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train_dolly.py
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train_dolly.py
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# Databricks notebook source
# MAGIC %md
# MAGIC ## Train Dolly
# MAGIC
# MAGIC This fine-tunes EleutherAI Pythia models
# MAGIC (e.g. [pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b),
# MAGIC [pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b), or
# MAGIC [pythia-12b](https://huggingface.co/EleutherAI/pythia-12b)) on
# MAGIC the [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) dataset.
# MAGIC
# MAGIC ```
# MAGIC Licensed under the Apache License, Version 2.0 (the "License");
# MAGIC you may not use this file except in compliance with the License.
# MAGIC You may obtain a copy of the License at
# MAGIC
# MAGIC http://www.apache.org/licenses/LICENSE-2.0
# MAGIC
# MAGIC Unless required by applicable law or agreed to in writing, software
# MAGIC distributed under the License is distributed on an "AS IS" BASIS,
# MAGIC WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# MAGIC See the License for the specific language governing permissions and
# MAGIC limitations under the License.
# MAGIC ```
# MAGIC
# MAGIC The EleutherAI Pythia models are [Apache 2.0 licensed](https://huggingface.co/EleutherAI/gpt-j-6B) and
# MAGIC the [databricks-dolly-15k](https://github.com/databrickslabs/dolly/tree/master/data) is licensed under the terms
# MAGIC of [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode),
# MAGIC which means it can be used for either academic or commercial purposes.
# COMMAND ----------
# MAGIC %md
# MAGIC Install these additional NVIDIA libraries for Databricks Runtime 13.x+ ML:
# COMMAND ----------
# MAGIC !wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/libcusparse-dev-11-7_11.7.3.50-1_amd64.deb -O /tmp/libcusparse-dev-11-7_11.7.3.50-1_amd64.deb && \
# MAGIC wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/libcublas-dev-11-7_11.10.1.25-1_amd64.deb -O /tmp/libcublas-dev-11-7_11.10.1.25-1_amd64.deb && \
# MAGIC wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/libcusolver-dev-11-7_11.4.0.1-1_amd64.deb -O /tmp/libcusolver-dev-11-7_11.4.0.1-1_amd64.deb && \
# MAGIC wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/libcurand-dev-11-7_10.2.10.91-1_amd64.deb -O /tmp/libcurand-dev-11-7_10.2.10.91-1_amd64.deb && \
# MAGIC dpkg -i /tmp/libcusparse-dev-11-7_11.7.3.50-1_amd64.deb && \
# MAGIC dpkg -i /tmp/libcublas-dev-11-7_11.10.1.25-1_amd64.deb && \
# MAGIC dpkg -i /tmp/libcusolver-dev-11-7_11.4.0.1-1_amd64.deb && \
# MAGIC dpkg -i /tmp/libcurand-dev-11-7_10.2.10.91-1_amd64.deb
# COMMAND ----------
# MAGIC %pip install -r requirements.txt
# COMMAND ----------
# MAGIC %load_ext autoreload
# MAGIC %autoreload 2
# COMMAND ----------
import logging
logging.basicConfig(
format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S"
)
logging.getLogger("py4j").setLevel(logging.WARNING)
logging.getLogger("sh.command").setLevel(logging.ERROR)
# COMMAND ----------
import os
import re
from datetime import datetime
from training.consts import DEFAULT_INPUT_MODEL, SUGGESTED_INPUT_MODELS
from training.trainer import load_training_dataset, load_tokenizer
dbutils.widgets.combobox("input_model", DEFAULT_INPUT_MODEL, SUGGESTED_INPUT_MODELS, "input_model")
dbutils.widgets.text("num_gpus", "", "num_gpus")
dbutils.widgets.text("local_training_root", "", "local_training_root")
dbutils.widgets.text("dbfs_output_root", "", "dbfs_output_root")
dbutils.widgets.text("experiment_id", "", "experiment_id")
dbutils.widgets.combobox("gpu_family", "a100", ["v100", "a10", "a100"])
# COMMAND ----------
timestamp = datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
model_name = "dolly"
experiment_id = dbutils.widgets.get("experiment_id")
input_model = dbutils.widgets.get("input_model")
if experiment_id:
experiment_id = re.sub(r"\s+", "_", experiment_id.strip())
model_name = f"{model_name}__{experiment_id}"
checkpoint_dir_name = f"{model_name}__{timestamp}"
dolly_training_dir_name = "dolly_training"
