NeMo Framework SFT with Mistral-7B

User Guide (Latest Version)

Learning Goals

Often we want to adapt or customize foundation models to be more performant on our specific task. Fine-tuning refers to how we can modify the weights of a pre-trained foundation model with additional custom data. Supervised fine-tuning (SFT) refers to unfreezing all the weights and layers in our model and training on a newly labeled set of examples. We can fine-tune to incorporate new, domain-specific knowledge or teach the foundation model what type of response to provide. One specific type of SFT is also referred to as “instruction tuning” where we use SFT to teach a model to follow instructions better.

In this project, you’ll test out the supervised fine-tuning method on the Mistral-7B model using an instructive dataset.

NeMo Tools and Resources

Software Requirements

  • Use the latest NeMo Framework Training container

  • This playbook has been tested on: nvcr.io/nvidia/nemo:24.05. It is expected to work similarly on other environments.

Hardware Requirements

  • Minimum 8xA100 80G (1 node) for SFT on Mistral-7B

Data

Databricks-dolly-15k is an open-source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization. For more details about the data refer to databricks-dolly-15k | Hugging Face.

The following steps have been tested with this container: nvcr.io/nvidia/nemo:24.05.

If you already have a .nemo file for the Mistral-7B model, you can skip this step.

Step 1: Download Mistral-7B from Huggingface-hub

Request download permission and create the destination directory. Two options are available.

To download using the CLI tool:

Copy
Copied!
            

mkdir mistral-7B-hf huggingface-cli download mistralai/Mistral-7B-v0.1 --local-dir mistral-7B-hf

To download using the Hugging Face API, run the following Python code:

Copy
Copied!
            

from huggingface_hub import snapshot_download snapshot_download(repo_id="mistralai/Mistral-7B-v0.1", local_dir="mistral-7B-hf", local_dir_use_symlinks=False)

In this example, the Mistral-7B Hugging Face model will be downloaded to ./mistral-7B-hf.

Step 2: Convert to .nemo

Run the container using the following command:

Copy
Copied!
            

docker run --gpus device=1 --shm-size=2g --net=host --ulimit memlock=-1 --rm -it -v ${PWD}:/workspace -w /workspace -v ${PWD}/results:/results nvcr.io/nvidia/nemo:24.05 bash

Convert the Hugging Face model to .nemo model:

Copy
Copied!
            

python3 /opt/NeMo/scripts/checkpoint_converters/convert_mistral_7b_hf_to_nemo.py --input_name_or_path=./mistral-7B-hf/ --output_path=mistral.nemo

The generated mistral.nemo file uses distributed checkpointing and can be loaded with any tensor parallel (tp) or pipeline parallel (pp) combination without modifying (e.g. reshaping/splitting) the mistral.nemo checkpoint.

Step 1: Download the dataset

Download the dolly-15k dataset from Hugging Face:

Copy
Copied!
            

git clone https://huggingface.co/datasets/databricks/databricks-dolly-15k; wget https://huggingface.co/datasets/databricks/databricks-dolly-15k/resolve/main/databricks-dolly-15k.jsonl -O databricks-dolly-15k/databricks-dolly-15k.jsonl

Once downloaded, verify the size of the file (databricks-dolly-15k.jsonl):

Copy
Copied!
            

$ du -sh databricks-dolly-15k/databricks-dolly-15k.jsonl 13M databricks-dolly-15k/databricks-dolly-15k.jsonl

In addition, you can verify the integrity of the file using checksum:

Copy
Copied!
            

$ sha256sum databricks-dolly-15k/databricks-dolly-15k.jsonl 2df9083338b4abd6bceb5635764dab5d833b393b55759dffb0959b6fcbf794ec databricks-dolly-15k/databricks-dolly-15k.jsonl

If the sizes or checksum do not match, please inspect the log to confirm all commands run successfully.

Step 2: Data preprocessing

You’ll need to pre-process the data to ensure it’s in the correct format. The expected format is a JSONL file with {‘input’: ‘xxx’, ‘output’: ‘yyy’} pairs. To run the pre-processing you will use the script that has already been prepared for you. Run this script and passing your jsonl file as –input.

To run the script, you need to launch the container. If the container is not already running, use the following command:

Copy
Copied!
            

docker run --gpus device=1 --shm-size=2g --net=host --ulimit memlock=-1 --rm -it -v ${PWD}:/workspace -w /workspace -v ${PWD}/results:/results nvcr.io/nvidia/nemo:24.05 bash

Next, run the following data preprocess script:

Copy
Copied!
            

python3 /opt/NeMo-Framework-Launcher/launcher_scripts/nemo_launcher/collections/dataprep_scripts/dolly_dataprep/preprocess.py --input databricks-dolly-15k/databricks-dolly-15k.jsonl

The following shows a example output:

Copy
Copied!
            

Preprocessing data to jsonl format... Data was successfully preprocessed and saved by databricks-dolly-15k/databricks-dolly-15k-output.jsonl.

Check that the output jsonl files exist:

Copy
Copied!
            

