Add MicroLlama training support

README.md

@@ -73,30 +73,31 @@ LitGPT has 🤯 **custom, from-scratch implementations** of [20+ LLMs](tutorials
 
 | Model | Model size | Author | Reference |
 |----|----|----|----|
-| CodeGemma | 7B | Google | [Google Team, Google Deepmind](https://ai.google.dev/gemma/docs/codegemma) |
-| Code Llama | 7B, 13B, 34B, 70B | Meta AI | [Rozière et al. 2023](https://arxiv.org/abs/2308.12950) |
-| Danube2 | 1.8B | H2O.ai | [H2O.ai](https://h2o.ai/platform/danube-1-8b/) |
-| Dolly | 3B, 7B, 12B | Databricks | [Conover et al. 2023](https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm) |
-| Falcon | 7B, 40B, 180B | TII UAE | [TII 2023](https://falconllm.tii.ae)                                                                                         |
-| FreeWilly2 (Stable Beluga 2) | 70B | Stability AI | [Stability AI 2023](https://stability.ai/blog/stable-beluga-large-instruction-fine-tuned-models)                             |
-| Function Calling Llama 2 | 7B | Trelis | [Trelis et al. 2023](https://huggingface.co/Trelis/Llama-2-7b-chat-hf-function-calling-v2)                                   |
-| Gemma | 2B, 7B | Google | [Google Team, Google Deepmind](https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf)                         |
-| Llama 2 | 7B, 13B, 70B | Meta AI | [Touvron et al. 2023](https://arxiv.org/abs/2307.09288)                                                                      |
-| Llama 3 | 8B, 70B | Meta AI | [Meta AI 2024](https://github.com/meta-llama/llama3)                                                                     |
-| LongChat | 7B, 13B | LMSYS | [LongChat Team 2023](https://lmsys.org/blog/2023-06-29-longchat/)                                                            |
-| Mixtral MoE | 8x7B | Mistral AI | [Mistral AI 2023](https://mistral.ai/news/mixtral-of-experts/)                                                                      |
-| Mistral | 7B | Mistral AI | [Mistral AI 2023](https://mistral.ai/news/announcing-mistral-7b/)                                                                         |
-| Nous-Hermes | 7B, 13B, 70B | NousResearch | [Org page](https://huggingface.co/NousResearch)                                                                              |
-| OpenLLaMA | 3B, 7B, 13B | OpenLM Research | [Geng & Liu 2023](https://github.com/openlm-research/open_llama)                                                             |
-| Phi | 1.3B, 2.7B | Microsoft Research  | [Li et al. 2023](https://arxiv.org/abs/2309.05463)                                                                           |
+| CodeGemma | 7B | Google | [Google Team, Google Deepmind](https://ai.google.dev/gemma/docs/codegemma)                                                                 |
+| Code Llama | 7B, 13B, 34B, 70B | Meta AI | [Rozière et al. 2023](https://arxiv.org/abs/2308.12950)                                                                   |
+| Danube2 | 1.8B | H2O.ai | [H2O.ai](https://h2o.ai/platform/danube-1-8b/)                                                                                             |
+| Dolly | 3B, 7B, 12B | Databricks | [Conover et al. 2023](https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm)      |
+| Falcon | 7B, 40B, 180B | TII UAE | [TII 2023](https://falconllm.tii.ae)                                                                                              |
+| FreeWilly2 (Stable Beluga 2) | 70B | Stability AI | [Stability AI 2023](https://stability.ai/blog/stable-beluga-large-instruction-fine-tuned-models)                 |
+| Function Calling Llama 2 | 7B | Trelis | [Trelis et al. 2023](https://huggingface.co/Trelis/Llama-2-7b-chat-hf-function-calling-v2)                                  |
+| Gemma | 2B, 7B | Google | [Google Team, Google Deepmind](https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf)                                       |