# Use the local training root path if it was provided. Otherwise try to find a sensible default.
local_training_root = dbutils.widgets.get("local_training_root")
if not local_training_root:
# Use preferred path when working in a Databricks cluster if it exists.
if os.path.exists("/local_disk0"):
local_training_root = os.path.join("/local_disk0", dolly_training_dir_name)
# Otherwise use the home directory.
else:
local_training_root = os.path.join(os.path.expanduser('~'), dolly_training_dir_name)
dbfs_output_root = dbutils.widgets.get("dbfs_output_root")
if not dbfs_output_root:
dbfs_output_root = f"/dbfs/{dolly_training_dir_name}"
os.makedirs(local_training_root, exist_ok=True)
os.makedirs(dbfs_output_root, exist_ok=True)
local_output_dir = os.path.join(local_training_root, checkpoint_dir_name)
dbfs_output_dir = os.path.join(dbfs_output_root, checkpoint_dir_name)
tensorboard_display_dir = f"{local_output_dir}/runs"
print(f"Local Output Dir: {local_output_dir}")
print(f"DBFS Output Dir: {dbfs_output_dir}")
print(f"Tensorboard Display Dir: {tensorboard_display_dir}")
# pick an appropriate config file
gpu_family = dbutils.widgets.get("gpu_family")
config_file_name = f"{gpu_family}_config.json"
deepspeed_config = os.path.join(os.getcwd(), "config", config_file_name)
print(f"Deepspeed config file: {deepspeed_config}")
# configure the batch_size
batch_size = 3
if gpu_family == "a10":
batch_size = 4
elif gpu_family == "a100":
batch_size = 6
# configure num_gpus, if specified
num_gpus_flag = ""
num_gpus = dbutils.widgets.get("num_gpus")
if num_gpus:
num_gpus = int(num_gpus)
num_gpus_flag = f"--num_gpus={num_gpus}"
if gpu_family == "v100":
bf16_flag = "--bf16 false"
else:
bf16_flag = "--bf16 true"
os.environ["TOKENIZERS_PARALLELISM"] = "false"
# COMMAND ----------
# MAGIC %load_ext tensorboard
# MAGIC %tensorboard --logdir '{tensorboard_display_dir}'
# COMMAND ----------
!deepspeed {num_gpus_flag} \
--module training.trainer \
--input-model {input_model} \
--deepspeed {deepspeed_config} \
--epochs 2 \
--local-output-dir {local_output_dir} \
--dbfs-output-dir {dbfs_output_dir} \
--per-device-train-batch-size {batch_size} \
--per-device-eval-batch-size {batch_size} \
--logging-steps 10 \
--save-steps 200 \
--save-total-limit 20 \
--eval-steps 50 \
--warmup-steps 50 \
--test-size 200 \
--lr 5e-6 \
{bf16_flag}
# COMMAND ----------
from training.generate import generate_response, load_model_tokenizer_for_generate
model, tokenizer = load_model_tokenizer_for_generate(dbfs_output_dir)
# COMMAND ----------
# Examples from https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html
instructions = [
"Write a love letter to Edgar Allan Poe.",
"Write a tweet announcing Dolly, a large language model from Databricks.",
"I'm selling my Nikon D-750, write a short blurb for my ad.",
"Explain to me the difference between nuclear fission and fusion.",
"Give me a list of 5 science fiction books I should read next.",
]
# set some additional pipeline args
pipeline_kwargs = {'torch_dtype': "auto"}
if gpu_family == "v100":
pipeline_kwargs['torch_dtype'] = "float16"
elif gpu_family == "a10" or gpu_family == "a100":
pipeline_kwargs['torch_dtype'] = "bfloat16"
# Use the model to generate responses for each of the instructions above.
for instruction in instructions:
response = generate_response(instruction, model=model, tokenizer=tokenizer, **pipeline_kwargs)
if response:
print(f"Instruction: {instruction}\n\n{response}\n\n-----------\n")
# COMMAND ----------