Training in progress, step 100

accelerate_config.yaml

@@ -0,0 +1,17 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: MULTI_GPU
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+gpu_ids: all
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat-200k",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 6,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.1",
+  "use_cache": false,
+  "vocab_size": 32000
+}

config_200k.yaml

@@ -0,0 +1,43 @@
+# Model arguments
+model_name_or_path: sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat-200k
+torch_dtype: null
+
+# Data training arguments
+# For definitions, see: src/h4/training/config.py
+dataset_mixer:
+  HuggingFaceH4/ultrafeedback_binarized: 1.0
+dataset_splits:
+- train_prefs
+- test_prefs
+preprocessing_num_workers: 12
+
+# DPOTrainer arguments
+bf16: true
+beta: 0.01
+do_eval: true
+evaluation_strategy: steps
+eval_steps: 100
+gradient_accumulation_steps: 2
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: False
+learning_rate: 5.0e-7
+log_level: info
+logging_steps: 25
+lr_scheduler_type: cosine
+max_length: 1024
+max_prompt_length: 512
+num_train_epochs: 1
+optim: adamw_torch
+output_dir: ./
+per_device_train_batch_size: 8
+per_device_eval_batch_size: 8
+push_to_hub: true
+save_strategy: "steps"
+save_steps: 100
+save_total_limit: 1
+seed: 42
+warmup_ratio: 0.1
+report_to:
+- tensorboard
+- wandb

