llama-stack-mirror/llama_toolchain/cli/download.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

import argparse
import asyncio
import os
import shutil
import time
from functools import partial
from pathlib import Path

import httpx

from termcolor import cprint

from llama_toolchain.cli.subcommand import Subcommand


class Download(Subcommand):
    """Llama cli for downloading llama toolchain assets"""

    def __init__(self, subparsers: argparse._SubParsersAction):
        super().__init__()
        self.parser = subparsers.add_parser(
            "download",
            prog="llama download",
            description="Download a model from llama.meta.comf or HuggingFace hub",
            formatter_class=argparse.RawTextHelpFormatter,
        )
        setup_download_parser(self.parser)


def setup_download_parser(parser: argparse.ArgumentParser) -> None:
    from llama_models.sku_list import all_registered_models

    models = all_registered_models()
    parser.add_argument(
        "--source",
        choices=["meta", "huggingface"],
        required=True,
    )
    parser.add_argument(
        "--model-id",
        choices=[x.descriptor() for x in models],
        required=True,
    )
    parser.add_argument(
        "--hf-token",
        type=str,
        required=False,
        default=None,
        help="Hugging Face API token. Needed for gated models like llama2/3. Will also try to read environment variable `HF_TOKEN` as default.",
    )
    parser.add_argument(
        "--meta-url",
        type=str,
        required=False,
        help="For source=meta, URL obtained from llama.meta.com after accepting license terms",
    )
    parser.add_argument(
        "--ignore-patterns",
        type=str,
        required=False,
        default="*.safetensors",
        help="""
For source=huggingface, files matching any of the patterns are not downloaded. Defaults to ignoring
safetensors files to avoid downloading duplicate weights.
""",
    )
    parser.set_defaults(func=partial(run_download_cmd, parser=parser))


def _hf_download(
    model: "Model",
    hf_token: str,
    ignore_patterns: str,
    parser: argparse.ArgumentParser,
):
    from huggingface_hub import snapshot_download
    from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError

    from llama_toolchain.common.model_utils import model_local_dir

    repo_id = model.huggingface_repo
    if repo_id is None:
        raise ValueError(f"No repo id found for model {model.descriptor()}")

    output_dir = model_local_dir(model)
    os.makedirs(output_dir, exist_ok=True)
    try:
        true_output_dir = snapshot_download(
            repo_id,
            local_dir=output_dir,
            ignore_patterns=ignore_patterns,
            token=hf_token,
            library_name="llama-toolchain",
        )
    except GatedRepoError:
        parser.error(
            "It looks like you are trying to access a gated repository. Please ensure you "
            "have access to the repository and have provided the proper Hugging Face API token "
            "using the option `--hf-token` or by running `huggingface-cli login`."
            "You can find your token by visiting https://huggingface.co/settings/tokens"
        )
    except RepositoryNotFoundError:
        parser.error(f"Repository '{args.repo_id}' not found on the Hugging Face Hub.")
    except Exception as e:
        parser.error(e)

    print(f"\nSuccessfully downloaded model to {true_output_dir}")


def _meta_download(model: "Model", meta_url: str):
    from llama_models.sku_list import llama_meta_net_info

    from llama_toolchain.common.model_utils import model_local_dir

    output_dir = Path(model_local_dir(model))
    os.makedirs(output_dir, exist_ok=True)

    info = llama_meta_net_info(model)

    # I believe we can use some concurrency here if needed but not sure it is worth it
    for f in info.files:
        output_file = str(output_dir / f)
        url = meta_url.replace("*", f"{info.folder}/{f}")
        total_size = info.pth_size if "consolidated" in f else 0
        cprint(f"Downloading `{f}`...", "white")
        downloader = ResumableDownloader(url, output_file, total_size)
        asyncio.run(downloader.download())

    print(f"\nSuccessfully downloaded model to {output_dir}")
    cprint(f"\nMD5 Checksums are at: {output_dir / 'checklist.chk'}", "white")


def run_download_cmd(args: argparse.Namespace, parser: argparse.ArgumentParser):
    from llama_models.sku_list import resolve_model

