From 2781d216c6041b53982b410dd11072723fdaa2c2 Mon Sep 17 00:00:00 2001 From: "google-labs-jules[bot]" <161369871+google-labs-jules[bot]@users.noreply.github.com> Date: Tue, 4 Nov 2025 01:26:51 +0000 Subject: [PATCH] feat: Refactor nanochat to run on Vertex AI Pipelines This refactoring enables the nanochat project to be executed as a scalable and robust pipeline on Vertex AI. The monolithic `speedrun.sh` script has been decomposed into a series of containerized components orchestrated by a Kubeflow pipeline. The codebase has been updated to use Google Cloud Storage for artifact management, allowing for seamless data sharing between pipeline steps. A `Dockerfile` and Python wrappers for each pipeline step have been added to the `vertex_pipelines` directory. The `README.md` has been updated with instructions on how to build the Docker image and run the Vertex AI pipeline. --- README.md | 59 +- nanochat/checkpoint_manager.py | 135 +++-- nanochat/common.py | 3 +- nanochat/dataset.py | 91 +++- pyproject.toml | 3 + uv.lock | 771 +++++++++++++++++++++++++-- vertex_pipelines/Dockerfile | 23 + vertex_pipelines/midtraining_step.py | 37 ++ vertex_pipelines/pipeline.py | 74 +++ vertex_pipelines/pretraining_step.py | 35 ++ vertex_pipelines/report_step.py | 18 + vertex_pipelines/sft_step.py | 30 ++ vertex_pipelines/tokenizer_step.py | 25 + 13 files changed, 1207 insertions(+), 97 deletions(-) create mode 100644 vertex_pipelines/Dockerfile create mode 100644 vertex_pipelines/midtraining_step.py create mode 100644 vertex_pipelines/pipeline.py create mode 100644 vertex_pipelines/pretraining_step.py create mode 100644 vertex_pipelines/report_step.py create mode 100644 vertex_pipelines/sft_step.py create mode 100644 vertex_pipelines/tokenizer_step.py diff --git a/README.md b/README.md index 18ea5ce..e9aeb95 100644 --- a/README.md +++ b/README.md @@ -204,7 +204,7 @@ Current LLM policy: disclosure. When submitting a PR, please declare any parts t ## Cite -If you find nanochat helpful in your research cite simply as: +If you find nano.sh helpful in your research cite simply as: ```bibtex @misc{nanochat, @@ -219,3 +219,60 @@ If you find nanochat helpful in your research cite simply as: ## License MIT + +## Running on Vertex AI Pipelines + +This project can also be run on Vertex AI Pipelines, which allows for a more robust and scalable execution environment. The following steps will guide you through the process of setting up and running the nanochat pipeline on Vertex AI. + +### Prerequisites + +Before you begin, you will need the following: + +* A Google Cloud Platform (GCP) project. +* A Google Cloud Storage (GCS) bucket. +* The `gcloud` command-line tool installed and configured. +* Docker installed and configured to push to Google Container Registry (GCR). + +### Building and Pushing the Docker Image + +The first step is to build the Docker image that will be used to run the pipeline components. This image contains all of the necessary dependencies, including Python, `uv`, Rust, and the `nanochat` source code. + +1. **Set your GCP Project ID:** + + ```bash + export GCP_PROJECT=$(gcloud config get-value project) + ``` + +2. **Authenticate with GCR:** + + ```bash + gcloud auth configure-docker + ``` + +3. **Build the Docker image:** + + ```bash + docker build -t gcr.io/${GCP_PROJECT}/nanochat:latest -f vertex_pipelines/Dockerfile . + ``` + +4. **Push the Docker image to GCR:** + + ```bash + docker push gcr.io/${GCP_PROJECT}/nanochat:latest + ``` + +### Running the Pipeline + +Once the Docker image has been pushed to GCR, you can run the pipeline using the `vertex_pipelines/pipeline.py` script. This script will compile the pipeline and submit it to Vertex AI for execution. + +```bash +python vertex_pipelines/pipeline.py \ + --gcp-project ${GCP_PROJECT} \ + --gcs-bucket gs://YOUR_GCS_BUCKET \ + --pipeline-root gs://YOUR_GCS_BUCKET/pipeline-root \ + --docker-image-uri gcr.io/${GCP_PROJECT}/nanochat:latest +``` + +Replace `gs://YOUR_GCS_BUCKET` with