run_dpo.py

@@ -0,0 +1,237 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 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 logging
+import random
+import sys
+
+import torch
+import transformers
+from transformers import AutoModelForCausalLM, set_seed
+
+from alignment import (
+    DataArguments,
+    DPOConfig,
+    H4ArgumentParser,
+    ModelArguments,
+    apply_chat_template,
+    get_checkpoint,
+    get_datasets,
+    get_kbit_device_map,
+    get_peft_config,
+    get_quantization_config,
+    get_tokenizer,
+    is_adapter_model,
+)
+from peft import PeftConfig, PeftModel
+from trl import DPOTrainer
+
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    parser = H4ArgumentParser((ModelArguments, DataArguments, DPOConfig))
+    model_args, data_args, training_args = parser.parse()
+
+    #######
+    # Setup
+    #######
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    # Log on each process the small summary:
+    logger.info(f"Model parameters {model_args}")
+    logger.info(f"Data parameters {data_args}")
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Check for last checkpoint
+    last_checkpoint = get_checkpoint(training_args)
+    if last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+        logger.info(f"Checkpoint detected, resuming training at {last_checkpoint=}.")
+
+    # Set seed for reproducibility
+    set_seed(training_args.seed)
+
+    ###############
+    # Load datasets
+    ###############
+    raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits)
+    logger.info(
+        f"Training on the following splits: {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}"
+    )
+    column_names = list(raw_datasets["train"].features)
+
+    #####################################
+    # Load tokenizer and process datasets
+    #####################################
+    data_args.truncation_side = "left"  # Truncate from left to ensure we don't lose labels in final turn
+    tokenizer = get_tokenizer(model_args, data_args)
+
+    #####################
+    # Apply chat template
+    #####################
+    raw_datasets = raw_datasets.map(
+        apply_chat_template,
+        fn_kwargs={"tokenizer": tokenizer, "task": "dpo"},
+        num_proc=data_args.preprocessing_num_workers,
+        remove_columns=column_names,
+        desc="Formatting comparisons with prompt template",
+    )
+
+    # Replace column names with what TRL needs, text_chosen -> chosen and text_rejected -> rejected
+    for split in ["train", "test"]:
+        raw_datasets[split] = raw_datasets[split].rename_columns(
+            {"text_prompt": "prompt", "text_chosen": "chosen", "text_rejected": "rejected"}
+        )
+
+    # Log a few random samples from the training set:
+    for index in random.sample(range(len(raw_datasets["train"])), 3):
+        logger.info(f"Prompt sample {index} of the raw training set:\n\n{raw_datasets['train'][index]['prompt']}")
+        logger.info(f"Chosen sample {index} of the raw training set:\n\n{raw_datasets['train'][index]['chosen']}")
+        logger.info(f"Rejected sample {index} of the raw training set:\n\n{raw_datasets['train'][index]['rejected']}")
+
+    torch_dtype = (
+        model_args.torch_dtype if model_args.torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype)
+    )
+    quantization_config = get_quantization_config(model_args)
+
+    model_kwargs = dict(
+        revision=model_args.model_revision,
+        trust_remote_code=model_args.trust_remote_code,
+        use_flash_attention_2=model_args.use_flash_attention_2,
+        torch_dtype=torch_dtype,
+        use_cache=False if training_args.gradient_checkpointing else True,
+        device_map=get_kbit_device_map() if quantization_config is not None else None,
+        quantization_config=quantization_config,
+    )
+
+    model = model_args.model_name_or_path
+    if is_adapter_model(model, model_args.model_revision) is True:
+        # Load the base model, merge the adapter weights and unload the adapter
+        # Note: to run QLoRA, you will need to merge the base model separately as the merged model in 16bit
+        logger.info(f"Merging PEFT adapters for {model_args.model_name_or_path=}")
+
+        peft_config = PeftConfig.from_pretrained(model_args.model_name_or_path, revision=model_args.model_revision)
+
+        model_kwargs = dict(
+            revision=model_args.base_model_revision,
+            trust_remote_code=model_args.trust_remote_code,
+            use_flash_attention_2=model_args.use_flash_attention_2,
+            torch_dtype=torch_dtype,
+            use_cache=False if training_args.gradient_checkpointing else True,
+        )
+        base_model = AutoModelForCausalLM.from_pretrained(
+            peft_config.base_model_name_or_path,
+            **model_kwargs,
+        )
+        model = PeftModel.from_pretrained(
+            base_model, model_args.model_name_or_path, revision=model_args.model_revision
+        )
+        model.eval()
+        model = model.merge_and_unload()
+        model_kwargs = None
+
+    ref_model = model
+    ref_model_kwargs = model_kwargs
+
+    if model_args.use_peft is True:
+        ref_model = None
+        ref_model_kwargs = None
+
+    #########################
+    # Instantiate DPO trainer
+    #########################
+    trainer = DPOTrainer(
+        model,
+        ref_model,
+        model_init_kwargs=model_kwargs,
+        ref_model_init_kwargs=ref_model_kwargs,
+        args=training_args,
+        beta=training_args.beta,
+        train_dataset=raw_datasets["train"],
+        eval_dataset=raw_datasets["test"],
+        tokenizer=tokenizer,
+        max_length=training_args.max_length,
+        max_prompt_length=training_args.max_prompt_length,
+        peft_config=get_peft_config(model_args),
+        loss_type=training_args.loss_type,
+    )
+
+    ###############
+    # Training loop
+    ###############
+    checkpoint = None
+    if training_args.resume_from_checkpoint is not None:
+        checkpoint = training_args.resume_from_checkpoint
+    elif last_checkpoint is not None:
+        checkpoint = last_checkpoint
+    train_result = trainer.train(resume_from_checkpoint=checkpoint)
+    metrics = train_result.metrics
+    metrics["train_samples"] = len(raw_datasets["train"])
+    trainer.log_metrics("train", metrics)
+    trainer.save_metrics("train", metrics)
+    trainer.save_state()
+
+    logger.info("*** Training complete ***")
+
+    ##########
+    # Evaluate
+    ##########
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate()
+        metrics["eval_samples"] = len(raw_datasets["test"])
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    ##################################
+    # Save model and create model card
+    ##################################
+    logger.info("*** Save model ***")
+    trainer.save_model(training_args.output_dir)
+    logger.info(f"Model saved to {training_args.output_dir}")
+
+    # Save everything else on main process
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "dataset": list(data_args.dataset_mixer.keys()),
+        "dataset_tags": list(data_args.dataset_mixer.keys()),
+        "tags": ["alignment-handbook"],
+    }
+    if trainer.accelerator.is_main_process:
+        trainer.create_model_card(**kwargs)
+        # Restore k,v cache for fast inference
+        trainer.model.config.use_cache = True
+        trainer.model.config.save_pretrained(training_args.output_dir)
+
+    if training_args.push_to_hub is True:
+        logger.info("Pushing to hub...")
+        trainer.push_to_hub(**kwargs)
+
+    logger.info("*** Training complete! ***")
+
+
+if __name__ == "__main__":
+    main()
+

runs/Apr26_16-38-17_ip-26-0-161-178/events.out.tfevents.1714149975.ip-26-0-161-178.306341.0