+| Llama 2 | 7B, 13B, 70B | Meta AI | [Touvron et al. 2023](https://arxiv.org/abs/2307.09288)                                                                           |
+| Llama 3 | 8B, 70B | Meta AI | [Meta AI 2024](https://github.com/meta-llama/llama3)                                                                                   |
+| LongChat | 7B, 13B | LMSYS | [LongChat Team 2023](https://lmsys.org/blog/2023-06-29-longchat/)                                                                       |
+| MicroLlama | 300M | Ken Wang | [MicroLlama repo](https://github.com/keeeeenw/MicroLlama)
+| Mixtral MoE | 8x7B | Mistral AI | [Mistral AI 2023](https://mistral.ai/news/mixtral-of-experts/)                                                                     |
+| Mistral | 7B | Mistral AI | [Mistral AI 2023](https://mistral.ai/news/announcing-mistral-7b/)                                                                        |
+| Nous-Hermes | 7B, 13B, 70B | NousResearch | [Org page](https://huggingface.co/NousResearch)                                                                          |
+| OpenLLaMA | 3B, 7B, 13B | OpenLM Research | [Geng & Liu 2023](https://github.com/openlm-research/open_llama)                                                         |
+| Phi | 1.3B, 2.7B | Microsoft Research  | [Li et al. 2023](https://arxiv.org/abs/2309.05463)                                                                          |
 | Platypus | 7B, 13B, 70B |  Lee et al. | [Lee, Hunter, and Ruiz 2023](https://arxiv.org/abs/2308.07317)                                                               |
-| Pythia | {14,31,70,160,410}M, {1,1.4,2.8,6.9,12}B | EleutherAI | [Biderman et al. 2023](https://arxiv.org/abs/2304.01373)                                                                     |
-| RedPajama-INCITE | 3B, 7B | Together | [Together 2023](https://together.ai/blog/redpajama-models-v1)                                                                |
-| StableCode | 3B | Stability AI | [Stability AI 2023](https://stability.ai/blog/stablecode-llm-generative-ai-coding)                                           |
-| StableLM  | 3B, 7B | Stability AI | [Stability AI 2023](https://github.com/Stability-AI/StableLM)                                                                |
-| StableLM Zephyr | 3B | Stability AI | [Stability AI 2023](https://stability.ai/blog/stablecode-llm-generative-ai-coding)                                           |
-| TinyLlama | 1.1B | Zhang et al. | [Zhang et al. 2023](https://github.com/jzhang38/TinyLlama)                                                                   |
-| Vicuna | 7B, 13B, 33B | LMSYS | [Li et al. 2023](https://lmsys.org/blog/2023-03-30-vicuna/)
+| Pythia | {14,31,70,160,410}M, {1,1.4,2.8,6.9,12}B | EleutherAI | [Biderman et al. 2023](https://arxiv.org/abs/2304.01373)                                            |
+| RedPajama-INCITE | 3B, 7B | Together | [Together 2023](https://together.ai/blog/redpajama-models-v1)                                                                 |
+| StableCode | 3B | Stability AI | [Stability AI 2023](https://stability.ai/blog/stablecode-llm-generative-ai-coding)                                                  |
+| StableLM  | 3B, 7B | Stability AI | [Stability AI 2023](https://github.com/Stability-AI/StableLM)                                                                    |
+| StableLM Zephyr | 3B | Stability AI | [Stability AI 2023](https://stability.ai/blog/stablecode-llm-generative-ai-coding)                                             |
+| TinyLlama | 1.1B | Zhang et al. | [Zhang et al. 2023](https://github.com/jzhang38/TinyLlama)                                                                         |
+| Vicuna | 7B, 13B, 33B | LMSYS | [Li et al. 2023](https://lmsys.org/blog/2023-03-30-vicuna/)                                                                          |
 
