Support Llama3 8b/70b

.gitignore

@@ -5,6 +5,7 @@ __pycache__
 build
 outputs
 dist/*
+*.model
 
 # data
 data

README.md

@@ -47,15 +47,26 @@ Install PyTorch from source or install the latest pytorch nightly, then install
 pip install -r requirements.txt
 ```
 
-Install additional dev requirements if you want to contribute to the repo:
+### Downloading a tokenizer.model
+
+`torchtitan` currently supports training Llama3 (8B, 70B), and Llama2 (13B, 70B) out of the box. To get started training these models, we need to download a tokenizer.model. Follow the instructions on the official [meta-llama](https://huggingface.co/meta-llama/Meta-Llama-3-8B) repository to ensure you have access to the Llama model weights.
+
+Once you have confirmed access, you can run the following command to download the Llama2/3 tokenizer to your local machine.
+
 ```
-pip install -r dev-requirements.txt
+# pass your hf_token in order to download tokenizer.model
+
+# llama3 tokenizer.model
+python torchtitan/datasets/download_tokenizer.py --repo_id meta-llama/Meta-Llama-3-8B --tokenizer_path "original" --hf_token=...
+
+# llama2 tokenizer.model
+python torchtitan/datasets/download_tokenizer.py --repo_id meta-llama/Llama-2-13b-hf --hf_token=...
 ```
 
-run the llama debug model locally to verify the setup is correct:
+Run the llama3 8B model locally on 8 GPUs:
 