$ ls databricks-dolly-15k/ .git/ .gitattributes README.md databricks-dolly-15k-output.jsonl databricks-dolly-15k.jsonl

Check the first example in the output jsonl file:

Copy
Copied!
            

$ head -n 1 databricks-dolly-15k/databricks-dolly-15k-output.jsonl {"input": "Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia's domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.\n\nWhen did Virgin Australia start operating?", "output": "Virgin Australia commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route.", "category": "closed_qa"}

Step 3: Split the data into train, validation, and test

Generate the train, test and validation splits. You can use your own script or create a new script. If you create a new script, use the following sample split_train_val.py by copying it over in the databricks-dolly-15k directory:

Copy
Copied!
            

import json import random input_file = "databricks-dolly-15k-output.jsonl" training_output_file = "training.jsonl" validation_output_file = "validation.jsonl" test_output_file = "test.jsonl" # Specify the proportion of data for training and validation train_proportion = 0.80 validation_proportion = 0.15 test_proportion = 0.05 # Read the JSONL file and shuffle the JSON objects with open(input_file, "r") as f: lines = f.readlines() random.shuffle(lines) # Calculate split indices total_lines = len(lines) train_index = int(total_lines * train_proportion) val_index = int(total_lines * validation_proportion) # Distribute JSON objects into training and validation sets train_data = lines[:train_index] validation_data = lines[train_index:train_index+val_index] test_data = lines[train_index+val_index:] # Write JSON objects to training file with open(training_output_file, "w") as f: for line in train_data: f.write(line.strip() + "\n") # Write JSON objects to validation file with open(validation_output_file, "w") as f: for line in validation_data: f.write(line.strip() + "\n") # Write JSON objects to training file with open(test_output_file, "w") as f: for line in test_data: f.write(line.strip() + "\n")

Then, go to the databricks-dolly-15k directory and generate the splits:

Copy
Copied!
            

python3 split_train_val.py

Check for the train, test, and validation jsonl files:

Copy
Copied!
            

$ ls README.md databricks-dolly-15k.jsonl databricks-dolly-15k-output.jsonl split_train_val.py training.jsonl validation.jsonl test.jsonl

Step 4. Run the SFT fine-tuning script

Set the environment variables and then pass the paths to your train, test, and validation data files:

Copy
Copied!
            

MODEL="YOUR PATH TO mistral.nemo" TRAIN_DS="[YOUR PATH TO databricks-dolly-15k/train.jsonl]" VALID_DS="[YOUR PATH TO databricks-dolly-15k/validation.jsonl]" TEST_DS="[YOUR PATH TO databricks-dolly-15k/test.jsonl]" VALID_NAMES="[databricks-dolly-15k]"

Set the concat sampling probability. The value depends on the number of files passed in the train set and the percentage of the fine-tuning data you want to use from each file.

The sum of the concat sampling probabilities should be 1.0.

The following example shows how to set the concat sampling probability for a train set with two jsonl files:

Copy
Copied!
            

TRAIN_DS="[/path/to/dataset_1.jsonl,/path/to/dataset_2.jsonl]" CONCAT_SAMPLING_PROBS="[0.3,0.7]"

In our example, we are using one train file, so CONCAT_SAMPLING_PROBS="[1.0]".

Set the TP and PP values based on the model you are using.

Copy
Copied!
            

CONCAT_SAMPLING_PROBS="[1]" TP_SIZE=1 PP_SIZE=32

Run the SFT command by appropriately setting the values for the parameters such as the number of steps, model checkpoint path, batch sizes, etc. For a full reference of parameter settings, refer to the config file.

Copy
Copied!
            

torchrun --nproc_per_node=8 \ /opt/NeMo/examples/nlp/language_modeling/tuning/megatron_gpt_finetuning.py \ trainer.precision=bf16 \ trainer.devices=8 \ trainer.num_nodes=1 \ trainer.val_check_interval=0.1 \ trainer.max_steps=50 \ model.restore_from_path=${MODEL} \ model.micro_batch_size=1 \ model.global_batch_size=128 \ model.tensor_model_parallel_size=${TP_SIZE} \ model.pipeline_model_parallel_size=${PP_SIZE} \ model.megatron_amp_O2=True \ model.sequence_parallel=True \ model.activations_checkpoint_granularity=selective \ model.activations_checkpoint_method=uniform \ model.optim.name=distributed_fused_adam \ model.optim.lr=1e-6 \ model.answer_only_loss=True \ model.peft.peft_scheme=none \ model.data.train_ds.file_names=${TRAIN_DS} \ model.data.validation_ds.file_names=${VALID_DS} \ model.data.test_ds.file_names=${TEST_DS} \ model.data.train_ds.concat_sampling_probabilities=${CONCAT_SAMPLING_PROBS} \ model.data.train_ds.max_seq_length=2048 \ model.data.validation_ds.max_seq_length=2048 \ model.data.train_ds.micro_batch_size=1 \ model.data.train_ds.global_batch_size=128 \ model.data.validation_ds.micro_batch_size=1 \ model.data.validation_ds.global_batch_size=128 \ model.data.test_ds.micro_batch_size=1 \ model.data.test_ds.global_batch_size=256 \ model.data.train_ds.num_workers=0 \ model.data.validation_ds.num_workers=0 \ model.data.test_ds.num_workers=0 \ model.data.validation_ds.metric.name=loss \ model.data.test_ds.metric.name=loss \ exp_manager.create_wandb_logger=False \ exp_manager.explicit_log_dir=/results \ exp_manager.resume_if_exists=True \ exp_manager.resume_ignore_no_checkpoint=True \ exp_manager.create_checkpoint_callback=True \ exp_manager.checkpoint_callback_params.monitor=validation_loss \ exp_manager.checkpoint_callback_params.save_best_model=False \ exp_manager.checkpoint_callback_params.save_nemo_on_train_end=True \ exp_manager.checkpoint_callback_params.mode=min \ ++cluster_type=BCP

