Merge branch 'main' into fix_fs_rank

.github/workflows/main.yml

@@ -127,6 +127,7 @@ jobs:
           spec: |
             version: v2
             description: GPU Tests
+            budget: ai2/oe-training
             tasks:
               - name: tests
                 image:

CHANGELOG.md

@@ -7,6 +7,16 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## Unreleased
 
+### Fixed
+
+- Fixed default value of `--tokenizer` argument to `scripts/prepare_tulu_data.py` to be an absolute path, not relative path, the script can be run from other directories.
+- Added the option to directly pass input embeddings to `OLMo` and `OLMoForCausalLM`.
+- Added support for Python 3.8.
+- Added code to throw an error if `output_attentions` is set to `True` in forward call to `OLMoForCausalLM`. This functionality hasn't been implemented yet.
+
+### Added
+- Added `output_hidden_states` argument and associated functionality to `OLMo` and `OLMoForCausalLM` to return model intermediate hidden states.
+
 ## [v0.2.4](https://github.com/allenai/OLMo/releases/tag/v0.2.4) - 2024-02-02
 
 ### Fixed

README.md

@@ -41,41 +41,17 @@ pip install ai2-olmo
 ## Models overview
 
 The core models in the OLMo family released so far are (all trained on the [Dolma dataset](https://huggingface.co/datasets/allenai/dolma)): 
-| Model | Training Tokens | Context Length |
-|------|--------|---------|
-| [OLMo 1B](https://huggingface.co/allenai/OLMo-1B)   | 3 Trillion | 2048  |
-| [OLMo 7B](https://huggingface.co/allenai/OLMo-7B) | 2.5 Trillion   |  2048  |
-| [OLMo 7B Twin 2T](https://huggingface.co/allenai/OLMo-7B-Twin-2T) | 2 Trillion  |   2048  |
-
-
-## Fine-tuning
-
-To fine-tune an OLMo model using our trainer you'll first need to prepare your dataset by tokenizing it and saving the tokens IDs to a flat numpy memory-mapped array. See [`scripts/prepare_tulu_data.py`](./scripts/prepare_tulu_data.py) for an example with the Tulu V2 dataset, which can be easily modified for other datasets.
-
-Next, prepare your training config. There are many examples in the [`configs/`](./configs) directory that you can use as a starting point. The most important thing is to make sure the model parameters (the `model` field in the config) match up with the checkpoint you're starting from. To be safe you can always start from the config that comes with the model checkpoint. At a minimum you'll need to make the following changes to the config or provide the corresponding overrides from the command line:
-
-- Update `load_path` to point to the checkpoint you want to start from.
-- Set `reset_trainer_state` to `true`.
-- Update `data.paths` to point to the `token_ids.npy` file you generated.
-- Optionally update `data.label_mask_paths` to point to the `label_mask.npy` file you generated, unless you don't need special masking for the loss.
-- Update `evaluators` to add/remove in-loop evaluations.
-
-Once you're satisfied with your training config, you can launch the training job via `torchrun`. For example:
-
-```
-torchrun --nproc_per_node=8 scripts/train.py {path_to_train_config} \
-    --data.paths=[{path_to_data}/input_ids.npy] \
-    --data.label_mask_paths=[{path_to_data}/label_mask.npy] \
-    --load_path={path_to_checkpoint} \
-    --reset_trainer_state
-```
-
-Note: passing CLI overrides like `--reset_trainer_state` is only necessary if you didn't update those fields in your config.
+| Model | Training Tokens | Context Length | Training Config | W&B Logs | Data Order File(s) ☨ |
+|-------|-----------------|:--------------:|-----------------|----------|--------------------|
+| [OLMo 1B](https://huggingface.co/allenai/OLMo-1B) | 3 Trillion | 2048 | [configs/official/OLMo-1B.yaml](https://github.com/allenai/OLMo/blob/main/configs/official/OLMo-1B.yaml) | [wandb.ai/…/OLMo-1B](https://wandb.ai/ai2-llm/OLMo-1B/reports/OLMo-1B--Vmlldzo2NzY1Njk1) | [epoch 1](https://olmo-checkpoints.org/ai2-llm/olmo-small/46zc5fly/train_data/global_indices.npy) |
+| [OLMo 7B](https://huggingface.co/allenai/OLMo-7B) | 2.5 Trillion | 2048 | [configs/official/OLMo-7B.yaml](https://github.com/allenai/OLMo/blob/main/configs/official/OLMo-7B.yaml) | [wandb.ai/…/OLMo-7B](https://wandb.ai/ai2-llm/OLMo-7B/reports/OLMo-7B--Vmlldzo2NzQyMzk5) | [epoch 1](https://olmo-checkpoints.org/ai2-llm/olmo-medium/wvc30anm/train_data/global_indices.npy), [epoch 2](https://olmo-checkpoints.org/ai2-llm/olmo-medium/wd2gxrza/train_data/global_indices.npy) |
+| [OLMo 7B Twin 2T](https://huggingface.co/allenai/OLMo-7B-Twin-2T) | 2 Trillion  | 2048 | [configs/official/OLMo-7B.yaml](https://github.com/allenai/OLMo/blob/main/configs/official/OLMo-7B.yaml) | [wandb.ai/…/OLMo-7B-Twin-2T](https://wandb.ai/ai2-llm/OLMo-7B/reports/OLMo-7B-Twin-2T--Vmlldzo2NzU0NTIz) | [epoch 1](https://olmo-checkpoints.org/ai2-llm/olmo-medium/wvc30anm/train_data/global_indices.npy) |
 