    model = resolve_model(args.model_id)
    if model is None:
        parser.error(f"Model {args.model_id} not found")
        return

    if args.source == "huggingface":
        _hf_download(model, args.hf_token, args.ignore_patterns, parser)
    else:
        meta_url = args.meta_url
        if not meta_url:
            meta_url = input(
                "Please provide the signed URL you received via email (e.g., https://llama3-1.llamameta.net/*?Policy...): "
            )
            assert meta_url is not None and "llama3-1.llamameta.net" in meta_url
        _meta_download(model, meta_url)


class ResumableDownloader:
    def __init__(
        self,
        url: str,
        output_file: str,
        total_size: int = 0,
        buffer_size: int = 32 * 1024,
    ):
        self.url = url
        self.output_file = output_file
        self.buffer_size = buffer_size
        self.total_size = total_size
        self.downloaded_size = 0
        self.start_size = 0
        self.start_time = 0

    async def get_file_info(self, client: httpx.AsyncClient) -> None:
        if self.total_size > 0:
            return

        # Force disable compression when trying to retrieve file size
        response = await client.head(
            self.url, follow_redirects=True, headers={"Accept-Encoding": "identity"}
        )
        response.raise_for_status()
        self.url = str(response.url)  # Update URL in case of redirects
        self.total_size = int(response.headers.get("Content-Length", 0))
        if self.total_size == 0:
            raise ValueError(
                "Unable to determine file size. The server might not support range requests."
            )

    async def download(self) -> None:
        self.start_time = time.time()
        async with httpx.AsyncClient() as client:
            await self.get_file_info(client)

            if os.path.exists(self.output_file):
                self.downloaded_size = os.path.getsize(self.output_file)
                self.start_size = self.downloaded_size
                if self.downloaded_size >= self.total_size:
                    print(f"Already downloaded `{self.output_file}`, skipping...")
                    return

            additional_size = self.total_size - self.downloaded_size
            if not self.has_disk_space(additional_size):
                print(
                    f"Not enough disk space to download `{self.output_file}`. "
                    f"Required: {(additional_size / M):.2f} MB"
                )
                raise ValueError(
                    f"Not enough disk space to download `{self.output_file}`"
                )

            while True:
                if self.downloaded_size >= self.total_size:
                    break

                # Cloudfront has a max-size limit
                max_chunk_size = 27_000_000_000
                request_size = min(
                    self.total_size - self.downloaded_size, max_chunk_size
                )
                headers = {
                    "Range": f"bytes={self.downloaded_size}-{self.downloaded_size + request_size}"
                }
                # print(f"Downloading `{self.output_file}`....{headers}")
                try:
                    async with client.stream(
                        "GET", self.url, headers=headers
                    ) as response:
                        response.raise_for_status()
                        with open(self.output_file, "ab") as file:
                            async for chunk in response.aiter_bytes(self.buffer_size):
                                file.write(chunk)
                                self.downloaded_size += len(chunk)
                                self.print_progress()
                except httpx.HTTPError as e:
                    print(f"\nDownload interrupted: {e}")
                    print("You can resume the download by running the script again.")
                except Exception as e:
                    print(f"\nAn error occurred: {e}")

            print(f"\nFinished downloading `{self.output_file}`....")

    def print_progress(self) -> None:
        percent = (self.downloaded_size / self.total_size) * 100
        bar_length = 50
        filled_length = int(bar_length * self.downloaded_size // self.total_size)
        bar = "█" * filled_length + "-" * (bar_length - filled_length)

        elapsed_time = time.time() - self.start_time
        M = 1024 * 1024  # noqa

        speed = (
            (self.downloaded_size - self.start_size) / (elapsed_time * M)
            if elapsed_time > 0
            else 0
        )
        print(
            f"\rProgress: |{bar}| {percent:.2f}% "
            f"({self.downloaded_size // M}/{self.total_size // M} MB) "
            f"Speed: {speed:.2f} MiB/s",
            end="",
            flush=True,
        )

    def has_disk_space(self, file_size: int) -> bool:
        dir_path = os.path.dirname(os.path.abspath(self.output_file))
        free_space = shutil.disk_usage(dir_path).free
        return free_space > file_size