From ae94f8ec9413228e3b62d18bc911e9f2b1e6ba6c Mon Sep 17 00:00:00 2001 From: jorivesga Date: Mon, 17 Nov 2025 06:05:52 +0200 Subject: [PATCH 1/6] Refm life science SwinUNETR Inference (#459) Add helm charts for the REFM life-science SwinUNETR inference service usecase --- .pre-commit-config.yaml | 8 +- .../examples/demo_inference_service.ipynb | 376 ++++++++++++++++++ .../examples/utils.py | 262 ++++++++++++ .../helm/Chart.yaml | 4 + .../helm/README.md | 68 ++++ .../helm/mount/README.md | 3 + .../helm/mount/data_utils.py | 39 ++ .../helm/mount/entrypoint.sh | 11 + .../helm/mount/inference_service.py | 223 +++++++++++ .../helm/mount/requirements.txt | 11 + .../helm/mount/swinunetr.py | 121 ++++++ .../helm/mount/swinunetr_configuration.py | 94 +++++ .../helm/overrides/kaiwo/kaiwo-enable.yaml | 3 + .../helm/templates/_helpers.tpl | 72 ++++ .../helm/templates/configmap.yaml | 11 + .../helm/templates/deployment.yaml | 96 +++++ .../helm/templates/service.yaml | 22 + .../helm/values.schema.json | 221 ++++++++++ .../helm/values.yaml | 55 +++ 19 files changed, 1696 insertions(+), 4 deletions(-) create mode 100644 workloads/dev-lifescience-swinunetr-inference/examples/demo_inference_service.ipynb create mode 100644 workloads/dev-lifescience-swinunetr-inference/examples/utils.py create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/Chart.yaml create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/README.md create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/mount/README.md create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/mount/data_utils.py create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/mount/entrypoint.sh create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/mount/inference_service.py create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/mount/requirements.txt create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/mount/swinunetr.py create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/mount/swinunetr_configuration.py create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/overrides/kaiwo/kaiwo-enable.yaml create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/templates/_helpers.tpl create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/templates/configmap.yaml create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/templates/deployment.yaml create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/templates/service.yaml create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/values.schema.json create mode 100644 workloads/dev-lifescience-swinunetr-inference/helm/values.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 39675cd..8240e42 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -17,26 +17,26 @@ repos: hooks: - id: black language_version: python3.12 - args: ["--config=pyproject.toml"] + args: [ "--config=pyproject.toml" ] - repo: https://github.com/pycqa/flake8 rev: 7.2.0 hooks: - id: flake8 - args: ["--config=.flake8"] + args: [ "--config=.flake8" ] - repo: https://github.com/pycqa/isort rev: 6.0.1 hooks: - id: isort name: isort (python) - args: ["--settings-path=pyproject.toml"] + args: [ "--settings-path=pyproject.toml" ] - repo: https://github.com/pre-commit/mirrors-mypy rev: v1.16.0 hooks: - id: mypy - args: ["--config-file=pyproject.toml"] + args: [ "--config-file=pyproject.toml", "--install-types", "--non-interactive" ] exclude: kaiwo|mount language_version: python3.12 additional_dependencies: diff --git a/workloads/dev-lifescience-swinunetr-inference/examples/demo_inference_service.ipynb b/workloads/dev-lifescience-swinunetr-inference/examples/demo_inference_service.ipynb new file mode 100644 index 0000000..c3b15d0 --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/examples/demo_inference_service.ipynb @@ -0,0 +1,376 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "df57a5ef-2a09-472b-ab6c-ddbbb6e7f7ce", + "metadata": {}, + "source": [ + "# Demo Inference Service\n", + "\n", + "Demo notebook for sending a prediction request to the deployed inference service, and visualizing the results." + ] + }, + { + "cell_type": "markdown", + "id": "96505e68-1c04-4d77-bc40-cd24bc323933", + "metadata": {}, + "source": [ + "## Requirements" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e18a2528-56e0-4552-a796-a6552892fd55", + "metadata": {}, + "outputs": [], + "source": "! pip install numpy==1.26.4 einops==0.4.1 scipy==1.10.1 connected-components-3d monai[nibabel,pillow,ignite,tqdm,pydicom]====1.5.0 synapseclient" + }, + { + "cell_type": "markdown", + "id": "edf47a9e-51f2-44e6-817d-3b1ff9f35e52", + "metadata": {}, + "source": [ + "## Import utility functions" + ] + }, + { + "cell_type": "code", + "id": "1d7f577c-abe3-4e39-9b04-52029eee283b", + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-04T07:51:09.815819Z", + "start_time": "2025-08-04T07:50:53.040426Z" + } + }, + "source": "from utils import plot_input_scan, send_prediction_request, plot_results, plot_results_overlap", + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "id": "e449c4c2-bf4f-4e3f-88d6-9c89029dfc0c", + "metadata": {}, + "source": [ + "## Dataset\n", + "\n", + "Download scan image from the [Multi-Atlas Labeling Beyond the Cranial Vault - Workshop and Challenge](https://www.synapse.org/Synapse:syn3193805/wiki/89480) dataset.\n", + "\n", + "You must create a Synapse account and get Personal Access Token (PAT) to access the dataset.\n", + "\n", + "```\n", + "pip install synapseclient\n", + "\n", + "synapse get syn3553734\n", + "\n", + "unzip Abdomen.zip\n", + "```\n", + "\n", + "The data contains labels for 13 different organs. Check this link for additional details on the dataset: [Abdomen dataset](https://www.synapse.org/Synapse:syn3193805/wiki/217789).\n", + "\n", + "- (1) spleen\n", + "- (2) right kidney\n", + "- (3) left kidney\n", + "- (4) gallbladder\n", + "- (5) esophagus\n", + "- (6) liver\n", + "- (7) stomach\n", + "- (8) aorta\n", + "- (9) inferior vena cava\n", + "- (10) portal vein and splenic vein\n", + "- (11) pancreas\n", + "- (12) right adrenal gland\n", + "- (13) left adrenal gland" + ] + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Port forwarding to inference service", + "id": "50ff6cfe-1691-4f9a-8406-918d929b3a96" + }, + { + "cell_type": "markdown", + "id": "fd9d0267-fb50-466e-94ed-47689e98f078", + "metadata": {}, + "source": [ + "1. Open your terminal and run the following command:\n", + " \n", + " `kubectl port-forward -n service/dev-lifescience-swinunetr- 8000:80`\n", + "\n", + "2. Keep the Terminal Open: The port-forward command runs in the foreground. You must keep this terminal window open for the connection to remain active." + ] + }, + { + "cell_type": "markdown", + "id": "f1aa4370-b34e-4957-88a2-f58e221c7188", + "metadata": {}, + "source": [ + "## Inference Parameters" + ] + }, + { + "cell_type": "code", + "id": "b4bc3921-c81e-4fa1-829f-d8674d9f793e", + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-04T07:51:09.825745Z", + "start_time": "2025-08-04T07:51:09.821312Z" + } + }, + "source": [ + "# Path to the image file to send for prediction.\n", + "image_path = \"./Abdomen/RawData/Training/img/img0001.nii.gz\"\n", + "\n", + "# Ground truth label file for the image.\n", + "label_path = \"./Abdomen/RawData/Training/label/label0001.nii.gz\"\n", + "\n", + "# Filename to save the output prediction\n", + "output_path = \"client_prediction.nii.gz\"\n", + "\n", + "# URL of the prediction inference service endpoint\n", + "service_url = \"http://localhost:8000/predict/\"" + ], + "outputs": [], + "execution_count": 2 + }, + { + "cell_type": "markdown", + "id": "bf2b1839-c8b0-48f1-8575-8465946c087d", + "metadata": {}, + "source": [ + "## Input view" + ] + }, + { + "cell_type": "code", + "id": "28ecbd0b-a38b-4868-bad7-b6c6af735365", + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-04T07:51:50.219603Z", + "start_time": "2025-08-04T07:51:45.747510Z" + } + }, + "source": [ + "plot_input_scan(input_path=image_path, num_slices_to_plot=3)" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pixdim[0] (qfac) should be 1 (default) or -1; setting qfac to 1\n", + "2025-08-04 10:51:45,765 - INFO - pixdim[0] (qfac) should be 1 (default) or -1; setting qfac to 1\n", + "2025-08-04 10:51:49,784 - INFO - Input data shape: torch.Size([229, 229, 220])\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 3 + }, + { + "cell_type": "markdown", + "id": "d3526241-feb0-4e9a-83c8-30267538f1bd", + "metadata": {}, + "source": [ + "## Send prediction request\n", + "\n", + "Send a CT san to the inference service for prediction:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6351f3f9-304a-48c5-8900-62ab27fd46e7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:utils:Attempting to send ../../data/inputs_nifti/LUNG1-001_input.nii.gz to http://localhost:8001/predict/\n", + "INFO:utils:Status Code: 200\n", + "INFO:utils:Success! Prediction saved to client_prediction.nii.gz\n" + ] + } + ], + "source": [ + "send_prediction_request(image_path=image_path, output_path=output_path, server_url=service_url)" + ] + }, + { + "cell_type": "markdown", + "id": "175b4943-b71a-421a-b821-7986fa9e6c82", + "metadata": {}, + "source": [ + "## Visualize results" + ] + }, + { + "cell_type": "code", + "id": "c6988e85-a0c3-4de0-a1c7-287fb43d13b2", + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-04T07:53:00.108362Z", + "start_time": "2025-08-04T07:52:08.500029Z" + } + }, + "source": [ + "channel_idx = 6\n", + "\n", + "plot_results(image_path, label_path, output_path, channel_idx=channel_idx, num_slices_to_plot=3)" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pixdim[0] (qfac) should be 1 (default) or -1; setting qfac to 1\n", + "2025-08-04 10:52:08,516 - INFO - pixdim[0] (qfac) should be 1 (default) or -1; setting qfac to 1\n", + "pixdim[0] (qfac) should be 1 (default) or -1; setting qfac to 1\n", + "2025-08-04 10:52:10,516 - INFO - pixdim[0] (qfac) should be 1 (default) or -1; setting qfac to 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input data shape: torch.Size([1, 229, 229, 220])\n", + "Label data shape: torch.Size([14, 229, 229, 220])\n", + "Prediction data shape: torch.Size([14, 229, 229, 220])\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 4 + }, + { + "cell_type": "code", + "id": "dc1cc7f2-3d49-40e0-a0d8-19dae6ed4673", + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-04T07:53:41.881454Z", + "start_time": "2025-08-04T07:53:00.124815Z" + } + }, + "source": [ + "slice_to_plot = 110\n", + "\n", + "plot_results_overlap(\n", + " image_path, label_path, output_path, channel_idx=channel_idx, slice_to_plot=slice_to_plot\n", + ")" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pixdim[0] (qfac) should be 1 (default) or -1; setting qfac to 1\n", + "2025-08-04 10:53:00,148 - INFO - pixdim[0] (qfac) should be 1 (default) or -1; setting qfac to 1\n", + "pixdim[0] (qfac) should be 1 (default) or -1; setting qfac to 1\n", + "2025-08-04 10:53:02,367 - INFO - pixdim[0] (qfac) should be 1 (default) or -1; setting qfac to 1\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 5 + }, + { + "cell_type": "code", + "id": "e01e3447-02cf-4d3d-b9f0-9b32dd6a0ec7", + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-04T07:54:25.689698Z", + "start_time": "2025-08-04T07:53:41.990150Z" + } + }, + "source": [ + "channel_idx = 6\n", + "slice_to_plot = 165\n", + "\n", + "plot_results_overlap(\n", + " image_path, label_path, output_path, channel_idx=channel_idx, slice_to_plot=slice_to_plot\n", + ")" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "pixdim[0] (qfac) should be 1 (default) or -1; setting qfac to 1\n", + "2025-08-04 10:53:42,010 - INFO - pixdim[0] (qfac) should be 1 (default) or -1; setting qfac to 1\n", + "pixdim[0] (qfac) should be 1 (default) or -1; setting qfac to 1\n", + "2025-08-04 10:53:44,120 - INFO - pixdim[0] (qfac) should be 1 (default) or -1; setting qfac to 1\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 6 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "194cacfaaa52c3ca" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/workloads/dev-lifescience-swinunetr-inference/examples/utils.py b/workloads/dev-lifescience-swinunetr-inference/examples/utils.py new file mode 100644 index 0000000..05eccc1 --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/examples/utils.py @@ -0,0 +1,262 @@ +import logging +import os +from typing import List + +import matplotlib.pyplot as plt +import numpy as np +import requests +from monai.transforms import AsDiscreted, Compose, EnsureChannelFirstd, LoadImaged, Spacingd + +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") +LOGGER = logging.getLogger(__name__) + + +def send_prediction_request( + image_path: str, output_path: str = "prediction_output.nii.gz", server_url: str = "http://localhost:8000/predict/" +): + """ + Sends an image to the prediction server and saves the result. + + Args: + image_path (str): Path to the input image file (.nii, .nii.gz, or .npy). + output_path (str): Path to save the received prediction file. + server_url (str): URL of the inference service. + """ + if not os.path.exists(image_path): + LOGGER.error(f"Error: Input image file not found at {image_path}") + return + + LOGGER.info(f"Attempting to send {image_path} to {server_url}") + try: + with open(image_path, "rb") as f: + files = {"file": (os.path.basename(image_path), f)} + response = requests.post(server_url, files=files, timeout=120) # 120-second timeout + + LOGGER.info(f"Status Code: {response.status_code}") + + if response.status_code == 200: + with open(output_path, "wb") as out_f: + out_f.write(response.content) + LOGGER.info(f"Success! Prediction saved to {output_path}") + else: + LOGGER.error("Error: Request failed.") + try: + error_detail = response.json() + LOGGER.error(f"Server Error Detail: {error_detail}") + except requests.exceptions.JSONDecodeError: + LOGGER.error(f"Server Error (non-JSON): {response.text}") + + except requests.exceptions.ConnectionError: + LOGGER.error(f"Error: Could not connect to the server at {server_url}. Is it running?") + except requests.exceptions.Timeout: + LOGGER.error("Error: The request timed out.") + except Exception as e: + LOGGER.error(f"An unexpected error occurred: {e}") + + +def load_and_transform_input(keys: List[str], data_dict: dict): + image_transforms = Compose( + [ + LoadImaged(keys=keys), + EnsureChannelFirstd(keys="input", channel_dim="no_channel"), + Spacingd(keys=keys, pixdim=(1.5, 1.5, 2), mode=("bilinear",)), + ] + ) + return image_transforms(data_dict) + + +def load_and_transform(keys: List[str], data_dict: dict): + """ + Loads and transforms data, converting the label to one-hot format. + """ + num_classes = 14 + + image_transforms = Compose( + [ + LoadImaged(keys=keys), + EnsureChannelFirstd(keys=["input", "label"], channel_dim="no_channel"), + AsDiscreted(keys="label", to_onehot=num_classes), + Spacingd(keys=keys, pixdim=(1.5, 1.5, 2.0), mode=("bilinear", "nearest", "nearest")), + ] + ) + return image_transforms(data_dict) + + +def plot_results( + input_path_str: str, + label_path_str: str, + pred_path_str: str, + channel_idx: int, + num_slices_to_plot: int = 4, +): + """ + Plots slices of the input, prediction, and optionally label NIfTI images. + + Args: + input_path_str (str): Path to the original input NIfTI file. + label_path_str (str): Path to the label NIfTI file. + pred_path_str (str): Path to the prediction NIfTI file. + channel_idx (int): Channel/Label to extract from the multichannel label file. + num_slices_to_plot (int): Number of slices to display. + """ + try: + data_dict = {"input": input_path_str, "pred": pred_path_str, "label": label_path_str} + keys = ["input", "label", "pred"] + + processed_dict = load_and_transform(keys=keys, data_dict=data_dict) + + input_data = processed_dict["input"].squeeze() + pred_data = processed_dict["pred"][channel_idx, ...].squeeze() + label_data = processed_dict["label"][channel_idx, ...].squeeze() + + num_cols = 3 + depth = input_data.shape[2] + + slice_indices = np.linspace(depth // 4, 3 * depth // 4, num_slices_to_plot, dtype=int) + slice_indices = np.clip(np.unique(slice_indices), 0, depth - 1) + + fig, axes = plt.subplots(len(slice_indices), num_cols, figsize=(num_cols * 3, len(slice_indices) * 3)) + if len(slice_indices) == 1: + axes = np.array([axes]).reshape(1, -1) + + for i, slice_idx in enumerate(slice_indices): + axes[i, 0].imshow(np.rot90(input_data[:, :, slice_idx]), cmap="gray") + axes[i, 0].set_title(f"Input - Slice {slice_idx}") + axes[i, 0].axis("off") + + axes[i, 1].imshow(np.rot90(label_data[:, :, slice_idx]), cmap="viridis") + axes[i, 1].set_title(f"Label - Slice {slice_idx}") + axes[i, 1].axis("off") + + axes[i, 2].imshow(np.rot90(pred_data[:, :, slice_idx]), cmap="viridis") + axes[i, 2].set_title(f"Prediction - Slice {slice_idx}") + axes[i, 2].axis("off") + + fig.suptitle(f"Input vs. Prediction (vs. Label) - {os.path.basename(input_path_str)}", fontsize=16) + plt.tight_layout(rect=[0, 0, 1, 0.96]) + plt.show() + plt.close(fig) + except Exception as e: + LOGGER.error(f"An error occurred during plotting: {e}", exc_info=True) + + +def plot_results_overlap( + input_path_str: str, + label_path_str: str, + pred_path_str: str, + channel_idx: int, + slice_to_plot: int, +): + """ + Plots a specific slice showing overlaps of input, prediction, and target label. + + This function generates a 1x3 plot for a single specified slice: + 1. Input slice with the target mask superimposed. + 2. Input slice with the prediction mask superimposed. + 3. Input slice with both target and prediction masks superimposed. + + Args: + input_path_str (str): Path to the original input NIfTI file. + label_path_str (str): Path to the label NIfTI file. + pred_path_str (str): Path to the prediction NIfTI file. + channel_idx (int): Labels to extract from the multichannel label file. + slice_to_plot (int): The specific slice index to visualize. + """ + try: + data_dict = {"input": input_path_str, "pred": pred_path_str, "label": label_path_str} + keys = ["input", "label", "pred"] + + processed_dict = load_and_transform(keys=keys, data_dict=data_dict) + + input_data = processed_dict["input"].squeeze() + pred_data = processed_dict["pred"][channel_idx, ...].squeeze() + label_data = processed_dict["label"][channel_idx, ...].squeeze() + + # Validate slice index + if not (0 <= slice_to_plot < input_data.shape[2]): + raise ValueError(f"Slice index {slice_to_plot} is out of bounds for depth {input_data.shape[2]}.") + + input_slice = np.rot90(input_data[:, :, slice_to_plot]) + pred_slice = np.rot90(pred_data[:, :, slice_to_plot]) + label_slice = np.rot90(label_data[:, :, slice_to_plot]) + + # Use a masked array to only show the "on" pixels of the masks + pred_mask = np.ma.masked_where(pred_slice == 0, pred_slice) + label_mask = np.ma.masked_where(label_slice == 0, label_slice) + + fig, axes = plt.subplots(1, 3, figsize=(12, 4)) + + # Plot 1: Input with Target + axes[0].imshow(input_slice, cmap="gray") + axes[0].imshow(label_mask, cmap="autumn", alpha=0.5) # autumn is yellow-red + axes[0].set_title(f"Input + Target (Slice {slice_to_plot})") + axes[0].axis("off") + + # Plot 2: Input with Prediction + axes[1].imshow(input_slice, cmap="gray") + axes[1].imshow(pred_mask, cmap="cool", alpha=0.5) # cool is cyan-magenta + axes[1].set_title(f"Input + Prediction (Slice {slice_to_plot})") + axes[1].axis("off") + + # Plot 3: Input with Target and Prediction + axes[2].imshow(input_slice, cmap="gray") + axes[2].imshow(label_mask, cmap="autumn", alpha=0.7) + axes[2].imshow(pred_mask, cmap="cool", alpha=0.4) + axes[2].set_title("Input + Target (Red) + Pred (Blue)") + axes[2].axis("off") + + # --- 4. Finalize and Show --- + fig.suptitle(f"Overlap Visualization - {os.path.basename(input_path_str)}", fontsize=16) + plt.tight_layout(rect=[0, 0, 1, 0.95]) + plt.show() + plt.close(fig) + except Exception as e: + LOGGER.error(f"An error occurred during overlap plotting: {e}", exc_info=True) + + +def plot_input_scan(input_path: str, num_slices_to_plot: int = 4): + """ + Plots several axial slices of a single NIfTI input scan. + + Args: + input_path (str): Path to the original input NIfTI file. + num_slices_to_plot (int): Number of slices to display. + """ + try: + # 1. Load only the input data + data_dict = {"input": input_path} + processed_dict = load_and_transform_input(keys=["input"], data_dict=data_dict) + input_data = processed_dict["input"].squeeze() + LOGGER.info(f"Input data shape: {input_data.shape}") + depth = input_data.shape[2] + + # 2. Select representative slices to plot + slice_indices = np.linspace(depth // 4, 3 * depth // 4, num_slices_to_plot, dtype=int) + slice_indices = np.clip(np.unique(slice_indices), 0, depth - 1) + + if len(slice_indices) == 0: + LOGGER.error("Could not determine valid slice indices to plot.") + return + + # 3. Create the plot grid + num_cols = int(np.ceil(np.sqrt(len(slice_indices)))) + num_rows = int(np.ceil(len(slice_indices) / num_cols)) + + fig, axes = plt.subplots(num_rows, num_cols, figsize=(num_cols * 3, num_rows * 3)) + # Flatten the axes array to make it easy to iterate over + axes = axes.flatten() + for i, slice_idx in enumerate(slice_indices): + ax = axes[i] + ax.imshow(np.rot90(input_data[:, :, slice_idx]), cmap="gray") + ax.set_title(f"Slice {slice_idx}") + ax.axis("off") + + # Turn off any unused subplots in the grid + for i in range(len(slice_indices), len(axes)): + axes[i].axis("off") + + fig.suptitle(f"Input Scan: {os.path.basename(input_path)}", fontsize=16) + plt.tight_layout(rect=[0, 0, 1, 0.96]) + plt.show() + except Exception as e: + LOGGER.error(f"An error occurred during plotting: {e}", exc_info=True) diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/Chart.yaml b/workloads/dev-lifescience-swinunetr-inference/helm/Chart.yaml new file mode 100644 index 0000000..0218469 --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/Chart.yaml @@ -0,0 +1,4 @@ +apiVersion: v2 +name: dev-lifescience-swinunetr-inference +description: A Helm chart for SwinUNETR inference +version: 0.0.1 diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/README.md b/workloads/dev-lifescience-swinunetr-inference/helm/README.md new file mode 100644 index 0000000..6ae0348 --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/README.md @@ -0,0 +1,68 @@ +# Life Science - SwinUNETR Inference + +This Helm Chart deploys a [SwinUNETR](https://arxiv.org/abs/2201.01266) model as an Inference Service, for multiorgan segmentation of 3D CT scans. + +## SwinUNETR model + +[SwinUNETR](https://arxiv.org/abs/2201.01266) is a deep learning architecture designed for medical image segmentation, particularly in 3D volumetric data +such as CT or MRI scans with the aim to detect tumors in the images. It combines the strengths of two powerful +models: 1. Swin Transformer - a hierarchical vision transformer that captures long-range dependencies and contextual +information efficiently and 2. UNETR (UNet with Transformers) - a transformer-based encoder-decoder architecture +tailored for medical image segmentation. + +Model weights are loaded from [HuggingFace](https://huggingface.co/darragh/swinunetr-btcv-base). + +Check out the [demo_inference_service.ipynb](../examples/demo_inference_service.ipynb) example to see how it works. + +## Data + +The training data are 3D CT scans from the [BTCV challenge dataset](https://www.synapse.org/#!Synapse:syn3193805/wiki/217752). The target segmentation includes 13 abdominal organs: + +1. Spleen +2. Right Kidney +3. Left Kideny +4. Gallbladder +5. Esophagus +6. Liver +7. Stomach +8. Aorta +9. IVC +10. Portal and Splenic Veins +11. Pancreas +12. Right adrenal gland +13. Left adrenal gland + + +## Prerequisites + +Ensure the following prerequisites are met before deploying any workloads: + +1. **Helm**: Install `helm`. Refer to the [Helm documentation](https://helm.sh/) for instructions. + +## Deploying the Workload + +It is recommended to use `helm template` and pipe the result to `kubectl create` , rather than using `helm install`. Generally, a command looks as follows + +```bash +helm template [your-release-name] ./helm | kubectl apply -f - +``` + +The chart provides three main ways to deploy models, detailed below. + +## User Input Values + +Refer to the `values.yaml` file for the user input values you can provide, along with instructions. + +## Interacting with Deployed Model + +### Verify Deployment + +Check the deployment status: + +```bash +kubectl get deployment +``` + +### Send prediction request + +Follow the [demo_inference_service.ipynb](../examples/demo_inference_service.ipynb) notebook to see how to use the inference service. diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/mount/README.md b/workloads/dev-lifescience-swinunetr-inference/helm/mount/README.md new file mode 100644 index 0000000..75734b3 --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/mount/README.md @@ -0,0 +1,3 @@ +Files in this directory are mounted to the workload at `/workload/mount`. + +**Note:** Subdirectories and binary files are not supported. diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/mount/data_utils.py b/workloads/dev-lifescience-swinunetr-inference/helm/mount/data_utils.py new file mode 100644 index 0000000..976f9c4 --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/mount/data_utils.py @@ -0,0 +1,39 @@ +import logging + +from monai import transforms + +LOGGER = logging.getLogger(__name__) + +IMAGE_DATA = "image" +SPACING_MODE = "bilinear" +DEVICE = "cuda" + + +def get_transforms(args): + """Returns the transforms to be applied to the input data.""" + inference_transforms = transforms.Compose( + [ + transforms.LoadImaged(keys=[IMAGE_DATA]), + transforms.EnsureChannelFirstd(keys=[IMAGE_DATA], channel_dim="no_channel"), + transforms.Spacingd( + keys=[IMAGE_DATA], pixdim=(args.space_x, args.space_y, args.space_z), mode=SPACING_MODE + ), + transforms.ScaleIntensityRanged( + keys=[IMAGE_DATA], a_min=args.a_min, a_max=args.a_max, b_min=args.b_min, b_max=args.b_max, clip=True + ), + ] + ) + return inference_transforms + + +def get_post_transforms_inverter(forward_transforms_obj): + """Transform to revert all transforms previously applied.""" + return transforms.Invertd( + keys=IMAGE_DATA, + transform=forward_transforms_obj, + orig_keys=IMAGE_DATA, + orig_meta_keys=f"{IMAGE_DATA}_meta_dict", + nearest_interp=True, + to_tensor=True, + device=DEVICE, + ) diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/mount/entrypoint.sh b/workloads/dev-lifescience-swinunetr-inference/helm/mount/entrypoint.sh new file mode 100644 index 0000000..b3ec4a7 --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/mount/entrypoint.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +# Exit immediately if a command exits with a non-zero status +set -e + +# Change directory to the location of this script +cd "$(dirname "$0")" + +pip install --no-cache-dir -r requirements.txt + +python inference_service.py diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/mount/inference_service.py b/workloads/dev-lifescience-swinunetr-inference/helm/mount/inference_service.py new file mode 100644 index 0000000..d788664 --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/mount/inference_service.py @@ -0,0 +1,223 @@ +import argparse +import logging +import os +import tempfile +import traceback +from contextlib import asynccontextmanager, nullcontext + +import nibabel as nib +import numpy as np +import torch +import uvicorn +from data_utils import DEVICE, IMAGE_DATA, get_post_transforms_inverter, get_transforms +from fastapi import FastAPI, File, HTTPException, UploadFile +from fastapi.responses import FileResponse +from monai import transforms +from swinunetr import SwinUnetrModelForInference +from torch.cuda.amp import autocast + +LOGGER = logging.getLogger(__name__) + +# Read configuration from environment variables with fallbacks +ARGS = argparse.Namespace( + hf_model=os.environ.get("HF_MODEL", "darragh/swinunetr-btcv-base"), + roi_x=int(os.environ.get("ROI_X", "96")), + roi_y=int(os.environ.get("ROI_Y", "96")), + roi_z=int(os.environ.get("ROI_Z", "96")), + space_x=float(os.environ.get("SPACE_X", "1.5")), + space_y=float(os.environ.get("SPACE_Y", "1.5")), + space_z=float(os.environ.get("SPACE_Z", "2.0")), + a_min=float(os.environ.get("A_MIN", "-175.0")), + a_max=float(os.environ.get("A_MAX", "250.0")), + b_min=float(os.environ.get("B_MIN", "0.0")), + b_max=float(os.environ.get("B_MAX", "1.0")), + infer_overlap=float(os.environ.get("INFER_OVERLAP", "0.5")), + compile=os.environ.get("COMPILE", "false").lower() == "true", + compile_mode=os.environ.get("COMPILE_MODE", "max-autotune"), + autocast=os.environ.get("AUTOCAST", "false").lower() == "true", +) + +MODEL: SwinUnetrModelForInference | None = None +IMAGE_TRANSFORMS: transforms.Compose | None = None +POST_TRANSFORMS_INVERTER: transforms.Compose | None = None +AUTOCAST_CONTEXT = nullcontext() + + +def load_model(args): + """Loads the pre-trained model.""" + global MODEL, IMAGE_TRANSFORMS, POST_TRANSFORMS_INVERTER, AUTOCAST_CONTEXT + MODEL = SwinUnetrModelForInference.from_pretrained(args.hf_model) + + if args.compile: + LOGGER.info(f"Compiling model to {args.compile_mode}") + MODEL = torch.compile(MODEL, mode=args.compile_mode, dynamic=False) + LOGGER.info("Model compiled successfully.") + + # Set the model to evaluation mode + MODEL.eval() + MODEL.to(DEVICE) + LOGGER.info(f"Model loaded on {DEVICE}.") + + # Configure autocast context once during model loading + if args.autocast: + LOGGER.info("Configuring autocast for inference") + AUTOCAST_CONTEXT = autocast() + else: + AUTOCAST_CONTEXT = nullcontext() + + IMAGE_TRANSFORMS = get_transforms(args=args) + POST_TRANSFORMS_INVERTER = get_post_transforms_inverter(IMAGE_TRANSFORMS) + LOGGER.info("Image transform pipeline initialized.") + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Load model on server startup.""" + LOGGER.info("Server startup: Loading model...") + global MODEL, IMAGE_TRANSFORMS, POST_TRANSFORMS_INVERTER, AUTOCAST_CONTEXT + try: + load_model(ARGS) + except Exception as e: + LOGGER.error(f"Error loading model during startup: {e}") + LOGGER.error(f"Traceback: \n{traceback.format_exc()}") + yield + + MODEL = None + IMAGE_TRANSFORMS = None + POST_TRANSFORMS_INVERTER = None + AUTOCAST_CONTEXT = nullcontext() + + +app = FastAPI(title="SwinUNETR Inference Service", lifespan=lifespan) + + +@app.get("/health", status_code=200) +async def health_check(): + """ + Simple health check endpoint. + Returns 200 OK if the model is loaded and the server is running. + """ + if MODEL is None or IMAGE_TRANSFORMS is None: + raise HTTPException( + status_code=503, detail="Model not loaded. Server is not ready yet." # 503 Service Unavailable + ) + return {"status": "ok"} + + +@app.post("/predict/", response_class=FileResponse) +async def predict(file: UploadFile = File(...)): + """ + Accepts an image file (NIfTI or .npy), performs inference, + and returns the segmentation mask as a NIfTI file. + """ + if MODEL is None or IMAGE_TRANSFORMS is None: + raise HTTPException( + status_code=503, detail="Model not loaded. Server might be starting or encountered an error." + ) + + try: + original_filename = file.filename + file_suffix = "" + if