+> ☨ *See [Inspecting training data](#inspecting-training-data) below for usage.*
 
 ## Inference
 
-You can utilize our HuggingFace integration to run inference on the olmo checkpoints:
+You can utilize our Hugging Face integration to run inference on the olmo checkpoints:
 
 ```python
 from hf_olmo import * # registers the Auto* classes
@@ -91,18 +67,17 @@ response = olmo.generate(**inputs, max_new_tokens=100, do_sample=True, top_k=50,
 print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
 ```
 
-Alternatively, with the huggingface pipeline abstraction:
+Alternatively, with the Hugging Face pipeline abstraction:
 
 ```python
 from transformers import pipeline
 olmo_pipe = pipeline("text-generation", model="allenai/OLMo-7B")
 print(olmo_pipe("Language modeling is"))
 ```
 
-
 ### Inference on finetuned checkpoints
 
-If you finetune the model using the code above, you can use the conversion script to convert a native OLMo checkpoint to a HuggingFace-compatible checkpoint
+If you finetune the model using the code above, you can use the conversion script to convert a native OLMo checkpoint to a Hugging Face-compatible checkpoint
 
 ```bash
 python hf_olmo/convert_olmo_to_hf.py --checkpoint-dir /path/to/checkpoint
@@ -116,7 +91,111 @@ olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B", torch_dtype=torch
 
 The quantized model is more sensitive to typing / cuda, so it is recommended to pass the inputs as inputs.input_ids.to('cuda') to avoid potential issues.
 
+## Reproducibility
+
+### Training
+
+The configs used to train the official OLMo models are provided in the [`configs/official/`](https://github.com/allenai/OLMo/blob/main/configs/official) directory.
+
+Note that while the training and validation data is public and free to download, the paths to the data within those configs are pointed at a CloudFlare R2 bucket, which requires an API key for programmatic access.
+So in order to use any of these configs to reproduce a training run you'll first have to download the corresponding data to a location of your choosing and then update the paths in the config accordingly.
+
+You can derive the public HTTP URL from an R2 URL by replacing `r2://olmo-data` with `https://olmo-data.org`.
+For example, if the R2 data URL is:
+
+`r2://olmo-data/preprocessed/olmo-mix/v1_5/gpt-neox-20b-pii-special/part-000-00000.npy`
+
+then the corresponding public URL is:
+
+`https://olmo-data.org/preprocessed/olmo-mix/v1_5/gpt-neox-20b-pii-special/part-000-00000.npy`
+
+Once you've updated the data paths in the config you can launch a training run via `torchrun`. For example, to launch the 1B model training on a single 8x GPU node, you would run:
+
+```bash
+torchrun --nproc_per_node=8 scripts/train.py configs/official/OLMo-1B.yaml
+```
+
+You can use the same method to launch multi-node jobs as well. See [the documentation](https://pytorch.org/docs/stable/elastic/run.html) for `torchrun` to understand the additional arguments you'll need to configure the rendezvous backend / endpoint.
+
+### Inspecting training data
+
+You may be interesting in inspecting the exact tokens that composed a particular batch during the training of one of the OLMo models.
+We provide tools to do this, but first you'll need to download the data as above (unless you have an R2 API key) and update the corresponding config accordingly.
+
+Then take note of the URL of the data order file you want, which can be found in the [Models Overview](#models-overview) table. For example, the data order file for the first epoch of the OLMo-7B model is [https://olmo-checkpoints.org/ai2-llm/olmo-medium/wvc30anm/train_data/global_indices.npy](https://olmo-checkpoints.org/ai2-llm/olmo-small/46zc5fly/train_data/global_indices.npy).
+
+Once you have that you can use this snippet to inspect the data within a particular batch:
+
+```python
+import numpy as np
+from cached_path import cached_path
+
+from olmo.config import TrainConfig
