Merge pull request #752 from allenai/shanea/add-olmo2-converter

Add OLMo 2 checkpoint converter and update docs

docs/Checkpoints.md

@@ -21,7 +21,7 @@ Unless otherwise specified, an OLMo checkpoint is assumed to be unsharded. OLMo
 Each unsharded checkpoint directory consists of:
 
 - `config.yaml`: the config at that training step.
-- `model.pt`, `optim.pt`, `train.safetensors`: model, optimizer and training state at that training step.
+- `model.safetensors`, `optim.safetensors`, `train.pt`: model, optimizer and training state at that training step. Checkpoints of older OLMo releases use the `.pt` extension instead.
 
 URLs to checkpoints at intermediate steps of our official models' trainings can be found in the csv files under [`checkpoints/official/`](https://github.com/allenai/OLMo/blob/main/checkpoints/official). These 'directory' URLs cannot currently be directly accessed, but files within the directory are publicly accessible.
 
@@ -40,10 +40,10 @@ Transformers Checkpoints
 
 These checkpoints can be used with the OLMo implementation in the Transformers library. Since the OLMo implementation is integrated into the library, OLMo models support most Transformers model functionality. These checkpoints cannot be used with the pretraining/fine-tuning script provided in this repo.
 
-Transformers checkpoints can be found in HF Hub repos that end in `-hf` (e.g. [OLMo-1.7-7B-hf](https://huggingface.co/allenai/OLMo-1.7-7B-hf)). An OLMo checkpoint can be converted into its Transformers equivalent using [convert_olmo_to_hf_new.py](https://github.com/allenai/OLMo/blob/main/scripts/convert_olmo_to_hf_new.py). Example usage:
+Transformers checkpoints can be found in most of our HF Hub repos (e.g. [OLMo-2-1124-7B](https://huggingface.co/allenai/OLMo-2-1124-7B)). An OLMo 2 checkpoint can be converted into its Transformers equivalent using [convert_olmo2_to_hf.py](https://github.com/allenai/OLMo/blob/main/scripts/convert_olmo2_to_hf.py). Similarly, the script for OLMo 1 is [convert_olmo_to_hf_new.py](https://github.com/allenai/OLMo/blob/main/scripts/convert_olmo_to_hf_new.py). Example usage:
 
 ```bash
-python scripts/convert_olmo_to_hf_new.py --input_dir /path/to/olmo/checkpoint --output_dir /path/to/hf/checkpoint/ --tokenizer_json_path tokenizers/allenai_gpt-neox-olmo-dolma-v1_5.json
+python scripts/convert_olmo2_to_hf.py --input_dir /path/to/olmo/checkpoint --output_dir /path/to/hf/checkpoint/ --tokenizer_json_path tokenizers/allenai_gpt-neox-olmo-dolma-v1_5.json
 ```
 
 *Warning*: As we continue to develop and improve OLMo, our implementation in this repo may become incompatible with the implementation in the Transformer library. During these periods, OLMo checkpoints may not be convertible to Transformers checkpoint. At present, all OLMo checkpoints of our officially released models are convertible to Transformers checkpoints.

