text_base64 = base64.b64encode(response_text.encode()).decode() + return GenerateResponse(text=text_base64, execution_time=total_time) + except Exception as e: + traceback.print_exc() + raise HTTPException(status_code=500, detail=f"text serialization failed: {e}") + +# Health and readiness endpoints +@app.get("/health") +def healthy(): + return {"message": f"{pod_name} is healthy"} + +@app.get("/readiness") +def ready(): + return {"message": f"{pod_name} is ready"} From b57bf4b75205e578c4664219a7c63e324cc6004e Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 27 Apr 2025 14:19:59 -0700 Subject: [PATCH 32/45] ../app/flux_model_api.py --- flux_serve/app/flux_model_api.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flux_serve/app/flux_model_api.py b/flux_serve/app/flux_model_api.py index 2bf6d3f..a3ac2e6 100644 --- a/flux_serve/app/flux_model_api.py +++ b/flux_serve/app/flux_model_api.py @@ -297,7 +297,7 @@ def load_model(): num_inference_steps=10 model_inputs={'prompt':prompt,'height':height,'width':width,'max_sequence_length':max_sequence_length,'num_inference_steps': num_inference_steps,'guidance_scale':guidance_scale} test_name=f"flux1-dev-50runs with dim {height}x{width} on {nodepool};num_inference_steps:{num_inference_steps}" -benchmark(50,test_name,model,model_inputs) +benchmark(10,test_name,model,model_inputs) # Define the image generation endpoint @app.post("/generate", response_model=GenerateImageResponse) From fead2dcc9663f27bac32b2610a8561be843b6fcb Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 27 Apr 2025 18:40:39 -0700 Subject: [PATCH 33/45] download_hf_model.py --- flux_serve/app/download_hf_model.py | 9 +++++++++ flux_serve/specs/amd-neuron-inf2-nodepool.yaml | 15 ++++++--------- flux_serve/specs/amd-neuron-trn1-nodepool.yaml | 15 ++++++--------- 3 files changed, 21 insertions(+), 18 deletions(-) create mode 100644 flux_serve/app/download_hf_model.py diff --git a/flux_serve/app/download_hf_model.py b/flux_serve/app/download_hf_model.py new file mode 100644 index 0000000..fde2467 --- /dev/null +++ b/flux_serve/app/download_hf_model.py @@ -0,0 +1,9 @@ +from huggingface_hub import login,snapshot_download +import os +hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() +repo_id=os.environ['COMPILED_MODEL_ID'] +repo_dir=os.environ['NEURON_COMPILED_ARTIFACTS'] +login(hf_token, add_to_git_credential=True) +snapshot_download(repo_id=repo_id,local_dir=repo_dir,token=hf_token) +print(f"Repository '{repo_id}' downloaded to '{repo_dir}'.") + diff --git a/flux_serve/specs/amd-neuron-inf2-nodepool.yaml b/flux_serve/specs/amd-neuron-inf2-nodepool.yaml index 840259f..151b67d 100644 --- a/flux_serve/specs/amd-neuron-inf2-nodepool.yaml +++ b/flux_serve/specs/amd-neuron-inf2-nodepool.yaml @@ -18,7 +18,7 @@ spec: nodeClassRef: group: karpenter.k8s.aws kind: EC2NodeClass - name: amd-neuron + name: amd-neuron-al2023 expireAfter: 720h # 30 * 24h = 720h limits: cpu: 1000 @@ -29,20 +29,17 @@ spec: apiVersion: karpenter.k8s.aws/v1 kind: EC2NodeClass metadata: - name: amd-neuron + name: amd-neuron-al2023 spec: - amiFamily: AL2 # Amazon Linux 2 amiSelectorTerms: - - id: "ami-0e73cfd34ee52b6a6" - - id: "ami-077b8064c2a1a3186" - - id: "ami-057dddc807f3f6644" - role: "KarpenterNodeRole-flux-usw2" + - alias: "al2023@v20250419" + role: "KarpenterNodeRole-cova" subnetSelectorTerms: - tags: - karpenter.sh/discovery: "flux-usw2" + karpenter.sh/discovery: "cova" securityGroupSelectorTerms: - tags: - karpenter.sh/discovery: "flux-usw2" + karpenter.sh/discovery: "cova" blockDeviceMappings: - deviceName: /dev/xvda ebs: diff --git a/flux_serve/specs/amd-neuron-trn1-nodepool.yaml b/flux_serve/specs/amd-neuron-trn1-nodepool.yaml index 2e7a23d..556fb37 100644 --- a/flux_serve/specs/amd-neuron-trn1-nodepool.yaml +++ b/flux_serve/specs/amd-neuron-trn1-nodepool.yaml @@ -18,7 +18,7 @@ spec: nodeClassRef: group: karpenter.k8s.aws kind: EC2NodeClass - name: amd-neuron + name: amd-neuron-al2023 expireAfter: 720h # 30 * 24h = 720h limits: cpu: 1000 @@ -29,20 +29,17 @@ spec: apiVersion: karpenter.k8s.aws/v1 kind: EC2NodeClass metadata: - name: amd-neuron + name: amd-neuron-al2023 spec: - amiFamily: AL2 # Amazon Linux 2 amiSelectorTerms: - - id: "ami-0e73cfd34ee52b6a6" - - id: "ami-077b8064c2a1a3186" - - id: "ami-057dddc807f3f6644" - role: "KarpenterNodeRole-flux-usw2" + - alias: "al2023@v20250419" + role: "KarpenterNodeRole-cova" subnetSelectorTerms: - tags: - karpenter.sh/discovery: "flux-usw2" + karpenter.sh/discovery: "cova" securityGroupSelectorTerms: - tags: - karpenter.sh/discovery: "flux-usw2" + karpenter.sh/discovery: "cova" blockDeviceMappings: - deviceName: /dev/xvda ebs: From 06cc2d298ab2f876f980f31fe8cd90915bded9c0 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 27 Apr 2025 19:05:30 -0700 Subject: [PATCH 34/45] cova --- flux_serve/app/cova_gradio.py | 123 ++++++++++++++++++++++++++++++++++ 1 file changed, 123 insertions(+) create mode 100644 flux_serve/app/cova_gradio.py diff --git a/flux_serve/app/cova_gradio.py b/flux_serve/app/cova_gradio.py new file mode 100644 index 0000000..db2ec86 --- /dev/null +++ b/flux_serve/app/cova_gradio.py @@ -0,0 +1,123 @@ +import gradio as gr +import requests +from PIL import Image +import io +import os +from fastapi import FastAPI +import base64 +import asyncio +import httpx + +app = FastAPI() + +models = [ + { + 'name': '512x512', + 'host_env': 'FLUX_NEURON_512X512_MODEL_API_SERVICE_HOST', + 'port_env': 'FLUX_NEURON_512X512_MODEL_API_SERVICE_PORT', + 'height': 512, + 'width': 512 + } +] + +for model in models: + host = os.environ[model['host_env']] + port = os.environ[model['port_env']] + model['url'] = f"http://{host}:{port}/generate" + +async def fetch_image(client, url, prompt, num_inference_steps): + payload = { + "prompt": prompt, + "num_inference_steps": int(num_inference_steps) + } + try: + response = await client.post(url, json=payload, timeout=60.0) + response.raise_for_status() + data = response.json() + image_bytes = base64.b64decode(data['image']) + image = Image.open(io.BytesIO(image_bytes)) + execution_time = data.get('execution_time', 0) + return image, f"{execution_time:.2f} seconds" + except httpx.RequestError as e: + return None, f"Request Error: {str(e)}" + except Exception as e: + return None, f"Error: {str(e)}" + +async def call_model_api(prompt, num_inference_steps): + async with httpx.AsyncClient() as client: + tasks = [ + fetch_image(client, model['url'], prompt, num_inference_steps) + for model in models + ] + results = await asyncio.gather(*tasks) + images = [] + exec_times = [] + for image, exec_time in results: + images.append(image) + exec_times.append(exec_time) + return images + exec_times + +@app.get("/health") +def healthy(): + return {"message": "Service is healthy"} + +@app.get("/readiness") +def ready(): + return {"message": "Service is ready"} + +with gr.Blocks() as interface: + gr.Markdown(f"# {model_id} Image Generation App") + gr.Markdown("Enter a prompt and specify the number of inference steps to generate images in different shapes.") + + with gr.Row(): + with gr.Column(scale=1): + prompt = gr.Textbox( + label="Prompt", + lines=1, + placeholder="Enter your prompt here...", + elem_id="prompt-box" + ) + inference_steps = gr.Number( + label="Inference Steps", + value=10, + precision=0, + info="Enter the number of inference steps; higher number takes more time but produces better image", + elem_id="steps-number" + ) + generate_button = gr.Button("Generate Images", variant="primary") + + with gr.Column(scale=2): + image_components = [] + exec_time_components = [] + + with gr.Row(): + for idx, model in enumerate(models): + with gr.Column(): + # Title + gr.Markdown(f"**{model['name']}**") + + # Scale down the image + preview_height = int(model['height'] / 2) + preview_width = int(model['width'] / 2) + + img = gr.Image( + label="", + height=preview_height, + width=preview_width, + interactive=False + ) + # Use Markdown for simpler smaller text + exec_time = gr.Markdown(value="") + + image_components.append(img) + exec_time_components.append(exec_time) + + # callback for the button + generate_button.click( + fn=call_model_api, + inputs=[prompt, inference_steps], + outputs=image_components + exec_time_components, + api_name="generate_images" + ) + +app = gr.mount_gradio_app(app, interface, path="/serve") From cbcc840b17e243b62c2b58139b16552f5cafe970 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 27 Apr 2025 20:02:19 -0700 Subject: [PATCH 35/45] cova_gradio.py --- flux_serve/app/cova_gradio.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flux_serve/app/cova_gradio.py b/flux_serve/app/cova_gradio.py index db2ec86..e9f22a1 100644 --- a/flux_serve/app/cova_gradio.py +++ b/flux_serve/app/cova_gradio.py @@ -66,7 +66,7 @@ def ready(): return {"message": "Service is ready"} with gr.Blocks() as interface: - gr.Markdown(f"# {model_id} Image Generation App") + gr.Markdown(f"# Image Generation App") gr.Markdown("Enter a prompt and specify the number of inference steps to generate images in different shapes.") with gr.Row(): From 831968f901437a56e27ac6782e555d6a28138397 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 27 Apr 2025 20:15:53 -0700 Subject: [PATCH 36/45] vllm_model_api.py --- flux_serve/app/vllm_model_api.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/flux_serve/app/vllm_model_api.py b/flux_serve/app/vllm_model_api.py index 4a42087..82eee60 100644 --- a/flux_serve/app/vllm_model_api.py +++ b/flux_serve/app/vllm_model_api.py @@ -17,6 +17,7 @@ from vllm import LLM, SamplingParams from sentence_transformers import SentenceTransformer import yaml +from transformers import AutoConfig cw_namespace='hw-agnostic-infer' default_max_new_tokens=50 @@ -124,6 +125,11 @@ class GenerateBenchmarkResponse(BaseModel): report: str = Field(..., description="Benchmark report") def load_model(): + model_id = vllm_config["model"] + config = AutoConfig.from_pretrained(model_id) + if config.rope_scaling is None: + config.rope_scaling = {"rope_type": "linear", "scaling_factor": 1.0} + vllm_config["model_config"] = config model = LLM(**vllm_config) return model From 1ba46f37136b5993067e84d9065bc87cfac4b667 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 27 Apr 2025 20:24:07 -0700 Subject: [PATCH 37/45] vllm_model_api.py --- flux_serve/app/vllm_model_api.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/flux_serve/app/vllm_model_api.py b/flux_serve/app/vllm_model_api.py index 82eee60..8b988bb 100644 --- a/flux_serve/app/vllm_model_api.py +++ b/flux_serve/app/vllm_model_api.py @@ -29,6 +29,9 @@ pod_name = os.environ['POD_NAME'] hf_token = os.environ['HUGGINGFACE_TOKEN'].strip() +repo_id=os.environ['MODEL_ID'] +os.environ['NEURON_COMPILED_ARTIFACTS']=repo_id + with open("/vllm_config.yaml", "r") as file: vllm_config=yaml.safe_load(file) From 43e8415baf9fbfa86c66de53cbcb30e4762ccf66 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 27 Apr 2025 20:36:58 -0700 Subject: [PATCH 38/45] download_hf_model.py --- flux_serve/app/download_hf_model.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/flux_serve/app/download_hf_model.py b/flux_serve/app/download_hf_model.py index fde2467..d46fc4c 100644 --- a/flux_serve/app/download_hf_model.py +++ b/flux_serve/app/download_hf_model.py @@ -1,9 +1,9 @@ from huggingface_hub import login,snapshot_download import os +repo_id=os.environ['MODEL_ID'] +os.environ['NEURON_COMPILED_ARTIFACTS']=repo_id hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() -repo_id=os.environ['COMPILED_MODEL_ID'] -repo_dir=os.environ['NEURON_COMPILED_ARTIFACTS'] login(hf_token, add_to_git_credential=True) -snapshot_download(repo_id=repo_id,local_dir=repo_dir,token=hf_token) -print(f"Repository '{repo_id}' downloaded to '{repo_dir}'.") +snapshot_download(repo_id=repo_id,local_dir=repo_id,token=hf_token) +print(f"Repository '{repo_id}' downloaded to '{repo_id}'.") From aaa255145e958ecdb0f3b875224a72cedc850bb6 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 27 Apr 2025 20:53:52 -0700 Subject: [PATCH 39/45] vllm_model_api.py --- flux_serve/app/vllm_model_api.