@@ -0,0 +1,3 @@
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+oid sha256:1965c5ca22ae4f3f8b62fc21e894bc3ef67848a9d40a53379b8db44b2fdb9d04
+size 8887

slurm_job.slurm

@@ -0,0 +1,75 @@
+#!/bin/bash
+#SBATCH --job-name=distil-zephyr
+#SBATCH --nodes=1
+# set 24h for job wall time limit
+#SBATCH --time=4:00:00
+#SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
+#SBATCH --cpus-per-task=32
+#SBATCH --gres=gpu:8
+#SBATCH --exclusive
+#SBATCH --partition=hopper-prod
+#SBATCH --output=/fsx/sanchit/alignment-logs/%x-%j.out
+
+set -x -e
+
+# START EDIT
+source ~/.bashrc
+source /fsx/sanchit/miniconda3/bin/activate alignment
+
+LOG_PATH="/fsx/sanchit/alignment-logs/main_log.txt"
+SAVE_DIR="/fsx/sanchit"
+# END EDIT
+
+echo "START TIME: $(date)"
+
+GPUS_PER_NODE=8
+NNODES=$SLURM_NNODES
+
+# so processes know who to talk to
+MASTER_ADDR=`scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1`
+
+# From https://i.hsfzxjy.site/2021-03-10-obtain-a-random-unused-tcp-port-with-bash/
+function unused_port() {
+    N=${1:-1}
+    comm -23 \
+        <(seq "1025" "65535" | sort) \
+        <(ss -Htan |
+            awk '{print $4}' |
+            cut -d':' -f2 |
+            sort -u) |
+        shuf |
+        head -n "$N"
+}
+MASTER_PORT=$(unused_port)
+
+# export TORCH_CPP_LOG_LEVEL=INFO
+# export TORCH_DISTRIBUTED_DEBUG=DETAIL
+
+export LAUNCHER="python -u -m accelerate.commands.launch --config_file ./accelerate_config.yaml"
+
+export PROGRAM="./run_dpo.py ./config_200k.yaml"
+export CMD="$LAUNCHER $PROGRAM"
+echo $CMD
+
+SRUN_ARGS=" \
+    --wait=60 \
+    --kill-on-bad-exit=1 \
+    "
+
+# py-spy top -s -i -n -- $LAUNCHER --node_rank $SLURM_PROCID --role $SLURMD_NODENAME: $CMD
+clear; srun $SRUN_ARGS --jobid $SLURM_JOB_ID bash -c "$CMD" 2>&1 | tee -a $SAVE_DIR/logs/main_log.txt
+
+
+# srun error handling:
+# --wait=60: wait 60 sec after the first task terminates before terminating all remaining tasks
+# --kill-on-bad-exit=1: terminate a step if any task exits with a non-zero exit code
+
+# SRUN_ARGS=" \
+#     --wait=60 \
+#     --kill-on-bad-exit=1 \
+#     "
+#
+# # py-spy top -s -i -n -- $LAUNCHER --node_rank $SLURM_PROCID --role $SLURMD_NODENAME: $CMD
+# clear; srun $SRUN_ARGS --jobid $SLURM_JOBID bash -c "$CMD" 2>&1 | tee -a $SAVE_DIR/logs/main_log.txt
+
+echo "END TIME: $(date)"

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

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+size 493443

tokenizer_config.json

@@ -0,0 +1,43 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 2048,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

training_args.bin

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+size 5112

wandb/debug-internal.log

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wandb/run-20240426_164617-71zld9et/files/conda-environment.yaml

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+ 21%|██        | 99/478 [02:06<08:01,  1.27s/it]
+ 21%|██        | 100/478 [02:08<08:02,  1.28s/it][INFO|trainer.py:3614] 2024-04-26 16:48:30,367 >> ***** Running Evaluation *****
+[INFO|trainer.py:3616] 2024-04-26 16:48:30,367 >>   Num examples = 2000
+[INFO|trainer.py:3619] 2024-04-26 16:48:30,367 >>   Batch size = 8
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+
+
+ 88%|████████▊ | 28/32 [00:06<00:01,  3.58it/s]
+[INFO|configuration_utils.py:471] 2024-04-26 16:48:38,653 >> Configuration saved in ./checkpoint-100/config.json
+[INFO|configuration_utils.py:697] 2024-04-26 16:48:38,655 >> Configuration saved in ./checkpoint-100/generation_config.json
+[INFO|modeling_utils.py:2598] 2024-04-26 16:48:46,786 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 2 checkpoint shards. You can find where each parameters has been saved in the index located at ./checkpoint-100/model.safetensors.index.json.
+[INFO|tokenization_utils_base.py:2488] 2024-04-26 16:48:46,789 >> tokenizer config file saved in ./checkpoint-100/tokenizer_config.json

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