your GCS bucket. The `pipeline-root` is a path in your GCS bucket where the pipeline artifacts will be stored. + +The pipeline will then start running on Vertex AI, and you can monitor its progress in the GCP console. diff --git a/nanochat/checkpoint_manager.py b/nanochat/checkpoint_manager.py index a9327c4..bf5baea 100644 --- a/nanochat/checkpoint_manager.py +++ b/nanochat/checkpoint_manager.py @@ -7,6 +7,8 @@ import glob import json import logging import torch +import io +from google.cloud import storage from nanochat.common import get_base_dir from nanochat.gpt import GPT, GPTConfig @@ -22,36 +24,88 @@ def log0(message): def save_checkpoint(checkpoint_dir, step, model_data, optimizer_data, meta_data): assert int(os.environ.get('RANK', 0)) == 0 # prevent footguns for now - os.makedirs(checkpoint_dir, exist_ok=True) - # Save the model state (parameters) - model_path = os.path.join(checkpoint_dir, f"model_{step:06d}.pt") - torch.save(model_data, model_path) - log0(f"Saved model file to: {model_path}") - # Save the optimizer state (useful for SFT or any other fine-tuning) - if optimizer_data is not None: - optimizer_path = os.path.join(checkpoint_dir, f"optim_{step:06d}.pt") - torch.save(optimizer_data, optimizer_path) - log0(f"Saved optimizer file to: {optimizer_path}") - # Save the metadata dict as json - meta_path = os.path.join(checkpoint_dir, f"meta_{step:06d}.json") - with open(meta_path, "w") as f: - json.dump(meta_data, f, indent=2) - log0(f"Saved metadata file to: {meta_path}") + if checkpoint_dir.startswith("gs://"): + storage_client = storage.Client() + bucket_name, prefix = checkpoint_dir[5:].split("/", 1) + bucket = storage_client.bucket(bucket_name) + + # Save model data + model_blob = bucket.blob(f"{prefix}/model_{step:06d}.pt") + with io.BytesIO() as buffer: + torch.save(model_data, buffer) + buffer.seek(0) + model_blob.upload_from_file(buffer) + log0(f"Saved model file to: gs://{bucket_name}/{prefix}/model_{step:06d}.pt") + + # Save optimizer data + if optimizer_data is not None: + optimizer_blob = bucket.blob(f"{prefix}/optim_{step:06d}.pt") + with io.BytesIO() as buffer: + torch.save(optimizer_data, buffer) + buffer.seek(0) + optimizer_blob.upload_from_file(buffer) + log0(f"Saved optimizer file to: gs://{bucket_name}/{prefix}/optim_{step:06d}.pt") + + # Save metadata + meta_blob = bucket.blob(f"{prefix}/meta_{step:06d}.json") + meta_blob.upload_from_string(json.dumps(meta_data, indent=2)) + log0(f"Saved metadata file to: gs://{bucket_name}/{prefix}/meta_{step:06d}.json") + else: + os.makedirs(checkpoint_dir, exist_ok=True) + # Save the model state (parameters) + model_path = os.path.join(checkpoint_dir, f"model_{step:06d}.pt") + torch.save(model_data, model_path) + log0(f"Saved model file to: {model_path}") + # Save the optimizer state (useful for SFT or any other fine-tuning) + if optimizer_data is not None: + optimizer_path = os.path.join(checkpoint_dir, f"optim_{step:06d}.pt") + torch.save(optimizer_data, optimizer_path) + log0(f"Saved optimizer file to: {optimizer_path}") + # Save the metadata dict as json + meta_path = os.path.join(checkpoint_dir, f"meta_{step:06d}.json") + with open(meta_path, "w") as f: + json.dump(meta_data, f, indent=2) + log0(f"Saved metadata file to: {meta_path}") def load_checkpoint(checkpoint_dir, step, device, load_optimizer=False): - # Load the model state - model_path = os.path.join(checkpoint_dir, f"model_{step:06d}.pt") - model_data = torch.load(model_path, map_location=device) - # Load the optimizer state if requested - optimizer_data = None - if load_optimizer: - optimizer_path = os.path.join(checkpoint_dir, f"optim_{step:06d}.pt") - optimizer_data = torch.load(optimizer_path, map_location=device) - # Load the metadata - meta_path = os.path.join(checkpoint_dir, f"meta_{step:06d}.json") - with open(meta_path, "r") as f: - meta_data = json.load(f) + if checkpoint_dir.startswith("gs://"): + storage_client = storage.Client() + bucket_name, prefix = checkpoint_dir[5:].split("/", 1) + bucket = storage_client.bucket(bucket_name) + + # Load model data + model_blob = bucket.blob(f"{prefix}/model_{step:06d}.pt") + with io.BytesIO() as buffer: + model_blob.download_to_file(buffer) + buffer.seek(0) + model_data = torch.load(buffer, map_location=device) + + # Load optimizer data + optimizer_data = None + if load_optimizer: + optimizer_blob = bucket.blob(f"{prefix}/optim_{step:06d}.pt") + with io.BytesIO() as buffer: + optimizer_blob.download_to_file(buffer) + buffer.seek(0) + optimizer_data = torch.load(buffer, map_location=device) + + # Load metadata + meta_blob = bucket.blob(f"{prefix}/meta_{step:06d}.json") + meta_data = json.loads(meta_blob.download_as_string()) + else: + # Load the model state + model_path = os.path.join(checkpoint_dir, f"model_{step:06d}.pt") + model_data = torch.load(model_path, map_location=device) + # Load the optimizer state if requested + optimizer_data = None + if load_optimizer: + optimizer_path = os.path.join(checkpoint_dir, f"optim_{step:06d}.pt") + optimizer_data = torch.load(optimizer_path, map_location=device) + # Load the metadata + meta_path = os.path.join(checkpoint_dir, f"meta_{step:06d}.json") + with open(meta_path, "r") as f: + meta_data = json.load(f) return model_data, optimizer_data, meta_data @@ -95,8 +149,16 @@ def build_model(checkpoint_dir, step, device, phase): def find_largest_model(checkpoint_dir): - # attempt to guess the model tag: take the biggest model available - model_tags = [f for f in os.listdir(checkpoint_dir) if os.path.isdir(os.path.join(checkpoint_dir, f))] + if checkpoint_dir.startswith("gs://"): + storage_client = storage.Client() + bucket_name, prefix = checkpoint_dir[5:].split("/", 1) + bucket = storage_client.bucket(bucket_name) + blobs = bucket.list_blobs(prefix=prefix, delimiter='/') + model_tags = [b.name.split('/')[-2] for b in blobs.prefixes] + else: + # attempt to guess the model tag: take the biggest model available + model_tags = [f for f in os.listdir(checkpoint_dir) if os.path.isdir(os.path.join(checkpoint_dir, f))] + if not model_tags: raise FileNotFoundError(f"No checkpoints found in {checkpoint_dir}") # 1) normally all model tags are of the form d, try that first: @@ -110,13 +172,22 @@ def find_largest_model(checkpoint_dir): candidates.sort(key=lambda x: x[0], reverse=True) return candidates[0][1] # 2) if that failed, take the most recently updated model: - model_tags.sort(key=lambda x: os.path.getmtime(os.path.join(checkpoint_dir, x)), reverse=True) + if not checkpoint_dir.startswith("gs://"): + model_tags.sort(key=lambda x: os.path.getmtime(os.path.join(checkpoint_dir, x)), reverse=True) return model_tags[0] def find_last_step(checkpoint_dir): - # Look into checkpoint_dir and find model_.pt with the highest step - checkpoint_files = glob.glob(os.path.join(checkpoint_dir, "model_*.pt")) + if checkpoint_dir.startswith("gs://"): + storage_client = storage.Client() + bucket_name, prefix = checkpoint_dir[5:].split("/", 1) + bucket = storage_client.bucket(bucket_name) + blobs = bucket.list_blobs(prefix=f"{prefix}/model_") + checkpoint_files = [blob.name for blob in blobs] + else: + # Look into checkpoint_dir and find model_.pt with the highest step + checkpoint_files = glob.glob(os.path.join(checkpoint_dir, "model_*.pt")) + if not checkpoint_files: raise FileNotFoundError(f"No checkpoints found in {checkpoint_dir}") last_step = int(max(os.path.basename(f).split("_")[-1].split(".")