+from olmo.data import build_memmap_dataset
+
+# Update these paths to what you want:
+data_order_file_path = cached_path("https://olmo-checkpoints.org/ai2-llm/olmo-medium/wvc30anm/train_data/global_indices.npy")
+train_config_path = "configs/official/OLMo-7B.yaml"
+
+
+cfg = TrainConfig.load(train_config_path)
+dataset = build_memmap_dataset(cfg, cfg.data)
+batch_size = cfg.global_train_batch_size
+global_indices = np.memmap(data_order_file_path, mode="r+", dtype=np.uint32)
+
+
+def get_batch_instances(batch_idx: int) -> list[list[int]]:
+    batch_start = batch_idx * batch_size
+    batch_end = (batch_idx + 1) * batch_size
+    batch_indices = global_indices[batch_start:batch_end]
+    batch_instances = []
+    for index in batch_indices:
+        token_ids = dataset[index]["input_ids"].tolist()
+        batch_instances.append(token_ids)
+    return batch_instances
+
+
+# Get all 2048 x 2048 token IDs in the first batch.
+get_batch_instances(0)
+```
+
+
+## Fine-tuning
+
+To fine-tune an OLMo model using our trainer you'll first need to prepare your dataset by tokenizing it and saving the tokens IDs to a flat numpy memory-mapped array. See [`scripts/prepare_tulu_data.py`](./scripts/prepare_tulu_data.py) for an example with the Tulu V2 dataset, which can be easily modified for other datasets.
+
+Next, prepare your training config. There are many examples in the [`configs/`](https://github.com/allenai/OLMo/blob/main/configs) directory that you can use as a starting point. The most important thing is to make sure the model parameters (the `model` field in the config) match up with the checkpoint you're starting from. To be safe you can always start from the config that comes with the model checkpoint. At a minimum you'll need to make the following changes to the config or provide the corresponding overrides from the command line:
+
+- Update `load_path` to point to the checkpoint you want to start from.
+- Set `reset_trainer_state` to `true`.
+- Update `data.paths` to point to the `token_ids.npy` file you generated.
+- Optionally update `data.label_mask_paths` to point to the `label_mask.npy` file you generated, unless you don't need special masking for the loss.
+- Update `evaluators` to add/remove in-loop evaluations.
+
+Once you're satisfied with your training config, you can launch the training job via `torchrun`. For example:
+
+```
+torchrun --nproc_per_node=8 scripts/train.py {path_to_train_config} \
+    --data.paths=[{path_to_data}/input_ids.npy] \
+    --data.label_mask_paths=[{path_to_data}/label_mask.npy] \
+    --load_path={path_to_checkpoint} \
+    --reset_trainer_state
+```
+
+Note: passing CLI overrides like `--reset_trainer_state` is only necessary if you didn't update those fields in your config.
 
 ## Evaluation
 
 Additional tools for evaluating OLMo models are available at the [OLMo Eval](https://github.com/allenai/ai2-olmo-eval) repo.
+
+## Citing
+
+```bibtex
+@article{OLMo,
+  title={OLMo: Accelerating the Science of Language Models},
+  author={Dirk Groeneveld and Iz Beltagy and Pete Walsh and Akshita Bhagia and Rodney Kinney and Oyvind Tafjord and A. Jha and Hamish Ivison and Ian Magnusson and Yizhong Wang and Shane Arora and David Atkinson and Russell Authur and Khyathi Raghavi Chandu and Arman Cohan and Jennifer Dumas and Yanai Elazar and Yuling Gu and Jack Hessel and Tushar Khot and William Merrill and Jacob Daniel Morrison and Niklas Muennighoff and Aakanksha Naik and Crystal Nam and Matthew E. Peters and Valentina Pyatkin and Abhilasha Ravichander and Dustin Schwenk and Saurabh Shah and Will Smith and Emma Strubell and Nishant Subramani and Mitchell Wortsman and Pradeep Dasigi and Nathan Lambert and Kyle Richardson and Luke Zettlemoyer and Jesse Dodge and Kyle Lo and Luca Soldaini and Noah A. Smith and Hanna Hajishirzi},
+  year={2024},
+  url={https://api.semanticscholar.org/CorpusID:267365485},
+  journal={arXiv preprint},
+}
+```