Note: To run SFT on multiple nodes on a Slurm cluster, replace the torchrun --nproc_per_node=8 with python.

After completion of SFT, you should get an output similar to the following. If you want to get the wandb output, make sure to set exp_manager.create_wandb_logger=True and sign up for W&B to get the API key. You can follow the steps on the terminal to accomplish this task.

Copy
Copied!
            

wandb: Waiting for W&B process to finish... (success). wandb: wandb: Run history: wandb: consumed_samples ▁▃▅▆█ wandb: epoch ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁ wandb: global_step ▁▃▅▆█ wandb: grad_norm █▃▄▃▁ wandb: lr ▁▁▁▁▁ wandb: reduced_train_loss █▅▇▆▁ wandb: train_backward_timing ▇█▅▁▇ wandb: train_step_timing ▃▁█▆▂ wandb: trainer/global_step ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███ wandb: val_loss █▅▄▃▂▂▂▁▁▁ wandb: validation_loss █▅▄▃▂▂▂▁▁▁ wandb: validation_loss_databricks-dolly-15k █▅▄▃▂▂▂▁▁▁ wandb: validation_step_timing ▂██▂▂▆▃█▂██▂▂▆▇█▂██▂█▆▇█▂█▁▃█▆▇█▂▂▁▃█▆▇▇ wandb: wandb: Run summary: wandb: consumed_samples 6272.0 wandb: epoch 0 wandb: global_step 49.0 wandb: grad_norm 10.05424 wandb: lr 0.0 wandb: reduced_train_loss 1.66673 wandb: train_backward_timing 5e-05 wandb: train_step_timing 17.50282 wandb: trainer/global_step 49 wandb: val_loss 1.65022 wandb: validation_loss 1.65022 wandb: validation_loss_databricks-dolly-15k 1.65022 wandb: validation_step_timing 9.01902 wandb: wandb: You can sync this run to the cloud by running: wandb: wandb sync /results/wandb/offline-run-20230714_032640-iu65oacs wandb: Find logs at: /results/wandb/offline-run-20230714_032640-iu65oacs/logs

Step 5: Run evaluation

Run evaluation using megatron_gpt_generate.py.

Set the appropriate model checkpoint path, test file path, batch sizes, number of tokens, etc. Then, run evaluation on the test file:

Copy
Copied!
            

PATH_TO_TRAINED_MODEL=/results/checkpoints/megatron_gpt_sft.nemo TEST_DS="[YOUR PATH TO test.jsonl]" python /opt/NeMo/examples/nlp/language_modeling/tuning/megatron_gpt_generate.py \ model.restore_from_path=${PATH_TO_TRAINED_MODEL} \ trainer.devices=8 \ model.data.test_ds.file_names=${TEST_DS} \ model.data.test_ds.names=['dolly-15k_test'] \ model.data.test_ds.global_batch_size=16 \ model.data.test_ds.micro_batch_size=2 \ model.data.test_ds.tokens_to_generate=20 \ model.tensor_model_parallel_size=1 \ model.pipeline_model_parallel_size=1 \ inference.greedy=True \ model.data.test_ds.output_file_path_prefix=/results/sft_results \ model.data.test_ds.write_predictions_to_file=True

The following shows a sample output:

Copy
Copied!
            

$ tail -n 4 sft_results.jsonl {"sentence": "What is Azure HDInsight? Azure HDInsight is a cloud service that provides a high-performance, scalable, and cost-effective way to run Apache Hadoop on the"} {"sentence": "What is carnitine? Carnitine is a fatty acid that is found in the body. It is used to produce energy in the mitochondria of the cells. Carnit"} {"sentence": "List some TV shows that Canadian actor William B. Davis has been in."} {"sentence": "Identify which instrument is string or percussion: Handbell, Dobro, Drum"}

This is only a sample output based of a toy SFT example and your output may vary. The performance can be further improved by fine-tuning the model for more steps.

Previous NeMo Framework PEFT with Llama2, Mixtral-8x7B and Nemotron 4 340B
Next NeMo Framework PEFT with Mistral-7B
© | | | | | | |. Last updated on Jun 24, 2024.