 **Tip**: You can list all available models by running the `litgpt download list` command.
 

config_hub/pretrain/microllama.yaml

@@ -0,0 +1,115 @@
+
+# The name of the model to pretrain. Choose from names in ``litgpt.config``. Mutually exclusive with
+# ``model_config``. (type: Optional[str], default: null)
+model_name: micro-llama-300M
+
+# A ``litgpt.Config`` object to define the model architecture. Mutually exclusive with
+# ``model_config``. (type: Optional[Config], default: null)
+model_config:
+
+# Directory in which to save checkpoints and logs. If running in a Lightning Studio Job, look for it in
+# /teamspace/jobs/<job-name>/share. (type: <class 'Path'>, default: out/pretrain)
+out_dir: out/pretrain/micro-llama
+
+# The precision to use for pretraining. Possible choices: "bf16-true", "bf16-mixed", "32-true". (type: Optional[str], default: null)
+precision: bf16-mixed
+
+# Optional path to a checkpoint directory to initialize the model from.
+# Useful for continued pretraining. Mutually exclusive with ``resume``. (type: Optional[Path], default: null)
+initial_checkpoint_dir:
+
+# Path to a checkpoint directory to resume from in case training was interrupted, or ``True`` to resume
+# from the latest checkpoint in ``out_dir``. (type: Union[bool, Path], default: False)
+resume: false
+
+# Data-related arguments. If not provided, the default is ``litgpt.data.TinyLlama``.
+data: MicroLlama
+
+# Training-related arguments. See ``litgpt.args.TrainArgs`` for details
+train:
+
+  # Number of optimizer steps between saving checkpoints (type: Optional[int], default: 1000)
+  save_interval: 1000
+
+  # Number of iterations between logging calls (type: int, default: 1)
+  log_interval: 1
+
+  # Number of samples between optimizer steps across data-parallel ranks (type: int, default: 48)
+  # Scale this number according to the number of GPU and memory size per GPU
+  # For example, we used 48 for 4 x 24G 4090 
+  global_batch_size: 48
+
+  # Number of samples per data-parallel rank (type: int, default: 12)
+  # Scale this number according to the memory size per GPU
+  # For example, we used 12 for 24G 4090
+  micro_batch_size: 12
+
+  # Number of iterations with learning rate warmup active (type: int, default: 2000)
+  lr_warmup_steps: 2000
+
+  # Number of epochs to train on (type: Optional[int], default: null)
+  epochs:
+
+  # Total number of tokens to train on (type: Optional[int], default: 3000000000000)
+  max_tokens: 3000000000000
+
+  # Limits the number of optimizer steps to run. (type: Optional[int], default: null)
+  max_steps:
+
+  # Limits the length of samples. Off by default (type: Optional[int], default: null)
+  max_seq_length: 2048
+
+  # Whether to tie the embedding weights with the language modeling head weights. (type: Optional[bool], default: False)
+  tie_embeddings:
+
+  #   (type: Optional[float], default: 1.0)
+  max_norm: 1.0
+
+  #   (type: float, default: 4e-05)
+  min_lr: 4.0e-05
+
+# Evaluation-related arguments. See ``litgpt.args.EvalArgs`` for details
+eval:
+
+  # Number of optimizer steps between evaluation calls (type: int, default: 1000)
+  interval: 1000
+
+  # Number of tokens to generate (type: Optional[int], default: null)
+  max_new_tokens:
+
+  # Number of iterations (type: int, default: 100)
+  max_iters: 100
+
+  # Whether to evaluate on the validation set at the beginning of the training
+  initial_validation: false
+
+# Optimizer-related arguments
+optimizer:
+
+  class_path: torch.optim.AdamW
+
+  init_args:
+
+    #   (type: float, default: 0.001)
+    lr: 4e-4
+
+    #   (type: float, default: 0.01)
+    weight_decay: 0.1
+
+    #   (type: tuple, default: (0.9,0.999))
+    betas:
+      - 0.9
+      - 0.95
+
+# How many devices/GPUs to use. Uses all GPUs by default. (type: Union[int, str], default: auto)
+devices: auto
+
+# Optional path to the tokenizer dir that was used for preprocessing the dataset. Only some data
+# module require this. (type: Optional[Path], default: null)
+tokenizer_dir: checkpoints/meta-llama/Llama-2-7b-hf
+
+# The name of the logger to send metrics to. (type: Literal['wandb', 'tensorboard', 'csv'], default: tensorboard)
+logger_name: tensorboard
+
+# The random seed to use for reproducibility. (type: int, default: 42)
+seed: 42