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/flux_serve/app/vllm_model_api.py b/flux_serve/app/vllm_model_api.py index 8b988bb..5c18dd7 100644 --- a/flux_serve/app/vllm_model_api.py +++ b/flux_serve/app/vllm_model_api.py @@ -17,7 +17,6 @@ from vllm import LLM, SamplingParams from sentence_transformers import SentenceTransformer import yaml -from transformers import AutoConfig cw_namespace='hw-agnostic-infer' default_max_new_tokens=50 @@ -128,11 +127,6 @@ class GenerateBenchmarkResponse(BaseModel): report: str = Field(..., description="Benchmark report") def load_model(): - model_id = vllm_config["model"] - config = AutoConfig.from_pretrained(model_id) - if config.rope_scaling is None: - config.rope_scaling = {"rope_type": "linear", "scaling_factor": 1.0} - vllm_config["model_config"] = config model = LLM(**vllm_config) return model From 8bec2fcfb2864ae58dcbed1c735c735ab928920f Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 27 Apr 2025 21:33:35 -0700 Subject: [PATCH 40/45] download_hf_model.py --- flux_serve/app/download_hf_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flux_serve/app/download_hf_model.py b/flux_serve/app/download_hf_model.py index d46fc4c..202a32a 100644 --- a/flux_serve/app/download_hf_model.py +++ b/flux_serve/app/download_hf_model.py @@ -4,6 +4,6 @@ os.environ['NEURON_COMPILED_ARTIFACTS']=repo_id hf_token=os.environ['HUGGINGFACE_TOKEN'].strip() login(hf_token, add_to_git_credential=True) -snapshot_download(repo_id=repo_id,local_dir=repo_id,token=hf_token) +snapshot_download(repo_id=repo_id,local_dir="/"+repo_id,token=hf_token) print(f"Repository '{repo_id}' downloaded to '{repo_id}'.") From c1409a1089cc6468e9cfe3c0f4056c64e4ad4e91 Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 27 Apr 2025 22:45:26 -0700 Subject: [PATCH 41/45] cova_gradio_m.py --- flux_serve/app/cova_gradio_m.py | 132 ++++++++++++++++++++++++++++++++ 1 file changed, 132 insertions(+) create mode 100644 flux_serve/app/cova_gradio_m.py diff --git a/flux_serve/app/cova_gradio_m.py b/flux_serve/app/cova_gradio_m.py new file mode 100644 index 0000000..0901f37 --- /dev/null +++ b/flux_serve/app/cova_gradio_m.py @@ -0,0 +1,132 @@ +import os, io, base64, asyncio, json +from typing import Tuple, List +import gradio as gr +import httpx +from PIL import Image +from fastapi import FastAPI + +app = FastAPI() + +# --------------------------------------------------------------------------- +# ā¶ Model definitions -ā€“ extend with more rows if you deploy more shapes later +# --------------------------------------------------------------------------- +IMAGE_MODELS = [ + dict( + name="512 Ɨ 512", + host_env="FLUX_NEURON_512X512_MODEL_API_SERVICE_HOST", + port_env="FLUX_NEURON_512X512_MODEL_API_SERVICE_PORT", + height=512, + width=512, + # caption backend for this image size + caption_host_env="MLLAMA_32_11B_VLLM_TRN1_SERVICE_HOST", + caption_port_env="MLLAMA_32_11B_VLLM_TRN1_SERVICE_PORT", + # number of caption tokens to ask for per request + caption_max_new_tokens=64, + ) +] + +for m in IMAGE_MODELS: + m["image_url"] = f'http://{os.environ[m["host_env"]]}:{os.environ[m["port_env"]]}/generate' + m["caption_url"] = f'http://{os.environ[m["caption_host_env"]]}:{os.environ[m["caption_port_env"]]}/generate' + +# --------------------------------------------------------------------------- +# ā· Helpers +# --------------------------------------------------------------------------- +async def post_json(client: httpx.AsyncClient, url: str, payload: dict, timeout: float = 60.0): + """Small wrapper that returns (json, elapsed_seconds) or raises.""" + start = asyncio.get_event_loop().time() + r = await client.post(url, json=payload, timeout=timeout) + r.raise_for_status() + elapsed = asyncio.get_event_loop().time() - start + return r.json(), elapsed + +def pil_to_base64(img: Image.Image) -> str: + buf = io.BytesIO() + img.save(buf, format="PNG") + return base64.b64encode(buf.getvalue()).decode() + +async def fetch_end_to_end( + client : httpx.AsyncClient, + model_cfg : dict, + prompt : str, + num_steps : int +) -> Tuple[Image.Image, str, str]: + """ + Returns (image, latency_str, caption_str, caption_latency_str) + """ + # ā‘  Generate the image + img_payload = {"prompt": prompt, "num_inference_steps": int(num_steps)} + img_json, img_latency = await post_json(client, model_cfg["image_url"], img_payload) + image = Image.open(io.BytesIO(base64.b64decode(img_json["image"]))) + + # ā‘” Ask vLLM to describe that image + img_b64 = pil_to_base64(image) + caption_prompt = f"Describe the content of this image (base64 PNG follows): {img_b64}" + cap_payload = {"prompt": caption_prompt, + "max_new_tokens": model_cfg["caption_max_new_tokens"]} + cap_json, cap_latency = await post_json(client, model_cfg["caption_url"], cap_payload) + caption = base64.b64decode(cap_json["text"]).decode() + + return image, f"{img_latency:.2f}s", caption, f"{cap_latency:.2f}s" + +async def orchestrate_calls(prompt: str, num_steps: int): + async with httpx.AsyncClient() as client: + tasks = [fetch_end_to_end(client, cfg, prompt, num_steps) for cfg in IMAGE_MODELS] + results = await asyncio.gather(*tasks) + + # Flatten results for gradio ā†’ [img, img_lat, caption, cap_lat] * N + flat: List = [] + for tup in results: + flat.extend(tup) + return flat + +# --------------------------------------------------------------------------- +# āø Gradio UI +# --------------------------------------------------------------------------- +with gr.Blocks() as interface: + gr.Markdown("# āš” Flux Image-Gen + vLLM Caption Demo") + gr.Markdown("Enter a text prompt āžœ model draws an image āžœ LLM describes the image.") + + with gr.Row(): + # user controls + with gr.Column(scale=1): + prompt_in = gr.Textbox(lines=1, label="Prompt") + steps_in = gr.Number(label="Inference Steps", value=10, precision=0) + btn_generate = gr.Button("Generate", variant="primary") + + # results + with gr.Column(scale=2): + img_out_components: list = [] + img_lat_components: list = [] + cap_out_components: list = [] + cap_lat_components: list = [] + + for cfg in IMAGE_MODELS: + with gr.Group(): + gr.Markdown(f"### {cfg['name']}") + img = gr.Image(height=cfg["height"]//2, + width=cfg["width"]//2, + interactive=False) + lat = gr.Markdown() + cap = gr.Markdown() + cap_lat = gr.Markdown() + img_out_components.append(img) + img_lat_components.append(lat) + cap_out_components.append(cap) + cap_lat_components.append(cap_lat) + + # wire them all up + btn_generate.click( + orchestrate_calls, + inputs=[prompt_in, steps_in], + outputs=( + img_out_components + + img_lat_components + + cap_out_components + + cap_lat_components + ), + api_name="generate_and_caption", + ) + +app = gr.mount_gradio_app(app, interface, path="/serve") + From cc5695fc8eea86029876696122bb805d0879198c Mon Sep 17 00:00:00 2001 From: Yahav Date: Sun, 27 Apr 2025 23:01:44 -0700 Subject: [PATCH 42/45] cova_gradio_m.py --- flux_serve/app/cova_gradio_m.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/flux_serve/app/cova_gradio_m.py b/flux_serve/app/cova_gradio_m.py index 0901f37..000c82a 100644 --- a/flux_serve/app/cova_gradio_m.py +++ b/flux_serve/app/cova_gradio_m.py @@ -130,3 +130,10 @@ async def orchestrate_calls(prompt: str, num_steps: int): app = gr.mount_gradio_app(app, interface, path="/serve") +@app.get("/health") +def healthy(): + return {"message": "Service is healthy"} + +@app.get("/readiness") +def ready(): + return {"message": "Service is ready"} From 313782b35bb665cc0d9fe328f97af41a9d61f77a Mon Sep 17 00:00:00 2001 From: Yahav Date: Mon, 5 May 2025 09:17:02 -0700 Subject: [PATCH 43/45] t5_model_api.py --- flux_serve/app/t5_model_api.py | 172 +++++++++++++++++++++++++++++++++ 1 file changed, 172 insertions(+) create mode 100644 flux_serve/app/t5_model_api.py diff --git a/flux_serve/app/t5_model_api.py b/flux_serve/app/t5_model_api.py new file mode 100644 index 0000000..731e895 --- /dev/null +++ b/flux_serve/app/t5_model_api.py @@ -0,0 +1,172 @@ +import traceback +import math +import boto3 +import time +import argparse +import torch +import os +from fastapi import FastAPI, HTTPException +from pydantic import BaseModel, Field +from typing import Any, Dict, Optional, Union +from huggingface_hub import login,snapshot_download +import base64 +import torch +from transformers import T5Tokenizer +from neuronx_distributed.trace import parallel_model_load + +cw_namespace='hw-agnostic-infer' +default_max_new_tokens=50 +cloudwatch = boto3.client('cloudwatch', region_name='us-west-2') +sampling_params = SamplingParams(temperature=0.7,top_k=50,top_p=0.9,max_tokens=128,) + +app_name=os.environ['APP'] +nodepool=os.environ['NODEPOOL'] +pod_name = os.environ['POD_NAME'] +hf_token = os.environ['HUGGINGFACE_TOKEN'].strip() + +model_id=os.environ['MODEL_ID'] +repo_id=os.environ['COMPILED_MODEL_ID'] +local_dir=snapshot_download(repo_id,allow_patterns="tp_*.pt") +max_sequence_length = int(os.environ['MAX_SEQ_LEN']) + +tokenizer = T5Tokenizer.from_pretrained(model_id) +tokenizer.model_max_length = max_sequence_length +model = parallel_model_load(local_dir) + +def gentext(prompt,max_new_tokens): + start_time = time.time() + inputs = tokenizer(promp, return_tensors="pt", truncation=True, padding="max_length", max_length=max_new_tokens) + with torch.no_grad(): + output = model(inputs["input_ids"], inputs["attention_mask"]) + if isinstance(output, dict): + last_hidden_state = output["last_hidden_state"] + else: + last_hidden_state = output + embeddings = last_hidden_state.mean(dim=1).squeeze().to(torch.float32).cpu().numpy() + total_time = time.time()-start_time + return str(embeddings), float(total_time) + +def cw_pub_metric(metric_name,metric_value,metric_unit): + response = cloudwatch.put_metric_data( + Namespace=cw_namespace, + MetricData=[ + { + 'MetricName':metric_name, + 'Value':metric_value, + 'Unit':metric_unit, + }, + ] + ) + print(f"in pub_deployment_counter - response:{response}") + return response + +login(hf_token, add_to_git_credential=True) + +def benchmark(n_runs,test_name,prompt,max_new_tokens): + latency_collector = LatencyCollector() + + for _ in range(n_runs): + latency_collector.pre_hook() + gentext(prompt,max_new_tokens) + res = model.generate(prompt,sampling_params) + latency_collector.hook() + + p0_latency_ms = latency_collector.percentile(0) * 1000 + p50_latency_ms = latency_collector.percentile(50) * 1000 + p90_latency_ms = latency_collector.percentile(90) * 1000 + p95_latency_ms = latency_collector.percentile(95) * 1000 + p99_latency_ms = latency_collector.percentile(99) * 1000 + p100_latency_ms = latency_collector.percentile(100) * 1000 + + report_dict = dict() + report_dict["Latency P0"] = f'{p0_latency_ms:.1f}' + report_dict["Latency P50"]=f'{p50_latency_ms:.1f}' + report_dict["Latency P90"]=f'{p90_latency_ms:.1f}' + report_dict["Latency P95"]=f'{p95_latency_ms:.1f}' + report_dict["Latency P99"]=f'{p99_latency_ms:.1f}' + report_dict["Latency P100"]=f'{p100_latency_ms:.1f}' + + report = f'RESULT FOR {test_name}:' + for key, value in report_dict.items(): + report += f' {key}={value}' + print(report) + return report + +class LatencyCollector: + def __init__(self): + self.start = None + self.latency_list = [] + + def pre_hook(self, *args): + self.start = time.time() + + def hook(self, *args): + self.latency_list.append(time.time() - self.start) + + def percentile(self, percent): + latency_list = self.latency_list + pos_float = len(latency_list) * percent / 100 + max_pos = len(latency_list) - 1 + pos_floor = min(math.floor(pos_float), max_pos) + pos_ceil = min(math.ceil(pos_float), max_pos) + latency_list = sorted(latency_list) + return latency_list[pos_ceil] if pos_float - pos_floor > 0.5 else latency_list[pos_floor] + +class