[0] for f in checkpoint_files)) diff --git a/nanochat/common.py b/nanochat/common.py index 4e5fc06..ed50104 100644 --- a/nanochat/common.py +++ b/nanochat/common.py @@ -55,7 +55,8 @@ def get_base_dir(): home_dir = os.path.expanduser("~") cache_dir = os.path.join(home_dir, ".cache") nanochat_dir = os.path.join(cache_dir, "nanochat") - os.makedirs(nanochat_dir, exist_ok=True) + if not nanochat_dir.startswith("gs://"): + os.makedirs(nanochat_dir, exist_ok=True) return nanochat_dir def download_file_with_lock(url, filename, postprocess_fn=None): diff --git a/nanochat/dataset.py b/nanochat/dataset.py index 602daed..9155765 100644 --- a/nanochat/dataset.py +++ b/nanochat/dataset.py @@ -13,6 +13,7 @@ import time import requests import pyarrow.parquet as pq from multiprocessing import Pool +from google.cloud import storage from nanochat.common import get_base_dir @@ -24,8 +25,7 @@ BASE_URL = "https://huggingface.co/datasets/karpathy/fineweb-edu-100b-shuffle/re MAX_SHARD = 1822 # the last datashard is shard_01822.parquet index_to_filename = lambda index: f"shard_{index:05d}.parquet" # format of the filenames base_dir = get_base_dir() -DATA_DIR = os.path.join(base_dir, "base_data") -os.makedirs(DATA_DIR, exist_ok=True) +DATA_DIR = os.environ.get("NANOCHAT_DATA_DIR", os.path.join(base_dir, "base_data")) # ----------------------------------------------------------------------------- # These functions are useful utilities to other modules, can/should be imported @@ -33,12 +33,24 @@ os.makedirs(DATA_DIR, exist_ok=True) def list_parquet_files(data_dir=None): """ Looks into a data dir and returns full paths to all parquet files. """ data_dir = DATA_DIR if data_dir is None else data_dir - parquet_files = sorted([ - f for f in os.listdir(data_dir) - if f.endswith('.parquet') and not f.endswith('.tmp') - ]) - parquet_paths = [os.path.join(data_dir, f) for f in parquet_files] - return parquet_paths + if data_dir.startswith("gs://"): + storage_client = storage.Client() + bucket_name, prefix = data_dir[5:].split("/", 1) + bucket = storage_client.bucket(bucket_name) + blobs = bucket.list_blobs(prefix=prefix) + parquet_files = sorted([ + f"gs://{bucket_name}/{blob.name}" for blob in blobs + if blob.name.endswith('.parquet') and not blob.name.endswith('.tmp') + ]) + return parquet_files + else: + os.makedirs(data_dir, exist_ok=True) + parquet_files = sorted([ + f for f in os.listdir(data_dir) + if f.endswith('.parquet') and not f.endswith('.tmp') + ]) + parquet_paths = [os.path.join(data_dir, f) for f in parquet_files] + return parquet_paths def parquets_iter_batched(split, start=0, step=1): """ @@ -59,13 +71,23 @@ def parquets_iter_batched(split, start=0, step=1): # ----------------------------------------------------------------------------- def download_single_file(index): """ Downloads a single file index, with some backoff """ - - # Construct the local filepath for this file and skip if it already exists filename = index_to_filename(index) - filepath = os.path.join(DATA_DIR, filename) - if os.path.exists(filepath): - print(f"Skipping {filepath} (already exists)") - return True + + if DATA_DIR.startswith("gs://"): + storage_client = storage.Client() + bucket_name, prefix = DATA_DIR[5:].split("/", 1) + bucket = storage_client.bucket(bucket_name) + blob_name = os.path.join(prefix, filename) + blob = bucket.blob(blob_name) + + if blob.exists(): + print(f"Skipping {filename} (already exists in GCS)") + return True + else: + filepath = os.path.join(DATA_DIR, filename) + if os.path.exists(filepath): + print(f"Skipping {filepath} (already exists)") + return True # Construct the remote URL for this file url = f"{BASE_URL}/{filename}" @@ -77,26 +99,37 @@ def download_single_file(index): try: response = requests.get(url, stream=True, timeout=30) response.raise_for_status() - # Write to temporary file first - temp_path = filepath + f".tmp" - with open(temp_path, 'wb') as f: - for chunk in response.iter_content(chunk_size=1024 * 1024): # 1MB chunks - if chunk: - f.write(chunk) - # Move temp file to final location - os.rename(temp_path, filepath) - print(f"Successfully downloaded {filename}") + + if DATA_DIR.startswith("gs://"): + storage_client = storage.Client() + bucket_name, prefix = DATA_DIR[5:].split("/", 1) + bucket = storage_client.bucket(bucket_name) + blob_name = os.path.join(prefix, filename) + blob = bucket.blob(blob_name) + blob.upload_from_string(response.content) + else: + # Write to temporary file first + temp_path = filepath + f".tmp" + with