scripts/convert_olmo2_to_hf.py

@@ -0,0 +1,308 @@
+# Copyright 2024 EleutherAI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import gc
+import json
+import os
+import shutil
+from pathlib import Path
+from typing import Any, Dict
+
+import torch
+import yaml
+from tokenizers import Tokenizer
+from transformers import Olmo2Config, Olmo2ForCausalLM
+from transformers.models.gpt2.tokenization_gpt2_fast import GPT2TokenizerFast
+
+"""
+Sample usage:
+
+```
+python src/transformers/models/olmo2/convert_olmo2_weights_to_hf.py \
+    --input_dir /path/to/downloaded/olmo2/weights --output_dir /output/path
+```
+
+Thereafter, models can be loaded via:
+
+```py
+from transformers import Olmo2ForCausalLM, AutoTokenizer
+
+model = Olmo2ForCausalLM.from_pretrained("/output/path")
+tokenizer = AutoTokenizer.from_pretrained("/output/path")
+```
+
+Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
+come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
+"""
+
+
+def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
+    return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
+
+
+def read_json(path):
+    with open(path, "r") as f:
+        return json.load(f)
+
+
+def write_json(text, path):
+    with open(path, "w") as f:
+        json.dump(text, f)
+
+
+def write_model(
+    model_path,
+    input_base_path,
+    include_tokenizer=True,
+    tokenizer_path=None,
+    safe_serialization=True,
+    fix_eos_token_id=True,
+    tmp_cleanup=True,
+):
+    os.makedirs(model_path, exist_ok=True)
+    tmp_model_path = os.path.join(model_path, "tmp")
+    os.makedirs(tmp_model_path, exist_ok=True)
+
+    config_path = Path(input_base_path) / "config.yaml"
+    olmo2_config = yaml.safe_load(config_path.read_text())["model"]
+
+    if not olmo2_config.get("attention_layer_norm", False):
+        raise RuntimeError("OLMo2 checkpoints must have attention layer norm")
+    if not olmo2_config.get("norm_after", False):
+        raise RuntimeError("OLMo2 checkpoints must set norm_after to True")
+
+    n_layers = olmo2_config["n_layers"]
+    n_heads = olmo2_config["n_heads"]
+    dim = olmo2_config["d_model"]
+    dims_per_head = dim // n_heads
+    base = olmo2_config["rope_theta"]
+    inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
+    max_position_embeddings = olmo2_config["max_sequence_length"]
+
+    vocab_size = olmo2_config.get("embedding_size", olmo2_config["vocab_size"])
+
+    if olmo2_config.get("n_kv_heads", None) is not None:
+        num_key_value_heads = olmo2_config["n_kv_heads"]  # for GQA / MQA
+    elif olmo2_config["multi_query_attention"]:  # compatibility with other checkpoints
+        num_key_value_heads = 1
+    else:
+        num_key_value_heads = n_heads
+
+    print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
+
+    # Not sharded
+    # (The sharded implementation would also work, but this is simpler.)
+    loaded = torch.load(os.path.join(input_base_path, "model.pt"), map_location="cpu")
+
+    param_count = 0
+    index_dict: Dict[str, Any] = {"weight_map": {}}
+    for layer_i in range(n_layers):
+        filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
+        # Unsharded
+        # TODO: Layernorm stuff
+        # TODO: multi query attention
+        fused_dims = [dim, dims_per_head * num_key_value_heads, dims_per_head * num_key_value_heads]
+        q_proj_weight, k_proj_weight, v_proj_weight = torch.split(
+            loaded[f"transformer.blocks.{layer_i}.att_proj.weight"], fused_dims, dim=0
+        )
+        up_proj_weight, gate_proj_weight = torch.chunk(
+            loaded[f"transformer.blocks.{layer_i}.ff_proj.weight"], 2, dim=0
+        )
+        state_dict = {
+            f"model.layers.{layer_i}.self_attn.q_proj.weight": q_proj_weight,
+            f"model.layers.{layer_i}.self_attn.k_proj.weight": k_proj_weight,
+            f"model.layers.{layer_i}.self_attn.v_proj.weight": v_proj_weight,
+            f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[
+                f"transformer.blocks.{layer_i}.attn_out.weight"
+            ],
+            f"model.layers.{layer_i}.self_attn.q_norm.weight": loaded[
+                f"transformer.blocks.{layer_i}.q_norm.weight"
+            ],
+            f"model.layers.{layer_i}.self_attn.k_norm.weight": loaded[
+                f"transformer.blocks.{layer_i}.k_norm.weight"
+            ],
+            f"model.layers.{layer_i}.mlp.gate_proj.weight": gate_proj_weight,
+            f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"transformer.blocks.{layer_i}.ff_out.weight"],
+            f"model.layers.{layer_i}.mlp.up_proj.weight": up_proj_weight,
+            f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[
+                f"transformer.blocks.{layer_i}.attn_norm.weight"
+            ],
+            f"model.layers.{layer_i}.post_feedforward_layernorm.weight": loaded[
+                f"transformer.blocks.{layer_i}.ff_norm.weight"
+            ],
+        }
+
+        state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
+
+        for k, v in state_dict.items():
+            index_dict["weight_map"][k] = filename
+            param_count += v.numel()
+        torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+    filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
+
+    # Unsharded
+    # TODO: Deal with weight-tying
+    state_dict = {
+        "model.embed_tokens.weight": loaded["transformer.wte.weight"],
+        "model.norm.weight": loaded["transformer.ln_f.weight"],