GenerateRequest(BaseModel): + max_new_tokens: int + prompt: str + +class GenerateBenchmarkRequest(BaseModel): + n_runs: int + max_new_tokens: int + prompt: str + +class GenerateResponse(BaseModel): + text: str = Field(..., description="Base64-encoded text") + execution_time: float + +class GenerateBenchmarkResponse(BaseModel): + report: str = Field(..., description="Benchmark report") + +prompt= "What model are you?" +benchmark(10,"warmup",prompt,default_max_new_tokens) +app = FastAPI() + +@app.post("/benchmark",response_model=GenerateBenchmarkResponse) +def generate_benchmark_report(request: GenerateBenchmarkRequest): + print(f'DEBUG: GenerateBenchmarkRequest:{request}') + try: + with torch.no_grad(): + test_name=f'benchmark:{app_name} on {nodepool} with {request.max_new_tokens} output tokens' + response_report=benchmark(request.n_runs,test_name,model,request.prompt,request.max_new_tokens) + report_base64 = base64.b64encode(response_report.encode()).decode() + return GenerateBenchmarkResponse(report=report_base64) + except Exception as e: + traceback.print_exc() + raise HTTPException(status_code=500, detail=f"{e}") + +@app.post("/generate", response_model=GenerateResponse) +def generate_text_post(request: GenerateRequest): + try: + with torch.no_grad(): + response_text,total_time=gentext(request.prompt,request.max_new_tokens) + counter_metric=app_name+'-counter' + cw_pub_metric(counter_metric,1,'Count') + counter_metric=nodepool + cw_pub_metric(counter_metric,1,'Count') + latency_metric=app_name+'-latency' + cw_pub_metric(latency_metric,total_time,'Seconds') + text_base64 = base64.b64encode(response_text.encode()).decode() + return GenerateResponse(text=text_base64, execution_time=total_time) + except Exception as e: + traceback.print_exc() + raise HTTPException(status_code=500, detail=f"text serialization failed: {e}") + +# Health and readiness endpoints +@app.get("/health") +def healthy(): + return {"message": f"{pod_name} is healthy"} + +@app.get("/readiness") +def ready(): + return {"message": f"{pod_name} is ready"} From f551dc9c7514db7458ddf4ce5f645adfb555fca0 Mon Sep 17 00:00:00 2001 From: Yahav Date: Mon, 5 May 2025 11:57:22 -0700 Subject: [PATCH 44/45] compile --- flux_serve/specs/compile.yaml | 72 +++++++++++++++++++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 flux_serve/specs/compile.yaml diff --git a/flux_serve/specs/compile.yaml b/flux_serve/specs/compile.yaml new file mode 100644 index 0000000..2556a4e --- /dev/null +++ b/flux_serve/specs/compile.yaml @@ -0,0 +1,72 @@ +apiVersion: batch/v1 +kind: Job +metadata: + name: compile +spec: + template: + spec: + restartPolicy: OnFailure + nodeSelector: + karpenter.sh/nodepool: amd-neuron-trn1 + #serviceAccountName: appsimulator + schedulerName: my-scheduler + containers: + - name: app + image: 920372998901.dkr.ecr.us-east-1.amazonaws.com/model:amd64-neuron + imagePullPolicy: Always + volumeMounts: + - name: dshm + mountPath: /dev/shm + - name: neuron-bits-use1-pvc + mountPath: /model + command: + - /bin/bash + - "-exc" + - | + set -x + python /download_hf_model.py + find /model -type f -name '*.deb' -exec sh -c ' + for pkg; do + cp "$pkg" / && + apt install -y "/$(basename "$pkg")" + done + ' _ {} + + find /model -type f -name '*.whl' -exec sh -c ' + for pkg; do + cp "$pkg" / && + pip install "/$(basename "$pkg")" + done + ' _ {} + + while true; do sleep 3600; done + resources: + limits: + aws.amazon.com/neuron: 16 + requests: + aws.amazon.com/neuron: 16 + env: + - name: NODEPOOL + value: "amd-neuron-trn1" + - name: COMPILER_WORKDIR_ROOT + value: "/model" + - name: HEIGHT + value: "512" + - name: WIDTH + value: "512" + - name: MAX_SEQ_LEN + value: "32" + - name: GUIDANCE_SCALE + value: "3.5" + - name: MODEL_ID + value: "black-forest-labs/FLUX.1-dev" + - name: HUGGINGFACE_TOKEN + valueFrom: + secretKeyRef: + name: hf-secrets + key: HUGGINGFACE_TOKEN + volumes: + - name: dshm + emptyDir: + medium: Memory + - name: neuron-bits-use1-pvc + persistentVolumeClaim: + claimName: neuron-bits-use1-pvc From c933cb5453359cc98c1e93d52f5def21c9d94ec9 Mon Sep 17 00:00:00 2001 From: Yahav Date: Fri, 23 May 2025 11:50:30 -0700 Subject: [PATCH 45/45] comm --- .../specs/amd-neuron-inf2-nodepool.yaml | 8 +- .../specs/amd-neuron-trn1-nodepool.yaml | 8 +- flux_serve/specs/compile-flux-256x144.yaml | 27 ++--- flux_serve/specs/compile-flux-512x512.yaml | 7 +- flux_serve/specs/cova-gradio-config.yaml | 18 +++ flux_serve/specs/cova-gradio-deploy.yaml | 64 ++++++++++ flux_serve/specs/cova-ingress.yaml | 34 ++++++ .../specs/flux-karpenter-inline-sts.json | 13 +++ flux_serve/specs/flux-model-s3-storage.yaml | 3 +- .../specs/flux-neuron-256x144-model-api.yaml | 18 ++- .../specs/flux-neuron-512x512-model-api.yaml | 20 +++- flux_serve/specs/flux-neuron-gradio.yaml | 6 +- flux_serve/specs/flux-neuron-ingress.yaml | 6 +- .../specs/mllama-32-11b-vllm-trn1-config.yaml | 24 ++++ .../specs/mllama-32-11b-vllm-trn1-deploy.yaml | 110 ++++++++++++++++++ flux_serve/specs/neuron-bits-s3-storage.yaml | 33 ++++++ 16 files changed, 360 insertions(+), 39 deletions(-) create mode 100644 flux_serve/specs/cova-gradio-config.yaml create mode 100644 flux_serve/specs/cova-gradio-deploy.yaml create mode 100644 flux_serve/specs/cova-ingress.yaml create mode 100644 flux_serve/specs/flux-karpenter-inline-sts.json create mode 100644 flux_serve/specs/mllama-32-11b-vllm-trn1-config.yaml create mode 100644 flux_serve/specs/mllama-32-11b-vllm-trn1-deploy.yaml create mode 100644 flux_serve/specs/neuron-bits-s3-storage.yaml diff --git a/flux_serve/specs/amd-neuron-inf2-nodepool.yaml b/flux_serve/specs/amd-neuron-inf2-nodepool.yaml index 151b67d..201eebb 100644 --- a/flux_serve/specs/amd-neuron-inf2-nodepool.yaml +++ b/flux_serve/specs/amd-neuron-inf2-nodepool.yaml @@ -32,14 +32,14 @@ metadata: name: amd-neuron-al2023 spec: amiSelectorTerms: - - alias: "al2023@v20250419" - role: "KarpenterNodeRole-cova" + - alias: "al2023@v20250501" + role: "KarpenterNodeRole-cova-use1" subnetSelectorTerms: - tags: - karpenter.sh/discovery: "cova" + karpenter.sh/discovery: "cova-use1" securityGroupSelectorTerms: - tags: - karpenter.sh/discovery: "cova" + karpenter.sh/discovery: "cova-use1" blockDeviceMappings: - deviceName: /dev/xvda ebs: diff --git a/flux_serve/specs/amd-neuron-trn1-nodepool.yaml b/flux_serve/specs/amd-neuron-trn1-nodepool.yaml index 556fb37..c8176d5 100644 --- a/flux_serve/specs/amd-neuron-trn1-nodepool.yaml +++ b/flux_serve/specs/amd-neuron-trn1-nodepool.yaml @@ -32,14 +32,14 @@ metadata: name: amd-neuron-al2023 spec: amiSelectorTerms: - - alias: "al2023@v20250419" - role: "KarpenterNodeRole-cova" + - alias: "al2023@v20250501" + role: "KarpenterNodeRole-cova-use1" subnetSelectorTerms: - tags: - karpenter.sh/discovery: "cova" + karpenter.sh/discovery: "cova-use1" securityGroupSelectorTerms: - tags: - karpenter.sh/discovery: "cova" + karpenter.sh/discovery: "cova-use1" blockDeviceMappings: - deviceName: /dev/xvda ebs: diff --git a/flux_serve/specs/compile-flux-256x144.yaml b/flux_serve/specs/compile-flux-256x144.yaml index aff6374..5f7b3d1 100644 --- a/flux_serve/specs/compile-flux-256x144.yaml +++ b/flux_serve/specs/compile-flux-256x144.yaml @@ -1,35 +1,36 @@ apiVersion: batch/v1 kind: Job metadata: - name: compile-256x144 + name: compile-256x256 spec: template: spec: restartPolicy: OnFailure nodeSelector: karpenter.sh/nodepool: amd-neuron-trn1 - #serviceAccountName: appsimulator + serviceAccountName: appsimulator schedulerName: my-scheduler containers: - name: app - image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + image: 891377065549.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + #image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron imagePullPolicy: Always volumeMounts: - name: dshm mountPath: /dev/shm - - name: s3-flux-pvc - mountPath: /model + #- name: s3-flux-pvc + # mountPath: /model command: - /bin/bash - "-exc" - | set -x - mkdir -p /model/256x144 + while true; do sleep 3600; done + mkdir -p /model/256x256 cd /src ./compile.sh - cp -r * /model/256x144 + cp -r * /model/256x256 python /benchmark-flux.py - while true; do sleep 3600; done resources: limits: aws.amazon.com/neuron: 8 @@ -39,11 +40,11 @@ spec: - name: NODEPOOL value: "amd-neuron-trn1" - name: COMPILER_WORKDIR_ROOT - value: "/model/256x144" + value: "/model/256x256" - name: HEIGHT value: "256" - name: WIDTH - value: "144" + value: "256" - name: MAX_SEQ_LEN value: "32" - name: GUIDANCE_SCALE @@ -59,6 +60,6 @@ spec: - name: dshm emptyDir: medium: Memory - - name: s3-flux-pvc - persistentVolumeClaim: - claimName: s3-flux-pvc + #- name: s3-flux-pvc + # persistentVolumeClaim: + # claimName: s3-flux-pvc diff --git a/flux_serve/specs/compile-flux-512x512.yaml b/flux_serve/specs/compile-flux-512x512.yaml index 29c8220..1572338 100644 --- a/flux_serve/specs/compile-flux-512x512.yaml +++ b/flux_serve/specs/compile-flux-512x512.yaml @@ -12,7 +12,8 @@ spec: schedulerName: my-scheduler containers: - name: app - image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + #image: 891377065549.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + image: 920372998901.dkr.ecr.us-east-1.amazonaws.com/model:amd64-neuron imagePullPolicy: Always volumeMounts: - name: dshm @@ -32,9 +33,9 @@ spec: while true; do sleep 3600; done resources: limits: - aws.amazon.com/neuron: 6 + aws.amazon.com/neuron: 16 requests: - aws.amazon.com/neuron: 6 + aws.amazon.com/neuron: 16 env: - name: NODEPOOL value: "amd-neuron-trn1" diff --git a/flux_serve/specs/cova-gradio-config.yaml b/flux_serve/specs/cova-gradio-config.yaml new file mode 100644 index 0000000..6b34cbb --- /dev/null +++ b/flux_serve/specs/cova-gradio-config.yaml @@ -0,0 +1,18 @@ +apiVersion: v1 +kind: ConfigMap +metadata: + name: cova-gradio-config +data: + models.json: | + [ + { + "name": "512 Ɨ 512", + "host_env": "FLUX_NEURON_512X512_MODEL_API_SERVICE_HOST", + "port_env": "FLUX_NEURON_512X512_MODEL_API_SERVICE_PORT", + "height": 512, + "width": 512, + "caption_host_env": "MLLAMA_32_11B_VLLM_TRN1_SERVICE_HOST", + "caption_port_env": "MLLAMA_32_11B_VLLM_TRN1_SERVICE_PORT", + "caption_max_new_tokens": 1024 + } + ] diff --git a/flux_serve/specs/cova-gradio-deploy.yaml b/flux_serve/specs/cova-gradio-deploy.yaml new file mode 100644 index 0000000..c84d003 --- /dev/null +++ b/flux_serve/specs/cova-gradio-deploy.yaml @@ -0,0 +1,64 @@ +apiVersion: v1 +kind: Service +metadata: + name: cova-gradio +spec: + selector: + app: cova-gradio + ports: + - protocol: TCP + port: 8000 + targetPort: 8000 + type: ClusterIP +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + labels: + app: cova-gradio + name: cova-gradio +spec: + selector: + matchLabels: + app: cova-gradio + template: + metadata: + labels: + app: cova-gradio + spec: + nodeSelector: + alpha.eksctl.io/nodegroup-name: kub316-ng + #serviceAccountName: appsimulator + containers: + - name: app + image: 891377065549.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + #image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + imagePullPolicy: Always + volumeMounts: + - name: cova-gradio-volume + mountPath: /app + #command: ["sh", "-c", "while true; do sleep 3600; done"] + command: ["sh", "-c", "uvicorn cova_gradio_m:app --host=0.0.0.0"] + ports: + - containerPort: 8000 + protocol: TCP + readinessProbe: + httpGet: + path: /readiness + port: 8000 + initialDelaySeconds: 60 + periodSeconds: 10 + env: + - name: MODELS_FILE_PATH + value: "/app/models.json" + - name: POD_NAME + valueFrom: + fieldRef: + fieldPath: metadata.name + volumes: + - name: cova-gradio-volume + configMap: + name: cova-gradio-config + items: + - key: models.json + path: models.json diff --git a/flux_serve/specs/cova-ingress.yaml b/flux_serve/specs/cova-ingress.yaml new file mode 100644 index 0000000..928c6d7 --- /dev/null +++ b/flux_serve/specs/cova-ingress.yaml @@ -0,0 +1,34 @@ +apiVersion: networking.k8s.io/v1 +kind: Ingress +metadata: + name: cova + annotations: + kubernetes.io/ingress.class: alb + alb.ingress.kubernetes.io/scheme: internet-facing + alb.ingress.kubernetes.io/target-type: ip + alb.ingress.kubernetes.io/healthcheck-path: /health + alb.ingress.kubernetes.io/healthcheck-interval-seconds: '10' + alb.ingress.kubernetes.io/healthcheck-timeout-seconds: '9' + alb.ingress.kubernetes.io/healthy-threshold-count: '2' + alb.ingress.kubernetes.io/unhealthy-threshold-count: '10' + alb.ingress.kubernetes.io/success-codes: '200-301' + alb.ingress.kubernetes.io/listen-ports: '[{"HTTP":80}]' + alb.ingress.kubernetes.io/backend-protocol: HTTP + alb.ingress.kubernetes.io/target-group-attributes: stickiness.enabled=true,stickiness.lb_cookie.duration_seconds=10 + alb.ingress.kubernetes.io/load-balancer-name: cova + alb.ingress.kubernetes.io/actions.weighted-routing: > + {"type":"forward","forwardConfig":{"targetGroups":[{"serviceName":"cova-gradio","servicePort":8000,"weight":100}],"targetGroupStickinessConfig":{"enabled":true,"durationSeconds":200}}} + labels: + app: cova-neuron-gradio +spec: + ingressClassName: alb + rules: + - http: + paths: + - path: / + pathType: Prefix + backend: + service: + name: weighted-routing + port: + name: use-annotation diff --git a/flux_serve/specs/flux-karpenter-inline-sts.json b/flux_serve/specs/flux-karpenter-inline-sts.json new file mode 100644 index 0000000..7642272 --- /dev/null +++ b/flux_serve/specs/flux-karpenter-inline-sts.json @@ -0,0 +1,13 @@ +{ + "Version": "2012-10-17", + "Statement": [ + { + "Effect": "Allow", + "Action": [ + "sts:AssumeRole", + "sts:AssumeRoleWithWebIdentity" + ], + "Resource": "*" + } + ] +} diff --git a/flux_serve/specs/flux-model-s3-storage.yaml b/flux_serve/specs/flux-model-s3-storage.yaml index 0d4471a..884eb75 100644 --- a/flux_serve/specs/flux-model-s3-storage.yaml +++ b/flux_serve/specs/flux-model-s3-storage.yaml @@ -17,7 +17,8 @@ spec: driver: s3.csi.aws.com volumeHandle: s3-csi-driver-volume volumeAttributes: - bucketName: flux1-dev-neuron + bucketName: flux1-dev-neuron2 + #bucketName: flux1-dev-neuron --- apiVersion: v1 kind: PersistentVolumeClaim diff --git a/flux_serve/specs/flux-neuron-256x144-model-api.yaml b/flux_serve/specs/flux-neuron-256x144-model-api.yaml index 1f80515..1ada152 100644 --- a/flux_serve/specs/flux-neuron-256x144-model-api.yaml +++ b/flux_serve/specs/flux-neuron-256x144-model-api.yaml @@ -28,18 +28,30 @@ spec: spec: nodeSelector: karpenter.sh/nodepool: amd-neuron-inf2 - serviceAccountName: flux-serviceaccount + serviceAccountName: appsimulator + #serviceAccountName: flux-serviceaccount schedulerName: my-scheduler containers: - name: app - image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + image: 891377065549.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + #image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron imagePullPolicy: Always volumeMounts: - name: dshm mountPath: /dev/shm - name: s3-flux-pvc mountPath: /model - command: ["sh", "-c", "uvicorn flux_model_api:app --host=0.0.0.0"] + command: + - /bin/bash + - "-exc" + - | + set -x + pip install --upgrade pip + pip config set global.extra-index-url https://pip.repos.neuron.amazonaws.com + pip install --upgrade "neuronx-distributed==0.11.0" --extra-index-url https://pip.repos.neuron.amazonaws.com + pip install --upgrade "neuronx-distributed-inference>=0.2.0" --extra-index-url https://pip.repos.neuron.amazonaws.com + pip install --upgrade tenacity diffusers + uvicorn flux_model_api:app --host=0.0.0.0 resources: requests: aws.amazon.com/neuron: 6 diff --git a/flux_serve/specs/flux-neuron-512x512-model-api.yaml b/flux_serve/specs/flux-neuron-512x512-model-api.yaml index ed2935e..a2cc706 100644 --- a/flux_serve/specs/flux-neuron-512x512-model-api.yaml +++ b/flux_serve/specs/flux-neuron-512x512-model-api.yaml @@ -29,9 +29,11 @@ spec: nodeSelector: karpenter.sh/nodepool: amd-neuron-inf2 serviceAccountName: flux-serviceaccount + #serviceAccountName: appsimulator schedulerName: my-scheduler containers: - name: app + #image: 891377065549.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron imagePullPolicy: Always volumeMounts: @@ -39,12 +41,22 @@ spec: mountPath: /dev/shm - name: s3-flux-pvc mountPath: /model - command: ["sh", "-c", "uvicorn flux_model_api:app --host=0.0.0.0"] + command: + - /bin/bash + - "-exc" + - | + set -x + pip install --upgrade pip + pip config set global.extra-index-url https://pip.repos.neuron.amazonaws.com + pip install --upgrade "neuronx-distributed==0.11.0" --extra-index-url https://pip.repos.neuron.amazonaws.com + pip install --upgrade "neuronx-distributed-inference>=0.2.0" --extra-index-url https://pip.repos.neuron.amazonaws.com + pip install --upgrade tenacity diffusers + uvicorn flux_model_api:app --host=0.0.0.0 resources: requests: - aws.amazon.com/neuron: 6 + aws.amazon.com/neuron: 8 limits: - aws.amazon.com/neuron: 6 + aws.amazon.com/neuron: 8 ports: - containerPort: 8000 protocol: TCP @@ -52,7 +64,7 @@ spec: httpGet: path: /readiness port: 8000 - initialDelaySeconds: 60 + initialDelaySeconds: 460 periodSeconds: 10 env: - name: APP diff --git a/flux_serve/specs/flux-neuron-gradio.yaml b/flux_serve/specs/flux-neuron-gradio.yaml index e7ef5e8..843c348 100644 --- a/flux_serve/specs/flux-neuron-gradio.yaml +++ b/flux_serve/specs/flux-neuron-gradio.yaml @@ -27,14 +27,14 @@ spec: app: flux-neuron-gradio spec: nodeSelector: - alpha.eksctl.io/nodegroup-name: flux-usw2-ng + alpha.eksctl.io/nodegroup-name: cova-ng #serviceAccountName: appsimulator containers: - name: app image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron imagePullPolicy: Always #command: ["sh", "-c", "while true; do sleep 3600; done"] - command: ["sh", "-c", "uvicorn flux_gradio:app --host=0.0.0.0"] + command: ["sh", "-c", "uvicorn cova_gradio:app --host=0.0.0.0"] ports: - containerPort: 8000 protocol: TCP @@ -45,8 +45,6 @@ spec: initialDelaySeconds: 60 periodSeconds: 10 env: - - name: MODEL_ID - value: "black-forest-labs/FLUX.1-dev" - name: POD_NAME valueFrom: fieldRef: diff --git a/flux_serve/specs/flux-neuron-ingress.yaml b/flux_serve/specs/flux-neuron-ingress.yaml index b27e4f0..d81c8a2 100644 --- a/flux_serve/specs/flux-neuron-ingress.yaml +++ b/flux_serve/specs/flux-neuron-ingress.yaml @@ -1,7 +1,7 @@ apiVersion: networking.k8s.io/v1 kind: Ingress metadata: - name: flux-neuron + name: cova annotations: kubernetes.io/ingress.class: alb alb.ingress.kubernetes.io/scheme: internet-facing @@ -15,11 +15,11 @@ metadata: alb.ingress.kubernetes.io/listen-ports: '[{"HTTP":80}]' alb.ingress.kubernetes.io/backend-protocol: HTTP alb.ingress.kubernetes.io/target-group-attributes: stickiness.enabled=true,stickiness.lb_cookie.duration_seconds=10 - alb.ingress.kubernetes.io/load-balancer-name: flux-neuron + alb.ingress.kubernetes.io/load-balancer-name: cova alb.ingress.kubernetes.io/actions.weighted-routing: > {"type":"forward","forwardConfig":{"targetGroups":[{"serviceName":"flux-neuron-gradio","servicePort":8000,"weight":100}],"targetGroupStickinessConfig":{"enabled":true,"durationSeconds":200}}} labels: - app: flux-neuron-gradio + app: cova-neuron-gradio spec: ingressClassName: alb rules: diff --git a/flux_serve/specs/mllama-32-11b-vllm-trn1-config.yaml b/flux_serve/specs/mllama-32-11b-vllm-trn1-config.yaml new file mode 100644 index 0000000..adbed23 --- /dev/null +++ b/flux_serve/specs/mllama-32-11b-vllm-trn1-config.yaml @@ -0,0 +1,24 @@ +--- +apiVersion: v1 +kind: ConfigMap +metadata: + name: mllama-32-11b-vllm-trn1-config +data: + vllm_config.yaml: | + model: "yahavb/Llama-3.2-11B-Vision-Instruct-neuron-checkpoint" + tensor_parallel_size: 32 + max_num_seqs: 1 + block_size: 4096 + max_model_len: 128000 + override_neuron_config: + skip_warmup: true + context_encoding_buckets: [1024, 16384] + token_generation_buckets: [1024, 16384] + sequence_parallel_enabled: False + is_continuous_batching: True + on_device_sampling_config: + global_topk: 64 + dynamic: True + deterministic: False + device: "neuron" +--- diff --git a/flux_serve/specs/mllama-32-11b-vllm-trn1-deploy.yaml b/flux_serve/specs/mllama-32-11b-vllm-trn1-deploy.yaml new file mode 100644 index 0000000..2837eea --- /dev/null +++ b/flux_serve/specs/mllama-32-11b-vllm-trn1-deploy.yaml @@ -0,0 +1,110 @@ +apiVersion: v1 +kind: Service +metadata: + name: mllama-32-11b-vllm-trn1 +spec: + selector: + app: mllama-32-11b-vllm-trn1 + ports: + - port: 8000 + targetPort: 8000 + type: NodePort +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + labels: + app: mllama-32-11b-vllm-trn1 + name: mllama-32-11b-vllm-trn1 +spec: + selector: + matchLabels: + app: mllama-32-11b-vllm-trn1 + template: + metadata: + labels: + app: mllama-32-11b-vllm-trn1 + spec: + nodeSelector: + karpenter.sh/nodepool: amd-neuron-trn1 + serviceAccountName: appsimulator + #serviceAccountName: flux-serviceaccount + schedulerName: my-scheduler + volumes: + - name: dshm + emptyDir: + medium: Memory + containers: + - name: app + image: 891377065549.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + #image: 920372998901.dkr.ecr.us-west-2.amazonaws.com/model:amd64-neuron + imagePullPolicy: Always + volumeMounts: + - mountPath: /vllm_config.yaml + name: vllm-config-volume + subPath: vllm_config.yaml + - mountPath: /dev/shm + name: dshm + command: + - /bin/bash + - "-exc" + - | + set -x + pip install --upgrade pip + pip config set global.extra-index-url https://pip.repos.neuron.amazonaws.com + pip install --upgrade "neuronx-distributed==0.11.0" --extra-index-url https://pip.repos.neuron.amazonaws.com + pip install --upgrade "neuronx-distributed-inference>=0.2.0" --extra-index-url https://pip.repos.neuron.amazonaws.com + pip install --upgrade transformers accelerate protobuf sentence_transformers tenacity torch-neuron + git clone -b neuron-2.22-vllm-v0.7.2 https://github.com/aws-neuron/upstreaming-to-vllm.git + cd upstreaming-to-vllm + pip install -r requirements-neuron.txt + VLLM_TARGET_DEVICE="neuron" pip install -e . + cd / + python /download_hf_model.py + #python /mllama-offline.py + uvicorn vllm_model_api_m:app --host=0.0.0.0 + resources: + requests: + aws.amazon.com/neuron: 16 + limits: + aws.amazon.com/neuron: 16 + env: + - name: VLLM_NEURON_FRAMEWORK + value: "neuronx-distributed-inference" + - name: MODEL_ID + value: "yahavb/Llama-3.2-11B-Vision-Instruct" + - name: APP + value: "Llama-3.2-11B-Vision-Instruct-NxDI-TRN1" + - name: NODEPOOL + value: "amd-neuron-trn1" + - name: POD_NAME + valueFrom: + fieldRef: + fieldPath: metadata.name + - name: HUGGINGFACE_TOKEN + valueFrom: + secretKeyRef: + name: hf-secrets + key: HUGGINGFACE_TOKEN + - name: MAX_NEW_TOKENS + value: "50" + ports: + - containerPort: 8000 + protocol: TCP + readinessProbe: + failureThreshold: 3 + httpGet: + path: /readiness + port: 8000 + scheme: HTTP + initialDelaySeconds: 280 + periodSeconds: 10 + successThreshold: 1 + timeoutSeconds: 5 + volumes: + - name: vllm-config-volume + configMap: + name: mllama-32-11b-vllm-trn1-config + - name: dshm + emptyDir: + medium: Memory diff --git a/flux_serve/specs/neuron-bits-s3-storage.yaml b/flux_serve/specs/neuron-bits-s3-storage.yaml new file mode 100644 index 0000000..c26379b --- /dev/null +++ b/flux_serve/specs/neuron-bits-s3-storage.yaml @@ -0,0 +1,33 @@ +apiVersion: v1 +kind: PersistentVolume +metadata: + name: neuron-bits-use1-pv +spec: + capacity: + storage: 1200Gi + accessModes: + - ReadWriteMany + storageClassName: "" + claimRef: + namespace: default + name: neuron-bits-use1-pvc + mountOptions: + - region=us-east-1 + csi: + driver: s3.csi.aws.com + volumeHandle: s3-csi-driver-volume + volumeAttributes: + bucketName: neuron-bits-use1 +--- +apiVersion: v1 +kind: PersistentVolumeClaim +metadata: + name: neuron-bits-use1-pvc +spec: + accessModes: + - ReadWriteMany # Supported options: ReadWriteMany / ReadOnlyMany + storageClassName: "" # Required for static provisioning + resources: + requests: + storage: 1200Gi + volumeName: neuron-bits-use1-pv