open(temp_path, 'wb') as f: + for chunk in response.iter_content(chunk_size=1024 * 1024): # 1MB chunks + if chunk: + f.write(chunk) + # Move temp file to final location + os.rename(temp_path, filepath) + + print(f"Successfully downloaded and stored {filename}") return True except (requests.RequestException, IOError) as e: print(f"Attempt {attempt}/{max_attempts} failed for {filename}: {e}") # Clean up any partial files - for path in [filepath + f".tmp", filepath]: - if os.path.exists(path): - try: - os.remove(path) - except: - pass + if not DATA_DIR.startswith("gs://"): + for path in [filepath + f".tmp", filepath]: + if os.path.exists(path): + try: + os.remove(path) + except: + pass # Try a few times with exponential backoff: 2^attempt seconds if attempt < max_attempts: wait_time = 2 ** attempt diff --git a/pyproject.toml b/pyproject.toml index 3d03c4b..405b873 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,6 +16,9 @@ dependencies = [ "torch>=2.8.0", "uvicorn>=0.36.0", "wandb>=0.21.3", + "google-cloud-storage>=2.10.0", + "kfp>=2.0.0", + "google-cloud-aiplatform>=1.25.0", ] [build-system] diff --git a/uv.lock b/uv.lock index f01bba3..495ef02 100644 --- a/uv.lock +++ b/uv.lock @@ -2,23 +2,37 @@ version = 1 revision = 3 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.12' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'", - "python_full_version >= '3.12' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'", + "python_full_version >= '3.14' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'", + "python_full_version == '3.13.*' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'", + "python_full_version == '3.12.*' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'", + "python_full_version >= '3.14' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'", + "python_full_version == '3.13.*' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'", + "python_full_version == '3.12.*' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'", "python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'", "python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'", "python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'", "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu'", - "python_full_version >= '3.12' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'", - "python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'", + "python_full_version >= '3.14' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'", + "python_full_version == '3.13.*' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'", + "python_full_version == '3.12.*' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'", + "python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'", + "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'", "python_full_version == '3.11.*' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'", "python_full_version < '3.11' and sys_platform == 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'", "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'", "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'", - "python_full_version >= '3.12' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'", + "python_full_version >= '3.14' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 'extra-8-nanochat-gpu'", + "python_full_version == '3.13.*' and sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu' and extra != 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name = "xxhash" version = "3.5.0" diff --git a/vertex_pipelines/Dockerfile b/vertex_pipelines/Dockerfile new file mode 100644 index 0000000..07b0765 --- /dev/null +++ b/vertex_pipelines/Dockerfile @@ -0,0 +1,23 @@ +# Use the official Python 3.10 image. +FROM python:3.10-slim + +# Set the working directory. +WORKDIR /app + +# Install uv, Rust, and other system dependencies. +RUN apt-get update && apt-get install -y curl build-essential +RUN curl -LsSf https://astral.sh/uv/install.sh | sh +RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y +ENV PATH="/root/.cargo/bin:${PATH}" + +# Copy the entire project into the Docker image. +COPY . . + +# Install Python dependencies using uv. +RUN /root/.local/bin/uv sync --extra gpu + +# Build the rustbpe tokenizer. +RUN /root/.local/bin/uv run maturin develop --release --manifest-path rustbpe/Cargo.toml + +# Set the entrypoint. +ENTRYPOINT ["python"] diff --git a/vertex_pipelines/midtraining_step.py b/vertex_pipelines/midtraining_step.py new file mode 100644 index 0000000..0b99e78 --- /dev/null +++ b/vertex_pipelines/midtraining_step.py @@ -0,0 +1,37 @@ +import os +import subprocess +import argparse +from nanochat.common import get_base_dir + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--gcs-bucket", type=str, required=True, help="GCS bucket for artifacts") + parser.add_argument("--wandb-run", type=str, default="dummy", help="Wandb run name") + args = parser.parse_args() + + # Set the base directory to the GCS bucket. + os.environ["NANOCHAT_BASE_DIR"] = args.gcs_bucket + + # Download the identity conversations dataset. + subprocess.run([ + "curl", "-L", "-o", + f"{get_base_dir()}/identity_conversations.jsonl", + "https://karpathy-public.s3.us-west-2.amazonaws.com/identity_conversations.jsonl" + ], check=True) + + # Run mid-training. + subprocess.run([ + "torchrun", "--standalone", "--nproc_per_node=8", + "-m", "scripts.mid_train", "--", + f"--run={args.wandb_run}" + ], check=True) + + # Evaluate the model. + subprocess.run([ + "torchrun", "--standalone", "--nproc_per_node=8", + "-m", "scripts.chat_eval", "--", + "-i", "mid" + ], check=True) + +if __name__ == "__main__": + main() diff --git a/vertex_pipelines/pipeline.py b/vertex_pipelines/pipeline.py new file mode 100644 index 0000000..eb23c6d --- /dev/null +++ b/vertex_pipelines/pipeline.py @@ -0,0 +1,74 @@ +import kfp +from kfp.v2 import dsl +from kfp.v2.compiler import Compiler +from google.cloud import aiplatform + +@dsl.pipeline(name="nanochat-pipeline") +def nanochat_pipeline(gcs_bucket: str, docker_image_uri: str, wandb_run: str = "dummy"): + """ + A Vertex AI pipeline for training and evaluating a nanochat model. + """ + tokenizer_op = dsl.ContainerOp( + name="tokenizer", + image=docker_image_uri, + command=["python", "vertex_pipelines/tokenizer_step.py"], + arguments=["--gcs-bucket", gcs_bucket], + ) + + pretraining_op = dsl.ContainerOp( + name="pretraining", + image=docker_image_uri, + command=["python", "vertex_pipelines/pretraining_step.py"], + arguments=["--gcs-bucket", gcs_bucket, "--wandb-run", wandb_run], + ).after(tokenizer_op) + + midtraining_op = dsl.ContainerOp( + name="midtraining", + image=docker_image_uri, + command=["python", "vertex_pipelines/midtraining_step.py"], + arguments=["--gcs-bucket", gcs_bucket, "--wandb-run", wandb_run], + ).after(pretraining_op) + + sft_op = dsl.ContainerOp( + name="sft", + image=docker_image_uri, + command=["python", "vertex_pipelines/sft_step.py"], + arguments=["--gcs-bucket", gcs_bucket, "--wandb-run", wandb_run], + ).after(midtraining_op) + + report_op = dsl.ContainerOp( + name="report", + image=docker_image_uri, + command=["python", "vertex_pipelines/report_step.py"], + arguments=["--gcs-bucket", gcs_bucket], + ).after(sft_op) + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--gcp-project", type=str, required=True) + parser.add_argument("--gcs-bucket", type=str, required=True) + parser.add_argument("--pipeline-root", type=str, required=True) + parser.add_argument("--docker-image-uri", type=str, required=True) + parser.add_argument("--region", type=str, default="us-central1") + args = parser.parse_args() + + Compiler().compile( + pipeline_func=nanochat_pipeline, + package_path="nanochat_pipeline.json", + ) + + aiplatform.init(project=args.gcp_project, location=args.region) + + job = aiplatform.PipelineJob( + display_name="nanochat-pipeline", + template_path="nanochat_pipeline.json", + pipeline_root=args.pipeline_root, + parameter_values={ + "gcs_bucket": args.gcs_bucket, + "docker_image_uri": args.docker_image_uri, + }, + ) + + job.run() diff --git a/vertex_pipelines/pretraining_step.py b/vertex_pipelines/pretraining_step.py new file mode 100644 index 0000000..f5ada8d --- /dev/null +++ b/vertex_pipelines/pretraining_step.py @@ -0,0 +1,35 @@ +import os +import subprocess +import argparse +from nanochat.common import get_base_dir + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--gcs-bucket", type=str, required=True, help="GCS bucket for artifacts") + parser.add_argument("--wandb-run", type=str, default="dummy", help="Wandb run name") + args = parser.parse_args() + + # Set the base directory to the GCS bucket. + os.environ["NANOCHAT_BASE_DIR"] = args.gcs_bucket + + # Pre-train the d20 model. + subprocess.run([ + "torchrun", "--standalone", "--nproc_per_node=8", + "-m", "scripts.base_train", "--", + "--depth=20", f"--run={args.wandb_run}" + ], check=True) + + # Evaluate the model on a larger chunk of train/val data and draw some samples. + subprocess.run([ + "torchrun", "--standalone", "--nproc_per_node=8", + "-m", "scripts.base_loss" + ], check=True) + + # Evaluate the model on CORE tasks. + subprocess.run([ + "torchrun", "--standalone", "--nproc_per_node=8", + "-m", "scripts.base_eval" + ], check=True) + +if __name__ == "__main__": + main() diff --git a/vertex_pipelines/report_step.py b/vertex_pipelines/report_step.py new file mode 100644 index 0000000..acacbbe --- /dev/null +++ b/vertex_pipelines/report_step.py @@ -0,0 +1,18 @@ +import os +import subprocess +import argparse +from nanochat.common import get_base_dir + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--gcs-bucket", type=str, required=True, help="GCS bucket for artifacts") + args = parser.parse_args() + + # Set the base directory to the GCS bucket. + os.environ["NANOCHAT_BASE_DIR"] = args.gcs_bucket + + # Generate the full report. + subprocess.run(["python", "-m", "nanochat.report", "generate"], check=True) + +if __name__ == "__main__": + main() diff --git a/vertex_pipelines/sft_step.py b/vertex_pipelines/sft_step.py new file mode 100644 index 0000000..f7bb48b --- /dev/null +++ b/vertex_pipelines/sft_step.py @@ -0,0 +1,30 @@ +import os +import subprocess +import argparse +from nanochat.common import get_base_dir + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--gcs-bucket", type=str, required=True, help="GCS bucket for artifacts") + parser.add_argument("--wandb-run", type=str, default="dummy", help="Wandb run name") + args = parser.parse_args() + + # Set the base directory to the GCS bucket. + os.environ["NANOCHAT_BASE_DIR"] = args.gcs_bucket + + # Run supervised finetuning. + subprocess.run([ + "torchrun", "--standalone", "--nproc_per_node=8", + "-m", "scripts.chat_sft", "--", + f"--run={args.wandb_run}" + ], check=True) + + # Evaluate the model. + subprocess.run([ + "torchrun", "--standalone", "--nproc_per_node=8", + "-m", "scripts.chat_eval", "--", + "-i", "sft" + ], check=True) + +if __name__ == "__main__": + main() diff --git a/vertex_pipelines/tokenizer_step.py b/vertex_pipelines/tokenizer_step.py new file mode 100644 index 0000000..eb58eba --- /dev/null +++ b/vertex_pipelines/tokenizer_step.py @@ -0,0 +1,25 @@ +import os +import subprocess +import argparse +from nanochat.common import get_base_dir + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--gcs-bucket", type=str, required=True, help="GCS bucket for artifacts") + args = parser.parse_args() + + # Set the base directory to the GCS bucket. + os.environ["NANOCHAT_BASE_DIR"] = args.gcs_bucket + + # Download the dataset. + subprocess.run(["python", "-m", "nanochat.dataset", "-n", "8"], check=True) + subprocess.run(["python", "-m", "nanochat.dataset", "-n", "240"], check=True) + + # Train the tokenizer. + subprocess.run(["python", "-m", "scripts.tok_train", "--max_chars=2000000000"], check=True) + + # Evaluate the tokenizer. + subprocess.run(["python", "-m", "scripts.tok_eval"], check=True) + +if __name__ == "__main__": + main()