 ```
-./run_llama_train.sh
+CONFIG_FILE="./train_configs/llama3_8b.toml" ./run_llama_train.sh
 ```
 
 

requirements.txt

@@ -1,5 +1,6 @@
 torch >= 2.2.0.dev
-sentencepiece
 datasets
 tomli >= 1.1.0 ; python_version < "3.11"
 tensorboard
+sentencepiece
+tiktoken

torchtitan/datasets/download_tokenizer.py

@@ -4,21 +4,25 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-import os
 from typing import Optional
 
 from requests.exceptions import HTTPError
 
 
-def hf_download(repo_id: Optional[str] = None, hf_token: Optional[str] = None) -> None:
+def hf_download(
+    repo_id: str, tokenizer_path: str, local_dir: str, hf_token: Optional[str] = None
+) -> None:
     from huggingface_hub import hf_hub_download
 
-    os.makedirs(f"checkpoints/{repo_id}", exist_ok=True)
+    tokenizer_path = (
+        f"{tokenizer_path}/tokenizer.model" if tokenizer_path else "tokenizer.model"
+    )
+
     try:
         hf_hub_download(
             repo_id,
-            "tokenizer.model",
-            local_dir="torchtitan/datasets/tokenizer/",
+            tokenizer_path,
+            local_dir=local_dir,
             local_dir_use_symlinks=False,
             token=hf_token,
         )
@@ -38,12 +42,24 @@ def hf_download(repo_id: Optional[str] = None, hf_token: Optional[str] = None) -
     parser.add_argument(
         "--repo_id",
         type=str,
-        default="meta-llama/llama-2-70b",
-        help="Repository ID to download from.",
+        default="meta-llama/Meta-Llama-3-8B",
+        help="Repository ID to download from. default to Llama-3-8B",
+    )
+    parser.add_argument(
+        "--tokenizer_path",
+        type=str,
+        default="",
+        help="the tokenizer.model path relative to repo_id",
     )
     parser.add_argument(
         "--hf_token", type=str, default=None, help="HuggingFace API token."
     )
+    parser.add_argument(
+        "--local_dir",
+        type=str,
+        default="torchtitan/datasets/tokenizer/llama3/",
+        help="local directory to save the tokenizer.model",
+    )
 
     args = parser.parse_args()
-    hf_download(args.repo_id, args.hf_token)
+    hf_download(args.repo_id, args.tokenizer_path, args.local_dir, args.hf_token)

torchtitan/datasets/hf_datasets.py

@@ -9,7 +9,7 @@
 import torch
 from torch.utils.data import DataLoader, IterableDataset
 
-from torchtitan.datasets.tokenizer import TokenizerIf
+from torchtitan.datasets.tokenizer import Tokenizer
 from torchtitan.logging_utils import logger
 
 from datasets import load_dataset, load_from_disk
@@ -29,7 +29,7 @@ class HuggingFaceDataset(IterableDataset):
         dataset_path (Optional[str]):
             Path to the dataset in the file system. If provided, data will be loaded
             from this path instead of downloaded.
-        tokenizer (TokenizerIf):
+        tokenizer (Tokenizer):
             Tokenizer used to encode data. Tokenize must implement an `encode` and `decode` method.
         seq_len (int): max sequence length
         world_size (int): number of data parallel processes participating in training
@@ -59,7 +59,7 @@ def __init__(
         self,
         dataset_name: str,
         dataset_path: Optional[str],
-        tokenizer: TokenizerIf,
+        tokenizer: Tokenizer,
         seq_len: int = 2048,
         world_size: int = 1,
         rank: int = 0,
@@ -132,7 +132,7 @@ def __iter__(self):
 def build_hf_data_loader(
     dataset_name: str,
     dataset_path: Optional[str],
-    tokenizer: TokenizerIf,
+    tokenizer: Tokenizer,
     batch_size: int,
     seq_len: int,
     world_size,

torchtitan/datasets/tokenizer/__init__.py

@@ -0,0 +1,21 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from torchtitan.datasets.tokenizer.sentencepiece import SentencePieceTokenizer
+from torchtitan.datasets.tokenizer.tiktoken import TikTokenizer
+from torchtitan.datasets.tokenizer.tokenizer import Tokenizer
+
+from torchtitan.logging_utils import logger
+
+
+def create_tokenizer(tokenizer_type: str, tokenizer_path: str) -> Tokenizer:
+    logger.info(f"Building {tokenizer_type} tokenizer locally from {tokenizer_path}")
+    if tokenizer_type == "sentencepiece":
+        return SentencePieceTokenizer(tokenizer_path)
+    elif tokenizer_type == "tiktoken":
+        return TikTokenizer(tokenizer_path)
+    else:
+        raise ValueError(f"Unknown tokenizer type: {args.type}")

torchtitan/datasets/tokenizer.py → torchtitan/datasets/tokenizer/sentencepiece.py

@@ -6,46 +6,15 @@
 
 # copied and adjusted from https://github.com/facebookresearch/llama/blob/main/llama/tokenizer.py
 
-import os
-from abc import ABC, abstractmethod
 from typing import List
 
 from sentencepiece import SentencePieceProcessor
 
+from torchtitan.datasets.tokenizer.tokenizer import Tokenizer
 from torchtitan.logging_utils import logger
 
 
-class TokenizerIf(ABC):
-    # tokenizer interface
-    def __init__(self, tokenizer_path: str):
-        assert os.path.exists(
-            tokenizer_path
-        ), f"The tokenizer path does not exist: {tokenizer_path}"
-        assert os.path.isfile(tokenizer_path), tokenizer_path
-        self._n_words = 8
-
-    @abstractmethod
-    def encode(self, *args, **kwargs) -> List[int]:
-        ...
-
-    @abstractmethod
-    def decode(self, *args, **kwargs) -> str:
-        ...
-
-    @property
-    def n_words(self) -> int:
-        return self._n_words
-
-
-def create_tokenizer(tokenizer_type: str, tokenizer_path: str) -> TokenizerIf:
-    logger.info(f"Building {tokenizer_type} tokenizer locally from {tokenizer_path}")
-    if tokenizer_type == "sentencepiece":
-        return SentencePieceTokenizer(tokenizer_path)
-    else:
-        raise ValueError(f"Unknown tokenizer type: {args.type}")
-
-
-class SentencePieceTokenizer(TokenizerIf):
+class SentencePieceTokenizer(Tokenizer):
     """
     Tokenizing and encoding/decoding text based on a SentencePiece model.