litgpt/config.py

@@ -1548,7 +1548,7 @@ def norm_class(self) -> Type:
         rotary_percentage=1.0,
         parallel_residual=False,
         bias=False,
-        norm_class_name="RMSNorm",  # original TinyLlama uses FusedRMSNorm
+        norm_class_name="RMSNorm",  # original TinyLlama use FusedRMSNorm
         norm_eps=1e-5,
         mlp_class_name="LLaMAMLP",
         intermediate_size=5632,
@@ -1563,6 +1563,32 @@ def norm_class(self) -> Type:
         configs.append(copy)
 
 
+############
+# MicroLlama
+############
+micro_llama = [
+    dict(
+        name="micro-llama-300M",
+        hf_config=dict(org="keeeeenw", name="MicroLlama"),
+        block_size=2048,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=12,
+        n_head=16,
+        n_embd=1024,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        norm_class_name="RMSNorm",  # original TinyLlama and MicroLlama use FusedRMSNorm
+        norm_eps=1e-5,
+        mlp_class_name="LLaMAMLP",
+        intermediate_size=5632,
+        n_query_groups=4,
+    )
+]
+configs.extend(micro_llama)
+
+
 ##########################
 # Trelis Function Calling
 ##########################

litgpt/data/__init__.py

@@ -15,6 +15,7 @@
 from litgpt.data.tinyllama import TinyLlama
 from litgpt.data.tinystories import TinyStories
 from litgpt.data.openwebtext import OpenWebText
+from litgpt.data.microllama import MicroLlama
 
 
 __all__ = [
@@ -34,5 +35,6 @@
     "TextFiles",
     "TinyLlama",
     "TinyStories",
+    "MicroLlama"
     "get_sft_collate_fn",
 ]

litgpt/data/microllama.py

@@ -0,0 +1,13 @@
+# Copyright Lightning AI. Licensed under the Apache License 2.0, see LICENSE file.
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Union
+
+from litgpt.data import TinyLlama
+
+@dataclass
+class MicroLlama(TinyLlama):
+    """The MicroLlama data module is composed of only SlimPajama data."""
+
+    def __init__(self, data_path: Union[str, Path] = Path("data/"), seed: int = 42, num_workers: int = 8):
+        super().__init__(data_path=data_path, seed=seed, num_workers=num_workers, use_starcoder=False)

litgpt/data/prepare_slimpajama.py

@@ -11,6 +11,8 @@
 
 
 class SlimPajamaDataRecipe(DataChunkRecipe):
+    is_generator = True
+
     def __init__(self, tokenizer: Tokenizer, chunk_size: int):
         super().__init__(chunk_size)
         self.tokenizer = tokenizer

litgpt/data/prepare_starcoder.py

@@ -18,6 +18,8 @@
 
 
 class StarcoderDataRecipe(DataChunkRecipe):
+    is_generator = True
+
     def __init__(self, tokenizer: Tokenizer, chunk_size: int):
         super().__init__(chunk_size)
         self.tokenizer = tokenizer

litgpt/data/tinyllama.py

@@ -24,6 +24,8 @@ class TinyLlama(DataModule):
     """The random seed for shuffling the dataset."""
     num_workers: int = 8
     """How many DataLoader processes to use for loading."""
+    use_starcoder: bool = True
+    """Toggle for using Starcoder data."""
 