original_filename.endswith(".nii.gz"): + file_suffix = ".nii.gz" + elif original_filename.endswith(".nii"): + file_suffix = ".nii" + elif original_filename.endswith(".npy"): + file_suffix = ".npy" + else: + raise HTTPException(status_code=400, detail="Unsupported file type. Use .nii, .nii.gz, or .npy") + + with tempfile.NamedTemporaryFile(delete=False, suffix=file_suffix) as tmp_file: + content = await file.read() + tmp_file.write(content) + tmp_file_path = tmp_file.name + + LOGGER.info(f"Temporary file saved at: {tmp_file_path} for original: {original_filename}") + + original_affine = np.eye(4) # Default for npy + original_shape_3d = None + img_data_np = None + if file_suffix in [".nii", ".nii.gz"]: + nib_image = nib.load(tmp_file_path) + img_data_np = np.asarray(nib_image.dataobj, dtype=np.float32) + original_affine = nib_image.affine.astype(np.float32) + original_shape_3d = img_data_np.shape + elif file_suffix == ".npy": + img_data_np = np.load(tmp_file_path).astype(np.float32) + original_shape_3d = img_data_np.shape + LOGGER.warning( + "Using identity affine for .npy input. Inverse transform might not perfectly restore original physical space if original .npy had specific spacing." + ) + + # Prepare data dictionary for MONAI transforms + data_dict = {IMAGE_DATA: tmp_file_path} + # Apply transforms + val_input_transformed_dict = IMAGE_TRANSFORMS(data_dict) + + val_inputs = val_input_transformed_dict[IMAGE_DATA] + val_inputs = val_inputs.unsqueeze(0).to(DEVICE) + LOGGER.info(f"Shape transformed inputs: {val_inputs.shape}") + + LOGGER.info("Predicting...") + with torch.inference_mode(), AUTOCAST_CONTEXT: + logits = MODEL.forward( + inputs=val_inputs, + roi_size=(ARGS.roi_x, ARGS.roi_y, ARGS.roi_z), + sw_batch_size=4, + overlap=ARGS.infer_overlap, + mode="gaussian", + ) + LOGGER.info(f"Shape of prediction before inversion: {logits.shape}") + + # Post-processing and transforms inversion + invert_dict = { + IMAGE_DATA: logits[0], + } + inverted = POST_TRANSFORMS_INVERTER(invert_dict) + inverted_logits = inverted[IMAGE_DATA] + LOGGER.info(f"Shape of prediction after inverse transforms: {inverted_logits.shape}") + + probs = torch.sigmoid(inverted_logits) + LOGGER.info(f"Max/Min probs: {probs.max()} / {probs.min()}") + seg = (probs > 0.5).float() + prediction_np = seg.cpu().numpy().squeeze() + LOGGER.info(f"Final prediction shape: {prediction_np.shape}") + + # Save prediction to a NIfTI file + output_affine = inverted[IMAGE_DATA].affine.cpu().numpy() + pred_nifti = nib.Nifti1Image(prediction_np, output_affine) + + with tempfile.NamedTemporaryFile(delete=False, suffix="_prediction.nii.gz") as pred_output_file: + nib.save(pred_nifti, pred_output_file.name) + response_file_path = pred_output_file.name + + os.unlink(tmp_file_path) + + return FileResponse(response_file_path, media_type="application/gzip", filename="prediction.nii.gz") + + except HTTPException as http_exc: + raise http_exc + except Exception as e: + if "tmp_file_path" in locals() and os.path.exists(tmp_file_path): + os.unlink(tmp_file_path) + if "response_file_path" in locals() and os.path.exists(response_file_path): + os.unlink(response_file_path) + LOGGER.error(f"Error during prediction: {e}") + LOGGER.error(f"Traceback: \n{traceback.format_exc()}") + raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}") + + finally: + if os.path.exists(tmp_file_path): + os.unlink(tmp_file_path) + LOGGER.info(f"Temporary input file {tmp_file_path} unlinked.") + + +if __name__ == "__main__": + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + handlers=[logging.StreamHandler()], + ) + + port = int(os.environ.get("PORT", "8000")) + uvicorn.run(app, host="0.0.0.0", port=port) diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/mount/requirements.txt b/workloads/dev-lifescience-swinunetr-inference/helm/mount/requirements.txt new file mode 100644 index 0000000..67a7e1e --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/mount/requirements.txt @@ -0,0 +1,11 @@ +connected-components-3d==3.24.0 +einops==0.4.1 +fastapi==0.115.13 +monai[nibabel,pillow,ignite,tqdm,pydicom]==1.5.0 +numpy==1.26.4 +python-multipart==0.0.20 +scipy +tensorboard==2.13.0 +tensorboardX==2.1 +transformers==4.54.1 +uvicorn==0.34.3 diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/mount/swinunetr.py b/workloads/dev-lifescience-swinunetr-inference/helm/mount/swinunetr.py new file mode 100644 index 0000000..bf1049b --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/mount/swinunetr.py @@ -0,0 +1,121 @@ +from typing import Sequence, Union + +import torch +from monai.inferers import sliding_window_inference +from monai.networks.nets import SwinUNETR +from monai.utils import BlendMode +from swinunetr_configuration import SwinUnetrConfig +from torch import nn +from transformers.modeling_utils import ( + PreTrainedModel, +) +from transformers.utils import logging + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "darragh/swinunetr-btcv-tiny" +_CONFIG_FOR_DOC = "swinunetrConfig" + +SWINUNETR_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "swinunetr-btcv-tiny", + "swinunetr-btcv-small", + "swinunetr-btcv-base", +] + + +class SwinUnetrPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = SwinUnetrConfig + base_model_prefix = "swinunetr" + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=0.02) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +class SwinUnetrModelForInference(SwinUnetrPreTrainedModel): + """ + Swin UNETR based on: "Hatamizadeh et al., + Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images + " + Source : https://docs.monai.io/en/stable/_modules/monai/networks/nets/swin_unetr.html + """ + + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + + self.config = config + + self.model = SwinUNETR( + in_channels=config.in_channels, + out_channels=config.out_channels, + depths=config.depths, + num_heads=config.num_heads, + feature_size=config.feature_size, + norm_name=config.norm_name, + drop_rate=config.drop_rate, + attn_drop_rate=config.attn_drop_rate, + dropout_path_rate=config.dropout_path_rate, + normalize=config.normalize, + use_checkpoint=config.use_checkpoint, + spatial_dims=config.spatial_dims, + ) + + self.init_weights() + + def forward( + self, + inputs: torch.Tensor, + roi_size: Union[Sequence[int], int], + sw_batch_size: int, + overlap: float = 0.25, + mode: Union[BlendMode, str] = BlendMode.CONSTANT, + ): + r""" + Sliding window inference on `inputs` with `predictor`. + + The outputs of `predictor` could be a tensor, a tuple, or a dictionary of tensors. + Each output in the tuple or dict value is allowed to have different resolutions with respect to the input. + e.g., the input patch spatial size is [128,128,128], the output (a tuple of two patches) patch sizes + could be ([128,64,256], [64,32,128]). + In this case, the parameter `overlap` and `roi_size` need to be carefully chosen to ensure the output ROI is still + an integer. If the predictor's input and output spatial sizes are not equal, we recommend choosing the parameters + so that `overlap*roi_size*output_size/input_size` is an integer (for each spatial dimension). + + When roi_size is larger than the inputs' spatial size, the input image are padded during inference. + To maintain the same spatial sizes, the output image will be cropped to the original input size. + + Args: + inputs: input image to be processed (assuming NCHW[D]) + roi_size: the spatial window size for inferences. + When its components have None or non-positives, the corresponding inputs dimension will be used. + if the components of the `roi_size` are non-positive values, the transform will use the + corresponding components of img size. For example, `roi_size=(32, -1)` will be adapted + to `(32, 64)` if the second spatial dimension size of img is `64`. + sw_batch_size: the batch size to run window slices. + overlap: Amount of overlap between scans. + mode: {``"constant"``, ``"gaussian"``} + How to blend output of overlapping windows. Defaults to ``"constant"``. + + - ``"constant``": gives equal weight to all predictions. + - ``"gaussian``": gives less weight to predictions on edges of windows. + kwargs: optional keyword args to be passed to ``predictor``. + + Note: + - input must be channel-first and have a batch dim, supports N-D sliding window. + + """ + + return sliding_window_inference(inputs, roi_size, sw_batch_size, self.model, overlap, mode) diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/mount/swinunetr_configuration.py b/workloads/dev-lifescience-swinunetr-inference/helm/mount/swinunetr_configuration.py new file mode 100644 index 0000000..c94b60f --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/mount/swinunetr_configuration.py @@ -0,0 +1,94 @@ +# coding=utf-8 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Swin Unnetr configuration""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + +logger = logging.get_logger(__name__) + +SWINUNETR_PRETRAINED_CONFIG_ARCHIVE_MAP = { + "swinunetr-btcv-tiny": "https://huggingface.co/darragh/swinunetr-btcv-tiny/raw/main/config.json", + "swinunetr-btcv-small": "https://huggingface.co/darragh/swinunetr-btcv-small/raw/main/config.json", + "swinunetr-btcv-base": "https://huggingface.co/darragh/swinunetr-btcv-base/raw/main/config.json", +} + + +class SwinUnetrConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a :class:`~transformers.BertModel` or a + :class:`~transformers.TFBertModel`. It is used to instantiate a model according to the specified arguments, + defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration + to that of the BERT `bert-base-uncased `__ architecture. + + Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model + outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. + + + Args: + in_channels: dimension of input channels. + out_channels: dimension of output channels. + feature_size: dimension of network feature size. + depths: number of layers in each stage. + num_heads: number of attention heads. + norm_name: feature normalization type and arguments. + drop_rate: dropout rate. + attn_drop_rate: attention dropout rate. + dropout_path_rate: drop path rate. + normalize: normalize output intermediate features in each stage. + use_checkpoint: use gradient checkpointing for reduced memory usage. + spatial_dims: number of spatial dims. + + Examples:: + + >>> TBD + """ + + model_type = "swin" + + def __init__( + self, + architecture="SwinUNETR", + img_size=96, + in_channels=1, + out_channels=14, + depths=(2, 2, 2, 2), + num_heads=(3, 6, 12, 24), + feature_size=12, + norm_name="instance", + drop_rate=0.0, + attn_drop_rate=0.0, + dropout_path_rate=0.0, + normalize=True, + use_checkpoint=False, + spatial_dims=3, + **kwargs, + ): + super().__init__( + architecture=architecture, + img_size=img_size, + in_channels=in_channels, + out_channels=out_channels, + depths=depths, + num_heads=num_heads, + feature_size=feature_size, + norm_name=norm_name, + drop_rate=drop_rate, + attn_drop_rate=attn_drop_rate, + dropout_path_rate=dropout_path_rate, + normalize=normalize, + use_checkpoint=use_checkpoint, + spatial_dims=spatial_dims, + **kwargs, + ) diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/overrides/kaiwo/kaiwo-enable.yaml b/workloads/dev-lifescience-swinunetr-inference/helm/overrides/kaiwo/kaiwo-enable.yaml new file mode 100644 index 0000000..e6d278a --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/overrides/kaiwo/kaiwo-enable.yaml @@ -0,0 +1,3 @@ +# kaiwo settings (if enabled, use kaiwo CRDs to have kaiwo operator manage the workload) +kaiwo: + enabled: true diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/templates/_helpers.tpl b/workloads/dev-lifescience-swinunetr-inference/helm/templates/_helpers.tpl new file mode 100644 index 0000000..d9d5d8f --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/templates/_helpers.tpl @@ -0,0 +1,72 @@ +# Release name helper +{{- define "release.name" -}} +{{- default .Chart.Name .Values.nameOverride | trunc 63 | trimSuffix "-" -}} +{{- end -}} + +# Release fullname helper +{{- define "release.fullname" -}} +{{- $currentTime := now | date "20060102-1504" -}} +{{- if .Values.fullnameOverride -}} +{{- .Values.fullnameOverride | trunc 63 | trimSuffix "-" -}} +{{- else -}} +{{- if ne .Release.Name "release-name" -}} +{{- include "release.name" . }}-{{ .Release.Name | trunc 63 | trimSuffix "-" -}} +{{- else -}} +{{- include "release.name" . }}-{{ $currentTime | lower | trunc 63 | trimSuffix "-" -}} +{{- end -}} +{{- end -}} +{{- end -}} + +# Container resources helper +{{- define "container.resources" -}} +requests: + {{- if .Values.gpus }} + amd.com/gpu: "{{ .Values.gpus }}" + {{- end }} + {{- if .Values.ephemeral_storage }} + ephemeral-storage: "{{ .Values.ephemeral_storage }}" + {{- end }} +limits: + {{- if .Values.gpus }} + amd.com/gpu: "{{ .Values.gpus }}" + {{- end }} + {{- if .Values.ephemeral_storage }} + ephemeral-storage: "{{ .Values.ephemeral_storage }}" + {{- end }} +{{- end -}} + +# Container volume mounts helper +{{- define "container.volumeMounts" -}} +- mountPath: /workload/mount + name: workload-mount +- mountPath: /dev/shm + name: dshm +{{- end -}} + +# Container volumes helper +{{- define "container.volumes" -}} +- name: dshm + emptyDir: + medium: Memory + sizeLimit: {{ .Values.storage.dshm.sizeLimit }} +- configMap: + name: {{ include "release.fullname" . }} + defaultMode: 0755 + name: workload-mount +{{- end -}} + +# Container environment variables helper +{{- define "container.env" -}} +{{- range $key, $value := .Values.env_vars }} +{{- if (kindIs "map" $value) }} +- name: {{ $key }} + valueFrom: + secretKeyRef: + name: {{ $value.name }} + key: {{ $value.key }} +{{- else }} +- name: {{ $key }} + value: {{ $value | quote }} +{{- end }} +{{- end }} +{{- end -}} diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/templates/configmap.yaml b/workloads/dev-lifescience-swinunetr-inference/helm/templates/configmap.yaml new file mode 100644 index 0000000..db5a6c7 --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/templates/configmap.yaml @@ -0,0 +1,11 @@ +apiVersion: v1 +kind: ConfigMap +metadata: + name: {{ include "release.fullname" . }} +data: +{{- $files := .Files }} +{{- range $path, $_ := .Files.Glob "mount/*" }} + {{ $key := $path | trimPrefix "mount/" }} + {{- $key }}: | +{{ $files.Get $path | indent 4 }} +{{- end }} diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/templates/deployment.yaml b/workloads/dev-lifescience-swinunetr-inference/helm/templates/deployment.yaml new file mode 100644 index 0000000..c0a803f --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/templates/deployment.yaml @@ -0,0 +1,96 @@ +{{- define "deployment" -}} +apiVersion: apps/v1 +kind: Deployment +metadata: + name: {{ include "release.fullname" . }} + labels: + app: {{ include "release.fullname" . }} + {{- range $key, $value := .Values.metadata.labels }} + {{ $key }}: {{ $value | quote }} + {{- end }} +spec: + replicas: 1 + selector: + matchLabels: + app: {{ include "release.fullname" . }} + template: + metadata: + labels: + app: {{ include "release.fullname" . }} + spec: + {{- if .Values.nodeSelector }} + nodeSelector: + {{- .Values.nodeSelector | toYaml | nindent 8 }} + {{- end }} + {{- if .Values.imagePullSecrets }} + imagePullSecrets: + {{- range .Values.imagePullSecrets }} + - name: {{ . }} + {{- end }} + {{- end }} + containers: + - name: {{ .Chart.Name }} + {{- if .Values.entrypoint }} + command: ["sh", "-c"] + args: + - | + {{- .Values.entrypoint | nindent 12 }} + {{- end }} + {{- if .Values.env_vars }} + env: + {{- include "container.env" . | nindent 12 }} + {{- end }} + image: {{ .Values.image | quote}} + imagePullPolicy: {{ default "Always" .Values.imagePullPolicy | quote }} + ports: + {{- range $key, $value := .Values.deployment.ports }} + - name: {{ $key }} + containerPort: {{ $value }} + {{- end }} + {{- if .Values.livenessProbe }} + livenessProbe: + {{- .Values.livenessProbe | toYaml | nindent 12 -}} + {{- end }} + {{- if .Values.readinessProbe }} + readinessProbe: + {{- .Values.readinessProbe | toYaml | nindent 12 -}} + {{- end }} + {{- if .Values.startupProbe }} + startupProbe: + {{- .Values.startupProbe | toYaml | nindent 12 -}} + {{- end }} + resources: + {{- include "container.resources" . | nindent 12 }} + volumeMounts: + {{- include "container.volumeMounts" . | nindent 12 }} + volumes: + {{- include "container.volumes" . | nindent 8 }} +{{- end -}} + +{{- define "deployment_stripped" -}} +{{- $deployment := include "deployment" . | fromYaml }} +{{- $ := unset $deployment "metadata" }} +{{- $ := unset $deployment.spec.template "metadata" }} +{{- $deployment | toYaml }} +{{- end -}} + +{{- define "deployment_wrapped_with_kaiwoservice" -}} +apiVersion: kaiwo.silogen.ai/v1alpha1 +kind: KaiwoService +metadata: + name: {{ include "release.fullname" . }} + labels: + app: {{ include "release.fullname" . }} + {{- range $key, $value := .Values.metadata.labels }} + {{ $key }}: {{ $value | quote }} + {{- end }} +spec: + deployment: + {{- include "deployment" . | nindent 4 }} +{{- end -}} + +{{- if .Values.kaiwo.enabled -}} +{{- include "deployment_wrapped_with_kaiwoservice" . }} +{{- else -}} +{{- include "deployment" . }} +{{- end -}} diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/templates/service.yaml b/workloads/dev-lifescience-swinunetr-inference/helm/templates/service.yaml new file mode 100644 index 0000000..e4968e9 --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/templates/service.yaml @@ -0,0 +1,22 @@ +apiVersion: v1 +kind: Service +metadata: + name: {{ include "release.fullname" . }} + labels: + app: {{ include "release.fullname" . }} +spec: + type: ClusterIP + ports: + {{ range $name, $port := .Values.deployment.ports }} + {{- if ne $name "http" }} + - name: {{ $name }} + port: {{ $port }} + targetPort: {{ $port }} + {{- else -}} + - name: {{ $name }} + port: 80 + targetPort: {{ $port }} + {{- end }} + {{- end }} + selector: + app: {{ include "release.fullname" . }} diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/values.schema.json b/workloads/dev-lifescience-swinunetr-inference/helm/values.schema.json new file mode 100644 index 0000000..cf2637c --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/values.schema.json @@ -0,0 +1,221 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "type": "object", + "properties": { + "metadata": { + "type": "object", + "description": "Metadata for the deployment", + "properties": { + "labels": { + "type": "object", + "description": "Labels to apply to the deployment", + "additionalProperties": { + "type": "string" + } + } + }, + "required": ["labels"] + }, + "image": { + "type": "string", + "description": "Docker image to use for the deployment" + }, + "imagePullPolicy": { + "type": "string", + "description": "Image pull policy", + "enum": ["Always", "IfNotPresent", "Never"] + }, + "imagePullSecrets": { + "type": "array", + "description": "Image pull secrets for private registries" + }, + "entrypoint": { + "type": "string", + "description": "Entrypoint for the container" + }, + "gpus": { + "type": "integer", + "description": "Number of GPUs to allocate", + "minimum": 1 + }, + "ephemeral_storage": { + "type": "string", + "description": "Ephemeral storage space written with as Gi", + "minimum": 1 + }, + "env_vars": { + "type": "object", + "description": "Environment variables for the container", + "properties": { + "HF_MODEL": { + "type": "string", + "description": "Hugging Face model name" + }, + "ROI_X": { + "type": "integer", + "description": "Region of interest X dimension" + }, + "ROI_Y": { + "type": "integer", + "description": "Region of interest Y dimension" + }, + "ROI_Z": { + "type": "integer", + "description": "Region of interest Z dimension" + }, + "SPACE_X": { + "type": "number", + "description": "Spacing in X dimension" + }, + "SPACE_Y": { + "type": "number", + "description": "Spacing in Y dimension" + }, + "SPACE_Z": { + "type": "number", + "description": "Spacing in Z dimension" + }, + "A_MIN": { + "type": "number", + "description": "Minimum value for normalization" + }, + "A_MAX": { + "type": "number", + "description": "Maximum value for normalization" + }, + "B_MIN": { + "type": "number", + "description": "Minimum value for normalization range" + }, + "B_MAX": { + "type": "number", + "description": "Maximum value for normalization range" + }, + "INFER_OVERLAP": { + "type": "number", + "description": "Inference overlap value" + }, + "COMPILE": { + "type": "string", + "description": "Whether to compile the model", + "enum": ["true", "false"] + }, + "COMPILE_MODE": { + "type": "string", + "description": "Compilation mode for the model" + }, + "AUTOCAST": { + "type": "string", + "description": "Whether to use autocast", + "enum": ["true", "false"] + } + }, + "additionalProperties": { + "type": "string" + } + }, + "vllm_engine_args": { + "type": "object", + "description": "Arguments for the vllm engine", + "additionalProperties": { + "type": "string" + } + }, + "storage": { + "type": "object", + "description": "Storage configuration", + "properties": { + "dshm": { + "type": "object", + "description": "Shared memory configuration", + "properties": { + "sizeLimit": { + "type": "string", + "description": "Size limit for shared memory" + } + }, + "required": ["sizeLimit"] + } + }, + "required": ["dshm"] + }, + "volumes": { + "type": "array", + "description": "Custom volumes to mount from secrets or configmaps.", + "items": { + "type": "object", + "properties": { + "name": { + "type": "string" + }, + "mountPath": { + "type": "string" + }, + "secret": { + "type": "object", + "properties": { + "secretName": { + "type": "string" + } + }, + "required": ["secretName"] + } + }, + "required": ["name", "mountPath"] + } + }, + "deployment": { + "type": "object", + "description": "Deployment configuration", + "properties": { + "ports": { + "type": "object", + "description": "Ports for the deployment", + "properties": { + "http": { + "type": "integer", + "description": "HTTP port for the deployment" + } + }, + "required": ["http"] + } + }, + "required": ["ports"] + }, + "nodeSelector": { + "type": "object", + "properties": { + "dev": { + "type": "string", + "description": "If true, use the dev node selector" + } + } + }, + "kaiwo": { + "type": "object", + "properties": { + "enabled": { + "type": "boolean", + "description": "If true, use Kaiwo CRDs to have Kaiwo operator manage the workload" + } + } + }, + "startupProbe": { + "type": ["object"], + "additionalProperties": true, + "description": "Startup probe configuration for the container" + }, + "livenessProbe": { + "type": ["object"], + "additionalProperties": true, + "description": "Liveness probe configuration for the container" + }, + "readinessProbe": { + "type": ["object"], + "additionalProperties": true, + "description": "Readiness probe configuration for the container" + } + }, + "required": ["metadata", "image", "imagePullPolicy", "gpus", "ephemeral_storage", "env_vars", "storage", "deployment", "kaiwo"], + "additionalProperties": false +} diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/values.yaml b/workloads/dev-lifescience-swinunetr-inference/helm/values.yaml new file mode 100644 index 0000000..567840c --- /dev/null +++ b/workloads/dev-lifescience-swinunetr-inference/helm/values.yaml @@ -0,0 +1,55 @@ +metadata: + labels: {} + +image: rocm/pytorch:rocm7.0_ubuntu24.04_py3.12_pytorch_release_2.6.0 +imagePullPolicy: Always + +gpus: 1 +ephemeral_storage: 128Gi + +entrypoint: | + bash /workload/mount/entrypoint.sh + +env_vars: + HF_MODEL: "darragh/swinunetr-btcv-base" + ROI_X: 96 + ROI_Y: 96 + ROI_Z: 96 + SPACE_X: 1.5 + SPACE_Y: 1.5 + SPACE_Z: 2.0 + A_MIN: -175.0 + A_MAX: 250.0 + B_MIN: 0.0 + B_MAX: 1.0 + INFER_OVERLAP: 0.5 + COMPILE: "false" + COMPILE_MODE: "max-autotune" + AUTOCAST: "false" + +storage: + dshm: + sizeLimit: 32Gi + +deployment: + ports: + http: 8000 + +startupProbe: + httpGet: + path: /health + port: http + failureThreshold: 60 + periodSeconds: 10 +livenessProbe: + httpGet: + path: /health + port: http +readinessProbe: + httpGet: + path: /health + port: http + +# kaiwo settings (if enabled, use kaiwo CRDs to have kaiwo operator manage the workload) +kaiwo: + enabled: false From 26705671adf035588f56e1693de3cda2a413fba5 Mon Sep 17 00:00:00 2001 From: aivanni <4340981+aivanni@users.noreply.github.com> Date: Mon, 17 Nov 2025 12:13:16 +0200 Subject: [PATCH 2/6] Make fixes to Megatron-LM checkpoint processing and minor improvements (#463) * make fixes to checkpoint processing and minor improvements * Update readme of the multinode Megatron pretrain * update tutorials with debug info * change path to trained model in tutorial 04 megatron inference wl override * fix path to set-env-vars.sh * fix token alignment in inference * precommit hooks * update inference patch * fix patch for inference --- ...-deliver-resources-and-run-megatron-cpt.md | 14 ++ ...a70b-and-run-megatron-cpt-with-tp8-ddp2.md | 14 ++ .../helm/mount/Megatron-LM-inference.patch | 237 ++++++++++++++++++ .../megatron-lm-inference-llama3-1-70b.patch | 17 -- .../overrides/tutorial-04-llama-3-1-70b.yaml | 2 +- .../helm/templates/deployment.yaml | 2 +- .../helm/mount/Megatron-LM.patch | 103 +++++++- .../helm/templates/conversion-job.yaml | 7 +- .../helm/values.yaml | 6 + .../helm/README.md | 15 +- .../helm/mount/Megatron-LM.patch | 143 +++++++++++ .../helm/mount/ray_entrypoint.py | 4 + .../helm/mount/Megatron-LM.patch | 110 +++++++- .../helm/mount/train-cpt.sh | 3 +- 14 files changed, 639 insertions(+), 38 deletions(-) create mode 100644 workloads/llm-inference-megatron-lm/helm/mount/Megatron-LM-inference.patch delete mode 100644 workloads/llm-inference-megatron-lm/helm/mount/megatron-lm-inference-llama3-1-70b.patch create mode 100644 workloads/llm-pretraining-megatron-lm-ray/helm/mount/Megatron-LM.patch diff --git a/docs/tutorials/tutorial-03-deliver-resources-and-run-megatron-cpt.md b/docs/tutorials/tutorial-03-deliver-resources-and-run-megatron-cpt.md index bff6a10..236c190 100644 --- a/docs/tutorials/tutorial-03-deliver-resources-and-run-megatron-cpt.md +++ b/docs/tutorials/tutorial-03-deliver-resources-and-run-megatron-cpt.md @@ -63,6 +63,20 @@ helm template workloads/llm-pretraining-megatron-lm-ray/helm \ | kubectl apply -f - ``` +It is important to note that service account used by the rayjob must have `get rayjob` and `patch configmap | pvc` permissions in order to run garbage collection script from [https://github.com/silogen/ai-workloads/blob/main/workloads/llm-pretraining-megatron-lm-ray/helm/mount/gc.sh](https://github.com/silogen/ai-workloads/blob/main/workloads/llm-pretraining-megatron-lm-ray/helm/mount/gc.sh) successfully. If this requirement is not satisfied it will manifest by failing to start the ray cluster. The head pod of the cluster will have `Init:Error` status because init container that runs `gc.sh` script fails with the error similar to + +```bash +Error from server (Forbidden): rayjobs.ray.io is forbidden: User "system:serviceaccount:examplenamespace:default" cannot get resource "rayjobs" in API group "ray.io" in the namespace "examplenamespace" +``` + +To quickly overcome this issue while waiting for permissions setup one can comment out this line in [https://github.com/silogen/ai-workloads/blob/main/workloads/llm-pretraining-megatron-lm-ray/helm/templates/ray_job.yaml](https://github.com/silogen/ai-workloads/blob/main/workloads/llm-pretraining-megatron-lm-ray/helm/templates/ray_job.yaml#L69) + +``` +bash /local_resources/mount/gc.sh{{- if and .Values.kaiwo.storageEnabled .Values.kaiwo.enabled}} --skip-pvc{{- end }} {{ include "release.fullname" . }} +``` + +If automatic garbage collection was disabled this way then resources of the workload such as `PVC` and `ConfigMap` should be deleted manually using `kubectl delete` commands in the end of the run. + ### 2.4 Run inference workload with the final checkpoint (2.3) and query it using sample prompts on Llama-3.1-8B In order to perform inference with the just trained Llama-3.1-8B model and verify it's quality, follow the steps: diff --git a/docs/tutorials/tutorial-04-deliver-llama70b-and-run-megatron-cpt-with-tp8-ddp2.md b/docs/tutorials/tutorial-04-deliver-llama70b-and-run-megatron-cpt-with-tp8-ddp2.md index adecc24..481fb0b 100644 --- a/docs/tutorials/tutorial-04-deliver-llama70b-and-run-megatron-cpt-with-tp8-ddp2.md +++ b/docs/tutorials/tutorial-04-deliver-llama70b-and-run-megatron-cpt-with-tp8-ddp2.md @@ -62,6 +62,20 @@ helm template workloads/llm-pretraining-megatron-lm-ray/helm \ | kubectl apply -f - ``` +It is important to note that service account used by the rayjob must have `get rayjob` and `patch configmap | pvc` permissions in order to run garbage collection script from [https://github.com/silogen/ai-workloads/blob/main/workloads/llm-pretraining-megatron-lm-ray/helm/mount/gc.sh](https://github.com/silogen/ai-workloads/blob/main/workloads/llm-pretraining-megatron-lm-ray/helm/mount/gc.sh) successfully. If this requirement is not satisfied it will manifest by failing to start the ray cluster. The head pod of the cluster will have `Init:Error` status because init container that runs `gc.sh` script fails with the error similar to + +```bash +Error from server (Forbidden): rayjobs.ray.io is forbidden: User "system:serviceaccount:examplenamespace:default" cannot get resource "rayjobs" in API group "ray.io" in the namespace "examplenamespace" +``` + +To quickly overcome this issue while waiting for permissions setup one can comment out this line in [https://github.com/silogen/ai-workloads/blob/main/workloads/llm-pretraining-megatron-lm-ray/helm/templates/ray_job.yaml](https://github.com/silogen/ai-workloads/blob/main/workloads/llm-pretraining-megatron-lm-ray/helm/templates/ray_job.yaml#L69) + +``` +bash /local_resources/mount/gc.sh{{- if and .Values.kaiwo.storageEnabled .Values.kaiwo.enabled}} --skip-pvc{{- end }} {{ include "release.fullname" . }} +``` + +If automatic garbage collection was disabled this way then resources of the workload such as `PVC` and `ConfigMap` should be deleted manually using `kubectl delete` commands in the end of the run. + ### 2.4 Run inference workload with the final checkpoint (2.3) and query it using sample prompts on Llama-3.1-70B diff --git a/workloads/llm-inference-megatron-lm/helm/mount/Megatron-LM-inference.patch b/workloads/llm-inference-megatron-lm/helm/mount/Megatron-LM-inference.patch new file mode 100644 index 0000000..433d845 --- /dev/null +++ b/workloads/llm-inference-megatron-lm/helm/mount/Megatron-LM-inference.patch @@ -0,0 +1,237 @@ +diff --git a/megatron/core/dist_checkpointing/strategies/filesystem_async.py b/megatron/core/dist_checkpointing/strategies/filesystem_async.py +index 47ab4d11..0c1b868d 100644 +--- a/megatron/core/dist_checkpointing/strategies/filesystem_async.py ++++ b/megatron/core/dist_checkpointing/strategies/filesystem_async.py +@@ -20,6 +20,15 @@ from torch.distributed.checkpoint.planner import SavePlan, SavePlanner, WriteIte + from torch.distributed.checkpoint.storage import WriteResult + from torch.futures import Future + ++try: ++ # This PR https://github.com/pytorch/pytorch/pull/143359 introduced breaking change to saving checkpoints ++ # in torch_dist format. This is a workaround to fix the issue. ++ from torch.distributed.checkpoint.filesystem import _StorageWriterTransforms ++ from functools import partial ++ _write_item = partial(_write_item, _StorageWriterTransforms()) ++except ImportError: ++ pass ++ + logger = logging.getLogger(__name__) + + WriteBucket = Tuple[Path, str, Tuple[list, list]] # represents writes to a single file +diff --git a/megatron/inference/endpoints/completions.py b/megatron/inference/endpoints/completions.py +index 32dbc5dc..ab187a93 100644 +--- a/megatron/inference/endpoints/completions.py ++++ b/megatron/inference/endpoints/completions.py +@@ -144,12 +144,17 @@ class MegatronCompletions(Resource): + for batch_idx, (prompt_plus_generation, prompt) in enumerate( + zip(prompts_plus_generations, prompts) + ): ++ prompt_tokens = tok.tokenize(prompt) ++ prompt_token_count = len(prompt_tokens) ++ prompt_reconstructed = tok.detokenize(tokens[batch_idx][:prompt_token_count]) ++ + tok_offsets = tok.offsets(tokens[batch_idx], prompt_plus_generation) + if echo: +- str_trunc_start_idx, tok_idx_start = 0, 0 ++ str_trunc_start_idx, tok_idx_start, tok_idx_start_offsets = 0, 0, 0 + else: +- str_trunc_start_idx = len(prompt) +- tok_idx_start = np.searchsorted(tok_offsets, len(prompt)) ++ str_trunc_start_idx = len(prompt_reconstructed) ++ tok_idx_start_offsets = np.searchsorted(tok_offsets, str_trunc_start_idx) ++ tok_idx_start = prompt_token_count + + # truncate the generation at the first stop token + trunc_idxs = [ +@@ -161,21 +166,21 @@ class MegatronCompletions(Resource): + truncated_generation = prompt_plus_generation[str_trunc_start_idx:str_trunc_end_idx] + + # TODO(sasatheesh): handle cases where truncated_generation is not a full token +- tok_idx_end = np.searchsorted(tok_offsets, len(truncated_generation)) ++ tok_idx_end = np.searchsorted(tok_offsets, str_trunc_end_idx) + +- truncated_generation_logprobs = output_log_probs[batch_idx][tok_idx_start:tok_idx_end] ++ truncated_generation_logprobs = output_log_probs[batch_idx][max(tok_idx_start-1,0):tok_idx_end-1] + truncated_generation_tokens = tokens[batch_idx][tok_idx_start:tok_idx_end] + truncated_generation_topk_logprobs = ret_topk_logprobs[batch_idx][ + tok_idx_start:tok_idx_end + ] +- truncated_generation_tok_offsets = tok_offsets[tok_idx_start:tok_idx_end] ++ truncated_generation_tok_offsets = tok_offsets[tok_idx_start_offsets:tok_idx_end] + + results.append( + { + "index": batch_idx, + "text": truncated_generation, + "logprobs": { +- "token_logprobs": [None] + truncated_generation_logprobs, ++ "token_logprobs": truncated_generation_logprobs, + "tokens": [tok.detokenize([tk]) for tk in truncated_generation_tokens], + "text_offset": truncated_generation_tok_offsets, + "top_logprobs": truncated_generation_topk_logprobs, +diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py +index 5ac0747e..69a3dd75 100644 +--- a/megatron/training/arguments.py ++++ b/megatron/training/arguments.py +@@ -710,9 +710,9 @@ def validate_args(args, defaults={}): + if args.num_experts is not None: + assert args.spec is None, "Model Spec must be None when using MoEs" + +- if args.tensor_model_parallel_size > 1: +- assert args.sequence_parallel, \ +- "When using MoE and tensor parallelism, sequence parallelism must be used." ++ #if args.tensor_model_parallel_size > 1: ++ # assert args.sequence_parallel, \ ++ # "When using MoE and tensor parallelism, sequence parallelism must be used." + + if args.moe_ffn_hidden_size is None: + args.moe_ffn_hidden_size = args.ffn_hidden_size +diff --git a/megatron/training/checkpointing.py b/megatron/training/checkpointing.py +index 92813050..dd771395 100644 +--- a/megatron/training/checkpointing.py ++++ b/megatron/training/checkpointing.py +@@ -970,6 +970,9 @@ def load_args_from_checkpoint( + _set_arg('rotary_base', force=True) + _set_arg('rotary_percent', force=True) + _set_arg('rotary_interleaved', force=True) ++ _set_arg('rotary_seq_len_interpolation_factor', force=True) ++ _set_arg('use_rope_scaling', force=True) ++ _set_arg('norm_epsilon', force=True) + _set_arg('add_bias_linear', force=True) + _set_arg('add_qkv_bias', force=True) + _set_arg('squared_relu', force=True) +diff --git a/megatron/training/tokenizer/tokenizer.py b/megatron/training/tokenizer/tokenizer.py +index 11e7645d..366c7a7b 100644 +--- a/megatron/training/tokenizer/tokenizer.py ++++ b/megatron/training/tokenizer/tokenizer.py +@@ -182,15 +182,12 @@ class _HuggingFaceTokenizer(MegatronTokenizer): + return self._tokenizer.decode(token_ids, **kwargs) + + def offsets(self, ids: list[int], text: str) -> list[int]: +- retok_ids: "transformers.BatchEncoding" = self._tokenizer(text) +- offsets, next_start_idx = [], 0 +- for i in range(len(ids)): +- span = retok_ids.token_to_chars(i) +- if span is not None: +- offsets.append(span.start) +- next_start_idx = span.end +- else: +- offsets.append(next_start_idx) ++ tokens = self._tokenizer.convert_ids_to_tokens(ids) ++ offsets = [] ++ current = 0 ++ for t in tokens: ++ offsets.append(current) ++ current += len(t) + return offsets + + @property +diff --git a/pretrain_gpt.py b/pretrain_gpt.py +index d31c0954..a850624a 100644 +--- a/pretrain_gpt.py ++++ b/pretrain_gpt.py +@@ -125,7 +125,8 @@ def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megat + position_embedding_type=args.position_embedding_type, + rotary_percent=args.rotary_percent, + rotary_base=args.rotary_base, +- rope_scaling=args.use_rope_scaling ++ rope_scaling=args.use_rope_scaling, ++ seq_len_interpolation_factor=args.rotary_seq_len_interpolation_factor + ) + + return model +diff --git a/tools/checkpoint/convert.py b/tools/checkpoint/convert.py +index 935613b1..4a2297f6 100644 +--- a/tools/checkpoint/convert.py ++++ b/tools/checkpoint/convert.py +@@ -151,4 +151,8 @@ def main(): + + + if __name__ == '__main__': ++ try: ++ mp.set_start_method('spawn') ++ except RuntimeError: ++ pass + main() +diff --git a/tools/checkpoint/loader_llama_mistral.py b/tools/checkpoint/loader_llama_mistral.py +index b6697964..c96fec45 100644 +--- a/tools/checkpoint/loader_llama_mistral.py ++++ b/tools/checkpoint/loader_llama_mistral.py +@@ -320,6 +320,13 @@ def load_args_from_checkpoint(args): + args.padded_vocab_size = model_args["vocab_size"] + args.ffn_hidden_size = model_args["intermediate_size"] + ++ if "rope_theta" in model_args: ++ args.rotary_base = int(model_args["rope_theta"]) ++ if "rope_scaling" in model_args and model_args["rope_scaling"].get("type", "") == "linear" and "factor" in model_args["rope_scaling"]: ++ args.rotary_seq_len_interpolation_factor = int(model_args["rope_scaling"]["factor"]) ++ if "rope_scaling" in model_args and model_args["rope_scaling"].get("rope_type", "") == "llama3": ++ args.use_rope_scaling = True ++ + if "num_key_value_heads" in model_args: + args.group_query_attention = True + args.num_query_groups = model_args["num_key_value_heads"] +@@ -457,6 +464,7 @@ def _load_checkpoint(queue, args): + '--no-save-rng', + '--mock-data', # To pass the "blend data checks" in arguments.py + '--no-initialization', ++ '--no-gradient-accumulation-fusion', + '--load', args.load_dir, + '--no-one-logger', + ] +@@ -560,6 +568,10 @@ def _load_checkpoint(queue, args): + md.checkpoint_args = margs + md.consumed_train_samples = 0 + md.consumed_valid_samples = 0 ++ md.norm_epsilon = margs.norm_epsilon ++ md.rotary_base = margs.rotary_base ++ md.rotary_seq_len_interpolation_factor = margs.rotary_seq_len_interpolation_factor ++ md.use_rope_scaling = margs.use_rope_scaling + + margs.model_size = args.model_size + +diff --git a/tools/checkpoint/saver_mcore.py b/tools/checkpoint/saver_mcore.py +index 2caf26a9..83c7951b 100644 +--- a/tools/checkpoint/saver_mcore.py ++++ b/tools/checkpoint/saver_mcore.py +@@ -137,6 +137,15 @@ def save_checkpoint(queue, args): + '--no-one-logger', + ] + ++ if md.norm_epsilon: ++ sys.argv.extend(['--norm-epsilon', str(md.norm_epsilon)]) ++ if md.rotary_base: ++ sys.argv.extend(['--rotary-base', str(md.rotary_base)]) ++ if md.rotary_seq_len_interpolation_factor: ++ sys.argv.extend(['--rotary-seq-len-interpolation-factor', str(md.rotary_seq_len_interpolation_factor)]) ++ if md.use_rope_scaling: ++ sys.argv.append('--use-rope-scaling') ++ + if md.make_vocab_size_divisible_by is not None: + sys.argv.extend(['--make-vocab-size-divisible-by', str(md.make_vocab_size_divisible_by)]) + if md.params_dtype == torch.float16: +@@ -188,8 +197,8 @@ def save_checkpoint(queue, args): + margs.apply_query_key_layer_scaling = md.checkpoint_args.apply_query_key_layer_scaling + + # Sequence parallel is required if use both tensor-parallel and Moe. +- if margs.num_experts is not None and args.target_tensor_parallel_size is not None: +- if margs.num_experts > 1 and args.target_tensor_parallel_size > 1: ++ if args.target_tensor_parallel_size is not None: ++ if args.target_tensor_parallel_size > 1: + margs.sequence_parallel = True + + validate_args(margs) +diff --git a/tools/run_text_generation_server.py b/tools/run_text_generation_server.py +index e5b3f08a..fd6688e2 100644 +--- a/tools/run_text_generation_server.py ++++ b/tools/run_text_generation_server.py +@@ -84,7 +84,8 @@ def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megat + position_embedding_type=args.position_embedding_type, + rotary_percent=args.rotary_percent, + rotary_base=args.rotary_base, +- rope_scaling=args.use_rope_scaling ++ rope_scaling=args.use_rope_scaling, ++ seq_len_interpolation_factor=args.rotary_seq_len_interpolation_factor + ) + + return model diff --git a/workloads/llm-inference-megatron-lm/helm/mount/megatron-lm-inference-llama3-1-70b.patch b/workloads/llm-inference-megatron-lm/helm/mount/megatron-lm-inference-llama3-1-70b.patch deleted file mode 100644 index a72cb3a..0000000 --- a/workloads/llm-inference-megatron-lm/helm/mount/megatron-lm-inference-llama3-1-70b.patch +++ /dev/null @@ -1,17 +0,0 @@ -diff --git a/megatron/training/arguments.py b/megatron/training/arguments.py -index 5ac0747e..69a3dd75 100644 ---- a/megatron/training/arguments.py -+++ b/megatron/training/arguments.py -@@ -710,9 +710,9 @@ def validate_args(args, defaults={}): - if args.num_experts is not None: - assert args.spec is None, "Model Spec must be None when using MoEs" - -- if args.tensor_model_parallel_size > 1: -- assert args.sequence_parallel, \ -- "When using MoE and tensor parallelism, sequence parallelism must be used." -+ #if args.tensor_model_parallel_size > 1: -+ # assert args.sequence_parallel, \ -+ # "When using MoE and tensor parallelism, sequence parallelism must be used." - - if args.moe_ffn_hidden_size is None: - args.moe_ffn_hidden_size = args.ffn_hidden_size \ No newline at end of file diff --git a/workloads/llm-inference-megatron-lm/helm/overrides/tutorial-04-llama-3-1-70b.yaml b/workloads/llm-inference-megatron-lm/helm/overrides/tutorial-04-llama-3-1-70b.yaml index 0bb1037..42a5bf7 100644 --- a/workloads/llm-inference-megatron-lm/helm/overrides/tutorial-04-llama-3-1-70b.yaml +++ b/workloads/llm-inference-megatron-lm/helm/overrides/tutorial-04-llama-3-1-70b.yaml @@ -7,7 +7,7 @@ imagePullPolicy: Always remoteTokenizerPath: default-bucket/models/meta-llama/Llama-3.1-70B/ # Path to the checkpoint for Llama 3.1 70B -remoteModelPath: default-bucket/megatron-models/meta-llama/Llama-3.1-70B/ +remoteModelPath: default-bucket/experiments/megatron-lm/llama-3.1-70b-cpt-test/ envVars: BUCKET_STORAGE_HOST: http://minio.minio-tenant-default.svc.cluster.local:80 diff --git a/workloads/llm-inference-megatron-lm/helm/templates/deployment.yaml b/workloads/llm-inference-megatron-lm/helm/templates/deployment.yaml index f67f869..18224af 100644 --- a/workloads/llm-inference-megatron-lm/helm/templates/deployment.yaml +++ b/workloads/llm-inference-megatron-lm/helm/templates/deployment.yaml @@ -29,7 +29,7 @@ spec: - | bash /workload/mount/download_files.sh {{ .Values.remoteModelPath }} {{ .Values.remoteTokenizerPath | trimSuffix "/" }} git checkout fd6f0d11 - git apply /workload/mount/megatron-lm-inference-llama3-1-70b.patch + git apply /workload/mount/Megatron-LM-inference.patch echo "Patch applied successfully" bash /workload/mount/run_megatron.sh ports: diff --git a/workloads/llm-megatron-ckpt-conversion/helm/mount/Megatron-LM.patch b/workloads/llm-megatron-ckpt-conversion/helm/mount/Megatron-LM.patch index cf956ea..e9bfa47 100644 --- a/workloads/llm-megatron-ckpt-conversion/helm/mount/Megatron-LM.patch +++ b/workloads/llm-megatron-ckpt-conversion/helm/mount/Megatron-LM.patch @@ -5,7 +5,7 @@ index 47ab4d11..0c1b868d 100644 @@ -20,6 +20,15 @@ from torch.distributed.checkpoint.planner import SavePlan, SavePlanner, WriteIte from torch.distributed.checkpoint.storage import WriteResult from torch.futures import Future - + +try: + # This PR https://github.com/pytorch/pytorch/pull/143359 introduced breaking change to saving checkpoints + # in torch_dist format. This is a workaround to fix the issue. @@ -16,15 +16,43 @@ index 47ab4d11..0c1b868d 100644 + pass + logger = logging.getLogger(__name__) - + WriteBucket = Tuple[Path, str, Tuple[list, list]] # represents writes to a single file +diff --git a/megatron/training/checkpointing.py b/megatron/training/checkpointing.py +index 92813050..dd771395 100644 +--- a/megatron/training/checkpointing.py ++++ b/megatron/training/checkpointing.py +@@ -970,6 +970,9 @@ def load_args_from_checkpoint( + _set_arg('rotary_base', force=True) + _set_arg('rotary_percent', force=True) + _set_arg('rotary_interleaved', force=True) ++ _set_arg('rotary_seq_len_interpolation_factor', force=True) ++ _set_arg('use_rope_scaling', force=True) ++ _set_arg('norm_epsilon', force=True) + _set_arg('add_bias_linear', force=True) + _set_arg('add_qkv_bias', force=True) + _set_arg('squared_relu', force=True) +diff --git a/pretrain_gpt.py b/pretrain_gpt.py +index d31c0954..a850624a 100644 +--- a/pretrain_gpt.py ++++ b/pretrain_gpt.py +@@ -125,7 +125,8 @@ def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megat + position_embedding_type=args.position_embedding_type, + rotary_percent=args.rotary_percent, + rotary_base=args.rotary_base, +- rope_scaling=args.use_rope_scaling ++ rope_scaling=args.use_rope_scaling, ++ seq_len_interpolation_factor=args.rotary_seq_len_interpolation_factor + ) + + return model diff --git a/tools/checkpoint/convert.py b/tools/checkpoint/convert.py index 935613b1..4a2297f6 100644 --- a/tools/checkpoint/convert.py +++ b/tools/checkpoint/convert.py @@ -151,4 +151,8 @@ def main(): - - + + if __name__ == '__main__': + try: + mp.set_start_method('spawn') @@ -32,10 +60,24 @@ index 935613b1..4a2297f6 100644 + pass main() diff --git a/tools/checkpoint/loader_llama_mistral.py b/tools/checkpoint/loader_llama_mistral.py -index b6697964..054ab941 100644 +index b6697964..c96fec45 100644 --- a/tools/checkpoint/loader_llama_mistral.py +++ b/tools/checkpoint/loader_llama_mistral.py -@@ -457,6 +457,7 @@ def _load_checkpoint(queue, args): +@@ -320,6 +320,13 @@ def load_args_from_checkpoint(args): + args.padded_vocab_size = model_args["vocab_size"] + args.ffn_hidden_size = model_args["intermediate_size"] + ++ if "rope_theta" in model_args: ++ args.rotary_base = int(model_args["rope_theta"]) ++ if "rope_scaling" in model_args and model_args["rope_scaling"].get("type", "") == "linear" and "factor" in model_args["rope_scaling"]: ++ args.rotary_seq_len_interpolation_factor = int(model_args["rope_scaling"]["factor"]) ++ if "rope_scaling" in model_args and model_args["rope_scaling"].get("rope_type", "") == "llama3": ++ args.use_rope_scaling = True ++ + if "num_key_value_heads" in model_args: + args.group_query_attention = True + args.num_query_groups = model_args["num_key_value_heads"] +@@ -457,6 +464,7 @@ def _load_checkpoint(queue, args): '--no-save-rng', '--mock-data', # To pass the "blend data checks" in arguments.py '--no-initialization', @@ -43,18 +85,59 @@ index b6697964..054ab941 100644 '--load', args.load_dir, '--no-one-logger', ] +@@ -560,6 +568,10 @@ def _load_checkpoint(queue, args): + md.checkpoint_args = margs + md.consumed_train_samples = 0 + md.consumed_valid_samples = 0 ++ md.norm_epsilon = margs.norm_epsilon ++ md.rotary_base = margs.rotary_base ++ md.rotary_seq_len_interpolation_factor = margs.rotary_seq_len_interpolation_factor ++ md.use_rope_scaling = margs.use_rope_scaling + + margs.model_size = args.model_size + diff --git a/tools/checkpoint/saver_mcore.py b/tools/checkpoint/saver_mcore.py -index 2caf26a9..0bfe2a8a 100644 +index 2caf26a9..83c7951b 100644 --- a/tools/checkpoint/saver_mcore.py +++ b/tools/checkpoint/saver_mcore.py -@@ -188,8 +188,8 @@ def save_checkpoint(queue, args): +@@ -137,6 +137,15 @@ def save_checkpoint(queue, args): + '--no-one-logger', + ] + ++ if md.norm_epsilon: ++ sys.argv.extend(['--norm-epsilon', str(md.norm_epsilon)]) ++ if md.rotary_base: ++ sys.argv.extend(['--rotary-base', str(md.rotary_base)]) ++ if md.rotary_seq_len_interpolation_factor: ++ sys.argv.extend(['--rotary-seq-len-interpolation-factor', str(md.rotary_seq_len_interpolation_factor)]) ++ if md.use_rope_scaling: ++ sys.argv.append('--use-rope-scaling') ++ + if md.make_vocab_size_divisible_by is not None: + sys.argv.extend(['--make-vocab-size-divisible-by', str(md.make_vocab_size_divisible_by)]) + if md.params_dtype == torch.float16: +@@ -188,8 +197,8 @@ def save_checkpoint(queue, args): margs.apply_query_key_layer_scaling = md.checkpoint_args.apply_query_key_layer_scaling - + # Sequence parallel is required if use both tensor-parallel and Moe. - if margs.num_experts is not None and args.target_tensor_parallel_size is not None: - if margs.num_experts > 1 and args.target_tensor_parallel_size > 1: + if args.target_tensor_parallel_size is not None: + if args.target_tensor_parallel_size > 1: margs.sequence_parallel = True - + validate_args(margs) +diff --git a/tools/run_text_generation_server.py b/tools/run_text_generation_server.py +index e5b3f08a..fd6688e2 100644 +--- a/tools/run_text_generation_server.py ++++ b/tools/run_text_generation_server.py +@@ -84,7 +84,8 @@ def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megat + position_embedding_type=args.position_embedding_type, + rotary_percent=args.rotary_percent, + rotary_base=args.rotary_base, +- rope_scaling=args.use_rope_scaling ++ rope_scaling=args.use_rope_scaling, ++ seq_len_interpolation_factor=args.rotary_seq_len_interpolation_factor + ) + + return model diff --git a/workloads/llm-megatron-ckpt-conversion/helm/templates/conversion-job.yaml b/workloads/llm-megatron-ckpt-conversion/helm/templates/conversion-job.yaml index b6ca364..2a0f39e 100644 --- a/workloads/llm-megatron-ckpt-conversion/helm/templates/conversion-job.yaml +++ b/workloads/llm-megatron-ckpt-conversion/helm/templates/conversion-job.yaml @@ -130,10 +130,15 @@ spec: --loader {{ .loader }} \ --saver {{ .saver }} \ --target-tensor-parallel-size {{ .tensorParallel }} \ + --target-pipeline-parallel-size {{ .pipelineParallel }} \ + --target-expert-parallel-size {{ .expertParallel }} \ --checkpoint-type hf \ --load-dir /local-resources/sourcemodel \ --save-dir /local-resources/checkpoints \ - --tokenizer-model /local-resources/sourcemodel + --tokenizer-model /local-resources/sourcemodel \ + {{- range $.Values.additionalArgs }} + {{ . }} \ + {{- end }} echo "Checkpoint conversion completed" diff --git a/workloads/llm-megatron-ckpt-conversion/helm/values.yaml b/workloads/llm-megatron-ckpt-conversion/helm/values.yaml index c65b9c8..7f98c62 100644 --- a/workloads/llm-megatron-ckpt-conversion/helm/values.yaml +++ b/workloads/llm-megatron-ckpt-conversion/helm/values.yaml @@ -42,3 +42,9 @@ conversionArgs: loader: "llama_mistral" # Model loader: llama_mistral, megatron, etc. saver: "mcore" tensorParallel: 1 # 1 for 8B, 8 for 70B + pipelineParallel: 1 + expertParallel: 1 + +# Additional args to pass to conversion script, e.g. --bf16 or --fp16 for specific precision +additionalArgs: +- "--bf16" diff --git a/workloads/llm-pretraining-megatron-lm-ray/helm/README.md b/workloads/llm-pretraining-megatron-lm-ray/helm/README.md index 408b19e..577d9d2 100644 --- a/workloads/llm-pretraining-megatron-lm-ray/helm/README.md +++ b/workloads/llm-pretraining-megatron-lm-ray/helm/README.md @@ -8,7 +8,6 @@ To generate manifests and print them in standard output using the default `value helm template workloads/llm-pretraining-megatron-lm-ray/helm ``` - This will generate a kubernetes manifest with a RayJob, a ConfigMap and a PersistentVolumeClaim resources in the user's active namespace. To override the default values, a specific file can be passed using `--values` flag @@ -48,6 +47,20 @@ Some assumptions for running the pretraining jobs are as follows: The initial mo key: minio-secret-key ``` +Additionally service account used by the rayjob must have `get rayjob` and `patch configmap | pvc` permissions in order to run garbage collection script from [helm/mount/gc.sh](./mount/gc.sh) successfully. If this requirement is not satisfied it will manifest by failing to start the ray cluster. The head pod of the cluster will have `Init:Error` status because init container that runs `gc.sh` script fails with the error similar to + +```bash +Error from server (Forbidden): rayjobs.ray.io is forbidden: User "system:serviceaccount:examplenamespace:default" cannot get resource "rayjobs" in API group "ray.io" in the namespace "examplenamespace" +``` + +To quickly overcome this issue while waiting for permissions setup one can comment out this line in [./templates/ray_job.yaml](./templates/ray_job.yaml#L69) + +``` +bash /local_resources/mount/gc.sh{{- if and .Values.kaiwo.storageEnabled .Values.kaiwo.enabled}} --skip-pvc{{- end }} {{ include "release.fullname" . }} +``` + ## Cleanup Note that this chart, when run with `kubectl apply`, will create RayJob, PersistentVolumeClaim and ConfigMap objects. After the RayJob has finished, there is a 3600-second grace period to remove the RayJob object from the namespace. ConfigMap and PersistentVolumeClaim are attached to the lifecycle of the RayJob at the start of the workload and cleaned up automatically. However, if there is an issue during start up of the workload, there can be a situation, when ConfigMap and PersistentVolumeClaim are created but are not owned by the RayJob. In this case ConfigMap and PersistentVolumeClaim resources should be cleaned up manually using `kubectl delete` command. + +If automatic garbage collection was disabled then resources of the workload should be deleted manually using `kubectl delete` commands. diff --git a/workloads/llm-pretraining-megatron-lm-ray/helm/mount/Megatron-LM.patch b/workloads/llm-pretraining-megatron-lm-ray/helm/mount/Megatron-LM.patch new file mode 100644 index 0000000..e9bfa47 --- /dev/null +++ b/workloads/llm-pretraining-megatron-lm-ray/helm/mount/Megatron-LM.patch @@ -0,0 +1,143 @@ +diff --git a/megatron/core/dist_checkpointing/strategies/filesystem_async.py b/megatron/core/dist_checkpointing/strategies/filesystem_async.py +index 47ab4d11..0c1b868d 100644 +--- a/megatron/core/dist_checkpointing/strategies/filesystem_async.py ++++ b/megatron/core/dist_checkpointing/strategies/filesystem_async.py +@@ -20,6 +20,15 @@ from torch.distributed.checkpoint.planner import SavePlan, SavePlanner, WriteIte + from torch.distributed.checkpoint.storage import WriteResult + from torch.futures import Future + ++try: ++ # This PR https://github.com/pytorch/pytorch/pull/143359 introduced breaking change to saving checkpoints ++ # in torch_dist format. This is a workaround to fix the issue. ++ from torch.distributed.checkpoint.filesystem import _StorageWriterTransforms ++ from functools import partial ++ _write_item = partial(_write_item, _StorageWriterTransforms()) ++except ImportError: ++ pass ++ + logger = logging.getLogger(__name__) + + WriteBucket = Tuple[Path, str, Tuple[list, list]] # represents writes to a single file +diff --git a/megatron/training/checkpointing.py b/megatron/training/checkpointing.py +index 92813050..dd771395 100644 +--- a/megatron/training/checkpointing.py ++++ b/megatron/training/checkpointing.py +@@ -970,6 +970,9 @@ def load_args_from_checkpoint( + _set_arg('rotary_base', force=True) + _set_arg('rotary_percent', force=True) + _set_arg('rotary_interleaved', force=True) ++ _set_arg('rotary_seq_len_interpolation_factor', force=True) ++ _set_arg('use_rope_scaling', force=True) ++ _set_arg('norm_epsilon', force=True) + _set_arg('add_bias_linear', force=True) + _set_arg('add_qkv_bias', force=True) + _set_arg('squared_relu', force=True) +diff --git a/pretrain_gpt.py b/pretrain_gpt.py +index d31c0954..a850624a 100644 +--- a/pretrain_gpt.py ++++ b/pretrain_gpt.py +@@ -125,7 +125,8 @@ def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megat + position_embedding_type=args.position_embedding_type, + rotary_percent=args.rotary_percent, + rotary_base=args.rotary_base, +- rope_scaling=args.use_rope_scaling ++ rope_scaling=args.use_rope_scaling, ++ seq_len_interpolation_factor=args.rotary_seq_len_interpolation_factor + ) + + return model +diff --git a/tools/checkpoint/convert.py b/tools/checkpoint/convert.py +index 935613b1..4a2297f6 100644 +--- a/tools/checkpoint/convert.py ++++ b/tools/checkpoint/convert.py +@@ -151,4 +151,8 @@ def main(): + + + if __name__ == '__main__': ++ try: ++ mp.set_start_method('spawn') ++ except RuntimeError: ++ pass + main() +diff --git a/tools/checkpoint/loader_llama_mistral.py b/tools/checkpoint/loader_llama_mistral.py +index b6697964..c96fec45 100644 +--- a/tools/checkpoint/loader_llama_mistral.py ++++ b/tools/checkpoint/loader_llama_mistral.py +@@ -320,6 +320,13 @@ def load_args_from_checkpoint(args): + args.padded_vocab_size = model_args["vocab_size"] + args.ffn_hidden_size = model_args["intermediate_size"] + ++ if "rope_theta" in model_args: ++ args.rotary_base = int(model_args["rope_theta"]) ++ if "rope_scaling" in model_args and model_args["rope_scaling"].get("type", "") == "linear" and "factor" in model_args["rope_scaling"]: ++ args.rotary_seq_len_interpolation_factor = int(model_args["rope_scaling"]["factor"]) ++ if "rope_scaling" in model_args and model_args["rope_scaling"].get("rope_type", "") == "llama3": ++ args.use_rope_scaling = True ++ + if "num_key_value_heads" in model_args: + args.group_query_attention = True + args.num_query_groups = model_args["num_key_value_heads"] +@@ -457,6 +464,7 @@ def _load_checkpoint(queue, args): + '--no-save-rng', + '--mock-data', # To pass the "blend data checks" in arguments.py + '--no-initialization', ++ '--no-gradient-accumulation-fusion', + '--load', args.load_dir, + '--no-one-logger', + ] +@@ -560,6 +568,10 @@ def _load_checkpoint(queue, args): + md.checkpoint_args = margs + md.consumed_train_samples = 0 + md.consumed_valid_samples = 0 ++ md.norm_epsilon = margs.norm_epsilon ++ md.rotary_base = margs.rotary_base ++ md.rotary_seq_len_interpolation_factor = margs.rotary_seq_len_interpolation_factor ++ md.use_rope_scaling = margs.use_rope_scaling + + margs.model_size = args.model_size + +diff --git a/tools/checkpoint/saver_mcore.py b/tools/checkpoint/saver_mcore.py +index 2caf26a9..83c7951b 100644 +--- a/tools/checkpoint/saver_mcore.py ++++ b/tools/checkpoint/saver_mcore.py +@@ -137,6 +137,15 @@ def save_checkpoint(queue, args): + '--no-one-logger', + ] + ++ if md.norm_epsilon: ++ sys.argv.extend(['--norm-epsilon', str(md.norm_epsilon)]) ++ if md.rotary_base: ++ sys.argv.extend(['--rotary-base', str(md.rotary_base)]) ++ if md.rotary_seq_len_interpolation_factor: ++ sys.argv.extend(['--rotary-seq-len-interpolation-factor', str(md.rotary_seq_len_interpolation_factor)]) ++ if md.use_rope_scaling: ++ sys.argv.append('--use-rope-scaling') ++ + if md.make_vocab_size_divisible_by is not None: + sys.argv.extend(['--make-vocab-size-divisible-by', str(md.make_vocab_size_divisible_by)]) + if md.params_dtype == torch.float16: +@@ -188,8 +197,8 @@ def save_checkpoint(queue, args): + margs.apply_query_key_layer_scaling = md.checkpoint_args.apply_query_key_layer_scaling + + # Sequence parallel is required if use both tensor-parallel and Moe. +- if margs.num_experts is not None and args.target_tensor_parallel_size is not None: +- if margs.num_experts > 1 and args.target_tensor_parallel_size > 1: ++ if args.target_tensor_parallel_size is not None: ++ if args.target_tensor_parallel_size > 1: + margs.sequence_parallel = True + + validate_args(margs) +diff --git a/tools/run_text_generation_server.py b/tools/run_text_generation_server.py +index e5b3f08a..fd6688e2 100644 +--- a/tools/run_text_generation_server.py ++++ b/tools/run_text_generation_server.py +@@ -84,7 +84,8 @@ def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megat + position_embedding_type=args.position_embedding_type, + rotary_percent=args.rotary_percent, + rotary_base=args.rotary_base, +- rope_scaling=args.use_rope_scaling ++ rope_scaling=args.use_rope_scaling, ++ seq_len_interpolation_factor=args.rotary_seq_len_interpolation_factor + ) + + return model diff --git a/workloads/llm-pretraining-megatron-lm-ray/helm/mount/ray_entrypoint.py b/workloads/llm-pretraining-megatron-lm-ray/helm/mount/ray_entrypoint.py index b9fb2db..d2c0fa6 100644 --- a/workloads/llm-pretraining-megatron-lm-ray/helm/mount/ray_entrypoint.py +++ b/workloads/llm-pretraining-megatron-lm-ray/helm/mount/ray_entrypoint.py @@ -54,6 +54,10 @@ def setup_environment(self): """ Sets up the environment (device, env vars) required for Megatron initialization which will happen inside the pretrain function. """ + import subprocess + + subprocess.run("cd /workspace/Megatron-LM; git apply /local_resources/mount/Megatron-LM.patch;", shell=True) + # Synchronize GPU Visibility Environment Variables hip_visible_devices = os.environ.get("HIP_VISIBLE_DEVICES") print( diff --git a/workloads/llm-pretraining-megatron-lm/helm/mount/Megatron-LM.patch b/workloads/llm-pretraining-megatron-lm/helm/mount/Megatron-LM.patch index 3074cb5..e9bfa47 100644 --- a/workloads/llm-pretraining-megatron-lm/helm/mount/Megatron-LM.patch +++ b/workloads/llm-pretraining-megatron-lm/helm/mount/Megatron-LM.patch @@ -5,7 +5,7 @@ index 47ab4d11..0c1b868d 100644 @@ -20,6 +20,15 @@ from torch.distributed.checkpoint.planner import SavePlan, SavePlanner, WriteIte from torch.distributed.checkpoint.storage import WriteResult from torch.futures import Future - + +try: + # This PR https://github.com/pytorch/pytorch/pull/143359 introduced breaking change to saving checkpoints + # in torch_dist format. This is a workaround to fix the issue. @@ -16,15 +16,43 @@ index 47ab4d11..0c1b868d 100644 + pass + logger = logging.getLogger(__name__) - + WriteBucket = Tuple[Path, str, Tuple[list, list]] # represents writes to a single file +diff --git a/megatron/training/checkpointing.py b/megatron/training/checkpointing.py +index 92813050..dd771395 100644 +--- a/megatron/training/checkpointing.py ++++ b/megatron/training/checkpointing.py +@@ -970,6 +970,9 @@ def load_args_from_checkpoint( + _set_arg('rotary_base', force=True) + _set_arg('rotary_percent', force=True) + _set_arg('rotary_interleaved', force=True) ++ _set_arg('rotary_seq_len_interpolation_factor', force=True) ++ _set_arg('use_rope_scaling', force=True) ++ _set_arg('norm_epsilon', force=True) + _set_arg('add_bias_linear', force=True) + _set_arg('add_qkv_bias', force=True) + _set_arg('squared_relu', force=True) +diff --git a/pretrain_gpt.py b/pretrain_gpt.py +index d31c0954..a850624a 100644 +--- a/pretrain_gpt.py ++++ b/pretrain_gpt.py +@@ -125,7 +125,8 @@ def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megat + position_embedding_type=args.position_embedding_type, + rotary_percent=args.rotary_percent, + rotary_base=args.rotary_base, +- rope_scaling=args.use_rope_scaling ++ rope_scaling=args.use_rope_scaling, ++ seq_len_interpolation_factor=args.rotary_seq_len_interpolation_factor + ) + + return model diff --git a/tools/checkpoint/convert.py b/tools/checkpoint/convert.py index 935613b1..4a2297f6 100644 --- a/tools/checkpoint/convert.py +++ b/tools/checkpoint/convert.py @@ -151,4 +151,8 @@ def main(): - - + + if __name__ == '__main__': + try: + mp.set_start_method('spawn') @@ -32,10 +60,24 @@ index 935613b1..4a2297f6 100644 + pass main() diff --git a/tools/checkpoint/loader_llama_mistral.py b/tools/checkpoint/loader_llama_mistral.py -index b6697964..054ab941 100644 +index b6697964..c96fec45 100644 --- a/tools/checkpoint/loader_llama_mistral.py +++ b/tools/checkpoint/loader_llama_mistral.py -@@ -457,6 +457,7 @@ def _load_checkpoint(queue, args): +@@ -320,6 +320,13 @@ def load_args_from_checkpoint(args): + args.padded_vocab_size = model_args["vocab_size"] + args.ffn_hidden_size = model_args["intermediate_size"] + ++ if "rope_theta" in model_args: ++ args.rotary_base = int(model_args["rope_theta"]) ++ if "rope_scaling" in model_args and model_args["rope_scaling"].get("type", "") == "linear" and "factor" in model_args["rope_scaling"]: ++ args.rotary_seq_len_interpolation_factor = int(model_args["rope_scaling"]["factor"]) ++ if "rope_scaling" in model_args and model_args["rope_scaling"].get("rope_type", "") == "llama3": ++ args.use_rope_scaling = True ++ + if "num_key_value_heads" in model_args: + args.group_query_attention = True + args.num_query_groups = model_args["num_key_value_heads"] +@@ -457,6 +464,7 @@ def _load_checkpoint(queue, args): '--no-save-rng', '--mock-data', # To pass the "blend data checks" in arguments.py '--no-initialization', @@ -43,3 +85,59 @@ index b6697964..054ab941 100644 '--load', args.load_dir, '--no-one-logger', ] +@@ -560,6 +568,10 @@ def _load_checkpoint(queue, args): + md.checkpoint_args = margs + md.consumed_train_samples = 0 + md.consumed_valid_samples = 0 ++ md.norm_epsilon = margs.norm_epsilon ++ md.rotary_base = margs.rotary_base ++ md.rotary_seq_len_interpolation_factor = margs.rotary_seq_len_interpolation_factor ++ md.use_rope_scaling = margs.use_rope_scaling + + margs.model_size = args.model_size + +diff --git a/tools/checkpoint/saver_mcore.py b/tools/checkpoint/saver_mcore.py +index 2caf26a9..83c7951b 100644 +--- a/tools/checkpoint/saver_mcore.py ++++ b/tools/checkpoint/saver_mcore.py +@@ -137,6 +137,15 @@ def save_checkpoint(queue, args): + '--no-one-logger', + ] + ++ if md.norm_epsilon: ++ sys.argv.extend(['--norm-epsilon', str(md.norm_epsilon)]) ++ if md.rotary_base: ++ sys.argv.extend(['--rotary-base', str(md.rotary_base)]) ++ if md.rotary_seq_len_interpolation_factor: ++ sys.argv.extend(['--rotary-seq-len-interpolation-factor', str(md.rotary_seq_len_interpolation_factor)]) ++ if md.use_rope_scaling: ++ sys.argv.append('--use-rope-scaling') ++ + if md.make_vocab_size_divisible_by is not None: + sys.argv.extend(['--make-vocab-size-divisible-by', str(md.make_vocab_size_divisible_by)]) + if md.params_dtype == torch.float16: +@@ -188,8 +197,8 @@ def save_checkpoint(queue, args): + margs.apply_query_key_layer_scaling = md.checkpoint_args.apply_query_key_layer_scaling + + # Sequence parallel is required if use both tensor-parallel and Moe. +- if margs.num_experts is not None and args.target_tensor_parallel_size is not None: +- if margs.num_experts > 1 and args.target_tensor_parallel_size > 1: ++ if args.target_tensor_parallel_size is not None: ++ if args.target_tensor_parallel_size > 1: + margs.sequence_parallel = True + + validate_args(margs) +diff --git a/tools/run_text_generation_server.py b/tools/run_text_generation_server.py +index e5b3f08a..fd6688e2 100644 +--- a/tools/run_text_generation_server.py ++++ b/tools/run_text_generation_server.py +@@ -84,7 +84,8 @@ def model_provider(pre_process=True, post_process=True) -> Union[GPTModel, megat + position_embedding_type=args.position_embedding_type, + rotary_percent=args.rotary_percent, + rotary_base=args.rotary_base, +- rope_scaling=args.use_rope_scaling ++ rope_scaling=args.use_rope_scaling, ++ seq_len_interpolation_factor=args.rotary_seq_len_interpolation_factor + ) + + return model diff --git a/workloads/llm-pretraining-megatron-lm/helm/mount/train-cpt.sh b/workloads/llm-pretraining-megatron-lm/helm/mount/train-cpt.sh index f2e6969..de38114 100644 --- a/workloads/llm-pretraining-megatron-lm/helm/mount/train-cpt.sh +++ b/workloads/llm-pretraining-megatron-lm/helm/mount/train-cpt.sh @@ -11,7 +11,8 @@ ################################################################################# # Source the environment variables from the separate script -source ./set-env-vars.sh # path in container +DIR="$(cd -P "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +source "$DIR/set-env-vars.sh" # path in container TIME_STAMP=$(date +"%Y-%m-%d_%H-%M-%S") EXP_NAME="${EXP_NAME:-perf}" From 0eebd2113e96291356b469b9d40cf8c4b9aa2fbb Mon Sep 17 00:00:00 2001 From: Jussi Elo Date: Wed, 26 Nov 2025 09:29:34 +0200 Subject: [PATCH 3/6] Update the Docs workflow with contemporary setup (#465) --- .github/workflows/docs-file-copy.yml | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/.github/workflows/docs-file-copy.yml b/.github/workflows/docs-file-copy.yml index eb432eb..fca2b37 100644 --- a/.github/workflows/docs-file-copy.yml +++ b/.github/workflows/docs-file-copy.yml @@ -1,6 +1,8 @@ name: Copy workload documentation to public docs repo -# We rsync the ai-workloads documentation to a temp clone of the public docs repo -# and commit and push the changes to the main branch of the public docs repo. Purpose is to keep the Docs repo (consolidated SiloGen docs) updated with ai-workloads repository changes. +# We rsync the docs and workloads directories to the public documentation. +# We commit and push the changes to the develop branch of the public docs +# repository. Purpose is to keep the consolidated EAI documentation updated +# with changes from the contributing repositories. on: push: @@ -16,20 +18,22 @@ jobs: if: github.repository == 'silogen/ai-workloads' runs-on: ubuntu-latest steps: - - name: Checkout core repo + - name: Checkout the repo uses: actions/checkout@v4 - - name: Push to public docs repo + - name: Push to external docs develop branch run: | git config --global user.name 'GitHub Actions' git config --global user.email 'actions@github.com' git clone https://x-access-token:${{ secrets.DOCS_REPO_TOKEN }}@github.com/silogen/ai-workloads.git source_docs - git clone https://x-access-token:${{ secrets.DOCS_REPO_TOKEN }}@github.com/silogen/docs.git target_silogen_docs - cd target_silogen_docs + git clone https://x-access-token:${{ secrets.DOCS_REPO_TOKEN }}@github.com/silogen/AMDEnterpriseAISuiteDocs.git target_amd_docs + cd target_amd_docs + rsync -av --delete --exclude='.git' ../source_docs/docs docs/ai-workloads-docs rsync -av --delete --exclude='.git' ../source_docs/workloads docs/ai-workloads-manifests + git add . git diff --staged --quiet || git commit -m "Update external docs from ai-workloads repo" - git push origin main + git push origin develop env: DOCS_REPO_TOKEN: ${{ secrets.DOCS_REPO_TOKEN }} From e56e1d87166f38f4f291c7239d33ecbfee932260 Mon Sep 17 00:00:00 2001 From: jorivesga Date: Wed, 26 Nov 2025 10:22:59 +0200 Subject: [PATCH 4/6] Rename swinunetr training and inference workloads (#464) * rename swinunetr training and inference workloads to benchmak-lifecience-... for consistency with the other lifescience workloads (reinvent, semaflow) * remove folder commited by mistake --- .../examples/demo_inference_service.ipynb | 0 .../examples/utils.py | 0 .../helm/Chart.yaml | 0 .../helm/README.md | 0 .../helm/mount/README.md | 0 .../helm/mount/data_utils.py | 0 .../helm/mount/entrypoint.sh | 0 .../helm/mount/inference_service.py | 0 .../helm/mount/requirements.txt | 0 .../helm/mount/swinunetr.py | 0 .../helm/mount/swinunetr_configuration.py | 0 .../helm/overrides/kaiwo/kaiwo-enable.yaml | 0 .../helm/templates/_helpers.tpl | 0 .../helm/templates/configmap.yaml | 0 .../helm/templates/deployment.yaml | 0 .../helm/templates/service.yaml | 0 .../helm/values.schema.json | 0 .../helm/values.yaml | 0 .../helm/Chart.yaml | 0 .../helm/README.md | 0 .../helm/mount/README.md | 0 .../helm/mount/data_utils.py | 0 .../helm/mount/entrypoint.sh | 0 .../helm/mount/lr_scheduler.py | 0 .../helm/mount/main.py | 0 .../helm/mount/requirements.txt | 0 .../helm/mount/trainer.py | 0 .../helm/mount/utils.py | 0 .../helm/overrides/kaiwo/kaiwo-enable.yaml | 0 .../helm/templates/_helpers.tpl | 0 .../helm/templates/configmap.yaml | 0 .../helm/templates/job.yaml | 0 .../helm/values.schema.json | 0 .../helm/values.yaml | 0 34 files changed, 0 insertions(+), 0 deletions(-) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/examples/demo_inference_service.ipynb (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/examples/utils.py (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/Chart.yaml (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/README.md (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/mount/README.md (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/mount/data_utils.py (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/mount/entrypoint.sh (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/mount/inference_service.py (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/mount/requirements.txt (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/mount/swinunetr.py (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/mount/swinunetr_configuration.py (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/overrides/kaiwo/kaiwo-enable.yaml (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/templates/_helpers.tpl (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/templates/configmap.yaml (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/templates/deployment.yaml (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/templates/service.yaml (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/values.schema.json (100%) rename workloads/{dev-lifescience-swinunetr-inference => benchmark-lifescience-swinunetr-inference}/helm/values.yaml (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/Chart.yaml (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/README.md (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/mount/README.md (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/mount/data_utils.py (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/mount/entrypoint.sh (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/mount/lr_scheduler.py (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/mount/main.py (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/mount/requirements.txt (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/mount/trainer.py (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/mount/utils.py (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/overrides/kaiwo/kaiwo-enable.yaml (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/templates/_helpers.tpl (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/templates/configmap.yaml (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/templates/job.yaml (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/values.schema.json (100%) rename workloads/{dev-lifescience-swinunetr-training => benchmark-lifescience-swinunetr-training}/helm/values.yaml (100%) diff --git a/workloads/dev-lifescience-swinunetr-inference/examples/demo_inference_service.ipynb b/workloads/benchmark-lifescience-swinunetr-inference/examples/demo_inference_service.ipynb similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/examples/demo_inference_service.ipynb rename to workloads/benchmark-lifescience-swinunetr-inference/examples/demo_inference_service.ipynb diff --git a/workloads/dev-lifescience-swinunetr-inference/examples/utils.py b/workloads/benchmark-lifescience-swinunetr-inference/examples/utils.py similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/examples/utils.py rename to workloads/benchmark-lifescience-swinunetr-inference/examples/utils.py diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/Chart.yaml b/workloads/benchmark-lifescience-swinunetr-inference/helm/Chart.yaml similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/Chart.yaml rename to workloads/benchmark-lifescience-swinunetr-inference/helm/Chart.yaml diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/README.md b/workloads/benchmark-lifescience-swinunetr-inference/helm/README.md similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/README.md rename to workloads/benchmark-lifescience-swinunetr-inference/helm/README.md diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/mount/README.md b/workloads/benchmark-lifescience-swinunetr-inference/helm/mount/README.md similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/mount/README.md rename to workloads/benchmark-lifescience-swinunetr-inference/helm/mount/README.md diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/mount/data_utils.py b/workloads/benchmark-lifescience-swinunetr-inference/helm/mount/data_utils.py similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/mount/data_utils.py rename to workloads/benchmark-lifescience-swinunetr-inference/helm/mount/data_utils.py diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/mount/entrypoint.sh b/workloads/benchmark-lifescience-swinunetr-inference/helm/mount/entrypoint.sh similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/mount/entrypoint.sh rename to workloads/benchmark-lifescience-swinunetr-inference/helm/mount/entrypoint.sh diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/mount/inference_service.py b/workloads/benchmark-lifescience-swinunetr-inference/helm/mount/inference_service.py similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/mount/inference_service.py rename to workloads/benchmark-lifescience-swinunetr-inference/helm/mount/inference_service.py diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/mount/requirements.txt b/workloads/benchmark-lifescience-swinunetr-inference/helm/mount/requirements.txt similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/mount/requirements.txt rename to workloads/benchmark-lifescience-swinunetr-inference/helm/mount/requirements.txt diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/mount/swinunetr.py b/workloads/benchmark-lifescience-swinunetr-inference/helm/mount/swinunetr.py similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/mount/swinunetr.py rename to workloads/benchmark-lifescience-swinunetr-inference/helm/mount/swinunetr.py diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/mount/swinunetr_configuration.py b/workloads/benchmark-lifescience-swinunetr-inference/helm/mount/swinunetr_configuration.py similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/mount/swinunetr_configuration.py rename to workloads/benchmark-lifescience-swinunetr-inference/helm/mount/swinunetr_configuration.py diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/overrides/kaiwo/kaiwo-enable.yaml b/workloads/benchmark-lifescience-swinunetr-inference/helm/overrides/kaiwo/kaiwo-enable.yaml similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/overrides/kaiwo/kaiwo-enable.yaml rename to workloads/benchmark-lifescience-swinunetr-inference/helm/overrides/kaiwo/kaiwo-enable.yaml diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/templates/_helpers.tpl b/workloads/benchmark-lifescience-swinunetr-inference/helm/templates/_helpers.tpl similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/templates/_helpers.tpl rename to workloads/benchmark-lifescience-swinunetr-inference/helm/templates/_helpers.tpl diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/templates/configmap.yaml b/workloads/benchmark-lifescience-swinunetr-inference/helm/templates/configmap.yaml similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/templates/configmap.yaml rename to workloads/benchmark-lifescience-swinunetr-inference/helm/templates/configmap.yaml diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/templates/deployment.yaml b/workloads/benchmark-lifescience-swinunetr-inference/helm/templates/deployment.yaml similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/templates/deployment.yaml rename to workloads/benchmark-lifescience-swinunetr-inference/helm/templates/deployment.yaml diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/templates/service.yaml b/workloads/benchmark-lifescience-swinunetr-inference/helm/templates/service.yaml similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/templates/service.yaml rename to workloads/benchmark-lifescience-swinunetr-inference/helm/templates/service.yaml diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/values.schema.json b/workloads/benchmark-lifescience-swinunetr-inference/helm/values.schema.json similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/values.schema.json rename to workloads/benchmark-lifescience-swinunetr-inference/helm/values.schema.json diff --git a/workloads/dev-lifescience-swinunetr-inference/helm/values.yaml b/workloads/benchmark-lifescience-swinunetr-inference/helm/values.yaml similarity index 100% rename from workloads/dev-lifescience-swinunetr-inference/helm/values.yaml rename to workloads/benchmark-lifescience-swinunetr-inference/helm/values.yaml diff --git a/workloads/dev-lifescience-swinunetr-training/helm/Chart.yaml b/workloads/benchmark-lifescience-swinunetr-training/helm/Chart.yaml similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/Chart.yaml rename to workloads/benchmark-lifescience-swinunetr-training/helm/Chart.yaml diff --git a/workloads/dev-lifescience-swinunetr-training/helm/README.md b/workloads/benchmark-lifescience-swinunetr-training/helm/README.md similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/README.md rename to workloads/benchmark-lifescience-swinunetr-training/helm/README.md diff --git a/workloads/dev-lifescience-swinunetr-training/helm/mount/README.md b/workloads/benchmark-lifescience-swinunetr-training/helm/mount/README.md similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/mount/README.md rename to workloads/benchmark-lifescience-swinunetr-training/helm/mount/README.md diff --git a/workloads/dev-lifescience-swinunetr-training/helm/mount/data_utils.py b/workloads/benchmark-lifescience-swinunetr-training/helm/mount/data_utils.py similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/mount/data_utils.py rename to workloads/benchmark-lifescience-swinunetr-training/helm/mount/data_utils.py diff --git a/workloads/dev-lifescience-swinunetr-training/helm/mount/entrypoint.sh b/workloads/benchmark-lifescience-swinunetr-training/helm/mount/entrypoint.sh similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/mount/entrypoint.sh rename to workloads/benchmark-lifescience-swinunetr-training/helm/mount/entrypoint.sh diff --git a/workloads/dev-lifescience-swinunetr-training/helm/mount/lr_scheduler.py b/workloads/benchmark-lifescience-swinunetr-training/helm/mount/lr_scheduler.py similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/mount/lr_scheduler.py rename to workloads/benchmark-lifescience-swinunetr-training/helm/mount/lr_scheduler.py diff --git a/workloads/dev-lifescience-swinunetr-training/helm/mount/main.py b/workloads/benchmark-lifescience-swinunetr-training/helm/mount/main.py similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/mount/main.py rename to workloads/benchmark-lifescience-swinunetr-training/helm/mount/main.py diff --git a/workloads/dev-lifescience-swinunetr-training/helm/mount/requirements.txt b/workloads/benchmark-lifescience-swinunetr-training/helm/mount/requirements.txt similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/mount/requirements.txt rename to workloads/benchmark-lifescience-swinunetr-training/helm/mount/requirements.txt diff --git a/workloads/dev-lifescience-swinunetr-training/helm/mount/trainer.py b/workloads/benchmark-lifescience-swinunetr-training/helm/mount/trainer.py similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/mount/trainer.py rename to workloads/benchmark-lifescience-swinunetr-training/helm/mount/trainer.py diff --git a/workloads/dev-lifescience-swinunetr-training/helm/mount/utils.py b/workloads/benchmark-lifescience-swinunetr-training/helm/mount/utils.py similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/mount/utils.py rename to workloads/benchmark-lifescience-swinunetr-training/helm/mount/utils.py diff --git a/workloads/dev-lifescience-swinunetr-training/helm/overrides/kaiwo/kaiwo-enable.yaml b/workloads/benchmark-lifescience-swinunetr-training/helm/overrides/kaiwo/kaiwo-enable.yaml similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/overrides/kaiwo/kaiwo-enable.yaml rename to workloads/benchmark-lifescience-swinunetr-training/helm/overrides/kaiwo/kaiwo-enable.yaml diff --git a/workloads/dev-lifescience-swinunetr-training/helm/templates/_helpers.tpl b/workloads/benchmark-lifescience-swinunetr-training/helm/templates/_helpers.tpl similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/templates/_helpers.tpl rename to workloads/benchmark-lifescience-swinunetr-training/helm/templates/_helpers.tpl diff --git a/workloads/dev-lifescience-swinunetr-training/helm/templates/configmap.yaml b/workloads/benchmark-lifescience-swinunetr-training/helm/templates/configmap.yaml similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/templates/configmap.yaml rename to workloads/benchmark-lifescience-swinunetr-training/helm/templates/configmap.yaml diff --git a/workloads/dev-lifescience-swinunetr-training/helm/templates/job.yaml b/workloads/benchmark-lifescience-swinunetr-training/helm/templates/job.yaml similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/templates/job.yaml rename to workloads/benchmark-lifescience-swinunetr-training/helm/templates/job.yaml diff --git a/workloads/dev-lifescience-swinunetr-training/helm/values.schema.json b/workloads/benchmark-lifescience-swinunetr-training/helm/values.schema.json similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/values.schema.json rename to workloads/benchmark-lifescience-swinunetr-training/helm/values.schema.json diff --git a/workloads/dev-lifescience-swinunetr-training/helm/values.yaml b/workloads/benchmark-lifescience-swinunetr-training/helm/values.yaml similarity index 100% rename from workloads/dev-lifescience-swinunetr-training/helm/values.yaml rename to workloads/benchmark-lifescience-swinunetr-training/helm/values.yaml From df65f04ef9f38ee3fce7f51df06febd08a5b1903 Mon Sep 17 00:00:00 2001 From: Bo Date: Tue, 2 Dec 2025 11:03:16 +0100 Subject: [PATCH 5/6] Update JupyterLab to ROCm 7.0.2 (#468) * Update JupyterLab container image to rocm7.0.2 and adjust related configurations * Fix quotes in HTTP probe paths for consistency * Add failureThreshold and periodSeconds to startupProbe configuration * Update startupProbe configuration --- workloads/dev-workspace-jupyterlab/helm/README.md | 2 +- .../helm/overrides/dev-center/signature.yaml | 2 +- workloads/dev-workspace-jupyterlab/helm/values.yaml | 10 ++++++---- 3 files changed, 8 insertions(+), 6 deletions(-) diff --git a/workloads/dev-workspace-jupyterlab/helm/README.md b/workloads/dev-workspace-jupyterlab/helm/README.md index 15b7aad..de1fca1 100644 --- a/workloads/dev-workspace-jupyterlab/helm/README.md +++ b/workloads/dev-workspace-jupyterlab/helm/README.md @@ -8,7 +8,7 @@ You can configure the following parameters in the `values.yaml` file or override | Parameter | Description | Default | |------------------------|-----------------------------------------------------------------------------|-------------------------------------------------------------------------| -| `image` | Container image repository and tag | `rocm/pytorch:rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.6.0` | +| `image` | Container image repository and tag | `rocm/pytorch:rocm7.0.2_ubuntu24.04_py3.12_pytorch_release_2.8.0` | | `imagePullPolicy` | Image pull policy | `Always` | | `imagePullSecrets` | List of image pull secrets for private registries | `[]` | | `gpus` | Number of GPUs to allocate (set to 0 for CPU-only mode) | `1` | diff --git a/workloads/dev-workspace-jupyterlab/helm/overrides/dev-center/signature.yaml b/workloads/dev-workspace-jupyterlab/helm/overrides/dev-center/signature.yaml index 3516a77..527d61d 100644 --- a/workloads/dev-workspace-jupyterlab/helm/overrides/dev-center/signature.yaml +++ b/workloads/dev-workspace-jupyterlab/helm/overrides/dev-center/signature.yaml @@ -1,4 +1,4 @@ -image: rocm/pytorch:rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.6.0 +image: rocm/pytorch:rocm7.0.2_ubuntu24.04_py3.12_pytorch_release_2.8.0 gpus: 1 memory_per_gpu: 64 # Gi cpu_per_gpu: 4 diff --git a/workloads/dev-workspace-jupyterlab/helm/values.yaml b/workloads/dev-workspace-jupyterlab/helm/values.yaml index 126401e..c9eb60e 100644 --- a/workloads/dev-workspace-jupyterlab/helm/values.yaml +++ b/workloads/dev-workspace-jupyterlab/helm/values.yaml @@ -9,7 +9,7 @@ metadata: user_id: user workload_id: # defaults to the release name -image: rocm/pytorch:rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.6.0 +image: rocm/pytorch:rocm7.0.2_ubuntu24.04_py3.12_pytorch_release_2.8.0 imagePullPolicy: Always imagePullSecrets: [] gpus: 1 @@ -46,15 +46,17 @@ deployment: # ref: https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/ startupProbe: httpGet: - path: "{{ include \"httpRoute.baseUrl\" . }}/api/status" + path: '{{ include "httpRoute.baseUrl" . }}/api/status' port: http + failureThreshold: 60 + periodSeconds: 10 livenessProbe: httpGet: - path: "{{ include \"httpRoute.baseUrl\" . }}/api/status" + path: '{{ include "httpRoute.baseUrl" . }}/api/status' port: http readinessProbe: httpGet: - path: "{{ include \"httpRoute.baseUrl\" . }}/api/status" + path: '{{ include "httpRoute.baseUrl" . }}/api/status' port: http entrypoint: | From f8bb079ea240ea036ad86cf1f5c1de3893192e00 Mon Sep 17 00:00:00 2001 From: Bo Date: Tue, 2 Dec 2025 15:42:36 +0100 Subject: [PATCH 6/6] Update VS Code to ROCm 7.0.2 (#470) * EAI-470: update vscode dev workspace image to ROCm 7.0.2 * Update README and dev-center signature files * Update AWS S3 VSCode extension to version 1.8.5 * Update workloads/dev-workspace-vscode/helm/values.yaml Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update VSCode default settings, disable AI chat by default --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- workloads/dev-workspace-vscode/helm/README.md | 2 +- .../helm/mount/default_settings.json | 3 ++- .../helm/overrides/dev-center/signature.yaml | 2 +- workloads/dev-workspace-vscode/helm/values.yaml | 15 ++++++++------- 4 files changed, 12 insertions(+), 10 deletions(-) diff --git a/workloads/dev-workspace-vscode/helm/README.md b/workloads/dev-workspace-vscode/helm/README.md index 2538ffa..8466229 100644 --- a/workloads/dev-workspace-vscode/helm/README.md +++ b/workloads/dev-workspace-vscode/helm/README.md @@ -28,7 +28,7 @@ You can configure the following parameters in the `values.yaml` file or override | Parameter | Description | Default | |------------------------|-----------------------------------------------------------------------------|-------------------------------------------------------------------------| -| `image` | Container image repository and tag | `rocm/pytorch:rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.6.0` | +| `image` | Container image repository and tag | `rocm/pytorch:rocm7.0.2_ubuntu24.04_py3.12_pytorch_release_2.8.0` | | `imagePullPolicy` | Image pull policy | `Always` | | `imagePullSecrets` | List of image pull secrets for private registries | `[]` | | `gpus` | Number of GPUs to allocate (set to 0 for CPU-only mode) | `1` | diff --git a/workloads/dev-workspace-vscode/helm/mount/default_settings.json b/workloads/dev-workspace-vscode/helm/mount/default_settings.json index d66e5c5..d9d6f31 100644 --- a/workloads/dev-workspace-vscode/helm/mount/default_settings.json +++ b/workloads/dev-workspace-vscode/helm/mount/default_settings.json @@ -1,3 +1,4 @@ { - "workbench.colorTheme": "Default Dark Modern" + "workbench.colorTheme": "Default Dark Modern", + "chat.disableAIFeatures": true } diff --git a/workloads/dev-workspace-vscode/helm/overrides/dev-center/signature.yaml b/workloads/dev-workspace-vscode/helm/overrides/dev-center/signature.yaml index 3516a77..527d61d 100644 --- a/workloads/dev-workspace-vscode/helm/overrides/dev-center/signature.yaml +++ b/workloads/dev-workspace-vscode/helm/overrides/dev-center/signature.yaml @@ -1,4 +1,4 @@ -image: rocm/pytorch:rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.6.0 +image: rocm/pytorch:rocm7.0.2_ubuntu24.04_py3.12_pytorch_release_2.8.0 gpus: 1 memory_per_gpu: 64 # Gi cpu_per_gpu: 4 diff --git a/workloads/dev-workspace-vscode/helm/values.yaml b/workloads/dev-workspace-vscode/helm/values.yaml index 59a07bf..75d76a4 100644 --- a/workloads/dev-workspace-vscode/helm/values.yaml +++ b/workloads/dev-workspace-vscode/helm/values.yaml @@ -12,7 +12,7 @@ pvc_annotations: pvc.silogen.ai/user-pvc-storage-class-name: "multinode" pvc.silogen.ai/user-pvc-uid: "{{ .Values.metadata.user_id }}" -image: rocm/pytorch:rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.6.0 +image: rocm/pytorch:rocm7.0.2_ubuntu24.04_py3.12_pytorch_release_2.8.0 imagePullPolicy: Always imagePullSecrets: [] gpus: 1 @@ -50,7 +50,8 @@ startupProbe: httpGet: path: /healthz port: http - failureThreshold: 20 + failureThreshold: 60 + periodSeconds: 10 livenessProbe: httpGet: path: /healthz @@ -65,12 +66,12 @@ entrypoint: | curl -fsSL https://code-server.dev/install.sh | sh # Set up persistent VSCode configuration directory using environment variable - VSCODE_USER_DIR="$VSCODE_CONFIG_DIR/User" + VSCODE_USER_DIR="$VSCODE_CONFIG_DIR/code-server/User" # Create directory structure if it doesn't exist mkdir -p "$VSCODE_USER_DIR" - mkdir -p "$VSCODE_CONFIG_DIR/extensions" - mkdir -p "$VSCODE_CONFIG_DIR/logs" + mkdir -p "$VSCODE_CONFIG_DIR/code-server/extensions" + mkdir -p "$VSCODE_CONFIG_DIR/code-server/logs" # Copy default settings only if user settings don't exist (preserve user customizations) if [ ! -f "$VSCODE_USER_DIR/settings.json" ]; then @@ -82,8 +83,8 @@ entrypoint: | export XDG_CONFIG_HOME="$VSCODE_CONFIG_DIR" # Install extensions (these will be stored persistently) - curl -L -o /tmp/aws-s3-vscode-extension-1.8.4.vsix https://github.com/necatiarslan/aws-s3/raw/refs/heads/main/vsix/aws-s3-vscode-extension-1.8.4.vsix && - code-server --install-extension /tmp/aws-s3-vscode-extension-1.8.4.vsix + curl -L -o /tmp/aws-s3-vscode-extension-1.8.5.vsix https://github.com/necatiarslan/aws-s3/raw/refs/heads/main/vsix/aws-s3-vscode-extension-1.8.5.vsix && + code-server --install-extension /tmp/aws-s3-vscode-extension-1.8.5.vsix code-server --install-extension ms-python.python code-server --install-extension GitHub.vscode-pull-request-github code-server --install-extension ms-kubernetes-tools.vscode-kubernetes-tools