+        "lm_head.weight": loaded["transformer.ff_out.weight"]
+        if "transformer.ff_out.weight" in loaded
+        else loaded["transformer.wte.weight"],
+    }
+
+    for k, v in state_dict.items():
+        index_dict["weight_map"][k] = filename
+        param_count += v.numel()
+    torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+    # Write configs
+    index_dict["metadata"] = {"total_size": param_count * 2}
+    write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
+
+    if olmo2_config.get("mlp_hidden_size", None) is not None:
+        intermediate_size = olmo2_config["mlp_hidden_size"] // 2
+    else:
+        intermediate_size = (dim * olmo2_config["mlp_ratio"]) // 2
+
+    if fix_eos_token_id and olmo2_config["eos_token_id"] == 0:
+        # Fixing a bug in OLMo where eos token id was incorrectly set
+        print("Changing eos_token_id from 0 to 50279.")
+        olmo2_config["eos_token_id"] = 50279
+
+    config = Olmo2Config(
+        vocab_size=vocab_size,
+        hidden_size=dim,
+        intermediate_size=intermediate_size,
+        num_hidden_layers=n_layers,
+        num_attention_heads=n_heads,
+        num_key_value_heads=num_key_value_heads,
+        max_position_embeddings=max_position_embeddings,
+        pad_token_id=olmo2_config["pad_token_id"],
+        bos_token_id=None,
+        eos_token_id=olmo2_config["eos_token_id"],
+        tie_word_embeddings=olmo2_config["weight_tying"],
+        rms_norm_eps=olmo2_config["layer_norm_eps"],
+        rope_theta=base,
+    )
+    config.save_pretrained(tmp_model_path)
+
+    # Make space so we can load the model properly now.
+    del state_dict
+    del loaded
+    gc.collect()
+
+    if include_tokenizer:
+        _write_tokenizer(model_path, config, input_base_path, tokenizer_path)
+
+    print("Loading the checkpoint in a OLMo2 model.")
+    model = Olmo2ForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.float32, low_cpu_mem_usage=True)
+    # Avoid saving this as part of the config.
+    del model.config._name_or_path
+    print("Saving in the Transformers format.")
+    model.save_pretrained(model_path, safe_serialization=safe_serialization)
+    if tmp_cleanup:
+        # Make cleanup optional; attempting to `rmtree` the `tmp_model_path` causes
+        # errors if using NFS.
+        shutil.rmtree(tmp_model_path)
+
+
+def _write_tokenizer(
+    output_path: Path,
+    config: Olmo2Config,
+    checkpoint_dir: str,
+    input_tokenizer_path: Path | None,
+) -> None:
+    print(f"Saving a {GPT2TokenizerFast.__name__} to {output_path}.")
+
+    if input_tokenizer_path is not None:
+        base_tokenizer = Tokenizer.from_file(str(input_tokenizer_path))
+    else:
+        config_path = Path(checkpoint_dir) / "config.yaml"
+        tokenizer_config = yaml.safe_load(config_path.read_text())["tokenizer"]
+
+        # Initialize tokenizer and validate vocab size.
+        if Path(tokenizer_config["identifier"]).is_file():
+            base_tokenizer = Tokenizer.from_file(tokenizer_config["identifier"])
+        else:
+            base_tokenizer = Tokenizer.from_pretrained(tokenizer_config["identifier"])
+
+    eos_token_id = config.eos_token_id if config.eos_token_id is not None else base_tokenizer.get_vocab_size() - 1
+    pad_token_id = config.pad_token_id if config.pad_token_id is not None else eos_token_id
+
+    tokenizer = GPT2TokenizerFast(
+        tokenizer_object=base_tokenizer,
+        eos_token=base_tokenizer.decode([eos_token_id], skip_special_tokens=False),
+        pad_token=base_tokenizer.decode([pad_token_id], skip_special_tokens=False),
+    )
+
+    tokenizer.save_pretrained(output_path)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--input_dir",
+        required=True,
+        help="Location of OLMo2 weights, which contains config.yaml and model.pt.",
+    )
+    parser.add_argument(
+        "--no_tokenizer",
+        action="store_false",
+        dest="include_tokenizer",
+        help="If set, do not convert OLMo tokenizer to HF tokenizer.",
+    )
+    parser.add_argument(
+        "--tokenizer_json_path",
+        type=Path,
+        default=None,
+        help="Location of OLMo2 tokenizer json file. Defaults to what is set in the config file.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        required=True,
+        help="Location to write HF model and tokenizer",
+    )
+    parser.add_argument(
+        "--no_fix_eos_token_id",
+        action="store_false",
+        dest="fix_eos_token_id",
+        help="If set, does not change eos token id from 0 to 50279 if it is 0. Changing 0 to 50279 is a bug fix, so use this option with care.",
+    )
+    parser.add_argument(
+        "--no_tmp_cleanup",
+        action="store_false",
+        dest="tmp_cleanup",
+        help="If passed, don't remove temp dir at end of HF conversion.",
+    )
+    parser.add_argument(
+        "--no_safe_serialization",
+        action="store_false",
+        dest="safe_serialization",
+        help="Whether or not to save using `safetensors`.",
+    )
+    args = parser.parse_args()
+    write_model(
+        model_path=args.output_dir,
+        input_base_path=args.input_dir,
+        safe_serialization=args.safe_serialization,
+        include_tokenizer=args.include_tokenizer,
+        tokenizer_path=args.tokenizer_json_path,
+        fix_eos_token_id=args.fix_eos_token_id,
+        tmp_cleanup=args.tmp_cleanup,
+    )
+
+
+if __name__ == "__main__":
+    main()