     batch_size: int = field(init=False, repr=False, default=1)
     seq_length: int = field(init=False, repr=False, default=2048)
@@ -32,7 +34,11 @@ def __post_init__(self):
         # Could be a remote path (s3://) or a local path
         self.slimpajama_train = str(self.data_path).rstrip("/") + "/slimpajama/train"
         self.slimpajama_val = str(self.data_path).rstrip("/") + "/slimpajama/val"
-        self.starcoder_train = str(self.data_path).rstrip("/") + "/starcoder"
+        self.required_paths = [self.slimpajama_train, self.slimpajama_val]
+
+        if self.use_starcoder:
+            self.starcoder_train = str(self.data_path).rstrip("/") + "/starcoder"
+            self.required_paths += [self.starcoder_train]
 
     def connect(
         self, tokenizer: Optional[Tokenizer] = None, batch_size: int = 1, max_seq_length: Optional[int] = None
@@ -41,7 +47,7 @@ def connect(
         self.seq_length = max_seq_length + 1  # Increase by one because we need the next token as well
 
     def prepare_data(self) -> None:
-        for path in (self.slimpajama_train, self.slimpajama_val, self.starcoder_train):
+        for path in self.required_paths:
             if not path.startswith("s3://") and not Path(path).is_dir():
                 raise FileNotFoundError(
                     "The data path for TinyLlama is expected to be the directory containing these subdirectories:"
@@ -52,28 +58,33 @@ def prepare_data(self) -> None:
     def train_dataloader(self) -> DataLoader:
         from litdata.streaming import CombinedStreamingDataset, StreamingDataLoader, StreamingDataset, TokensLoader
 
-        train_datasets = [
-            StreamingDataset(
-                input_dir=self.slimpajama_train,
-                item_loader=TokensLoader(block_size=self.seq_length),
-                shuffle=True,
-                drop_last=True,
-            ),
-            StreamingDataset(
-                input_dir=self.starcoder_train,
-                item_loader=TokensLoader(block_size=self.seq_length),
-                shuffle=True,
-                drop_last=True,
-            ),
-        ]
-
-        # Mix SlimPajama data and Starcoder data with these proportions:
-        weights = (0.693584, 0.306416)
-        combined_dataset = CombinedStreamingDataset(
-            datasets=train_datasets, seed=self.seed, weights=weights, iterate_over_all=False
+        slim_train_data = StreamingDataset(
+            input_dir=self.slimpajama_train,
+            item_loader=TokensLoader(block_size=self.seq_length),
+            shuffle=True,
+            drop_last=True,
         )
+        train_data = slim_train_data
+
+        if self.use_starcoder:
+            train_datasets = [
+                slim_train_data,
+                StreamingDataset(
+                    input_dir=self.starcoder_train,
+                    item_loader=TokensLoader(block_size=self.seq_length),
+                    shuffle=True,
+                    drop_last=True,
+                ),
+            ]
+
+            # Mix SlimPajama data and Starcoder data with these proportions:
+            weights = (0.693584, 0.306416)
+            train_data = CombinedStreamingDataset(
+                datasets=train_datasets, seed=self.seed, weights=weights, iterate_over_all=False
+            )
+
         train_dataloader = StreamingDataLoader(
-            combined_dataset, batch_size=self.batch_size, pin_memory=True, num_workers=self.num_workers, drop_last=True
+            train_data, batch_size=self.batch_size, pin_memory=True, num_workers=self.num_workers, drop_last=True
         )
         return train_dataloader
 

litgpt/pretrain.py

@@ -20,7 +20,7 @@
 from litgpt import Tokenizer
 from litgpt.args import EvalArgs, TrainArgs
 from litgpt.config import name_to_config
-from litgpt.data import DataModule, TinyLlama
+from litgpt.data import DataModule, TinyLlama, MicroLlama
 from litgpt.model import GPT, Block, CausalSelfAttention, Config, LLaMAMLP
 from litgpt.utils import (
     CycleIterator,