Training in progress, step 1000

cmd.txt

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+/var/spool/slurmd/job117568/slurm_script 05-06_03-05

commit.txt

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+commit 001432feaa4bcad3e709036376d5dd95b14abfc0
+Author: Shteyman <[email protected]>
+Date:   Wed Jun 5 02:59:17 2024 -0700
+
+     unnecessary file delete

config.json

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+{
+  "_name_or_path": "JackFram/llama-68m",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 2,
+  "num_key_value_heads": 12,
+  "pad_token_id": 1,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

events.out.tfevents.1717581940.isl-gpu3.8841.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa74e7b5cfe6aa38454b4aec704fd8c38a85af75b76f4b1763b5255bc48cb0ee
+size 5694

experiment_code/config/config1.yaml

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+config_name: "JackFram/llama-68m"
+tokenizer_name: "JackFram/llama-68m"
+validation_split_percentage: 2
+train_file: "/home/dshteyma/shareGPT_data/ShareGPT_V3_unfiltered_cleaned_split.json"
+dataset_name_hub: "anon8231489123/ShareGPT_Vicuna_unfiltered/ShareGPT_V3_unfiltered_cleaned_split.json"
+dataset_name_local: "ShareGPT"
+# max_train_samples: 1000
+# max_eval_samples: 10
+do_train: True
+do_eval: True
+output_dir: "/home/dshteyma/target_draft_coupling_code/target_draft_training/training_outputs"
+overwrite_output_dir: True
+per_device_train_batch_size: 4
+gradient_accumulation_steps: 1
+report_to: "tensorboard"
+logging_dir: "/home/dshteyma/target_draft_coupling_code/target_draft_training/training_outputs"
+logging_steps: 500
+save_steps: 1000
+eval_strategy: "steps"
+eval_steps: 1000
+learning_rate: 0.0001
+gradient_accumulation_steps: 1
+weight_decay: 0.01
+warmup_ratio: 0.05
+push_to_hub: True
+hub_model_id: "DorinSht/ShareGPT_llama2_68M"
+hub_strategy: "all_checkpoints"
+

experiment_code/config/config_redpajama.yaml

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+config_name: "JackFram/llama-68m"
+tokenizer_name: "JackFram/llama-68m"
+validation_split_percentage: 2
+train_file: "/home/dshteyma/target_draft_coupling_code/dataset_dict.json"
+dataset_name_local: "RedPajama"
+dataset_name: "togethercomputer/RedPajama-Data-1T-Sample"
+dataset_name_hub: "togethercomputer/RedPajama-Data-1T-Sample"
+# max_train_samples: 1000
+# max_eval_samples: 10
+do_train: True
+do_eval: True
+output_dir: "/home/dshteyma/target_draft_coupling_code/target_draft_training/training_outputs"
+overwrite_output_dir: True
+per_device_train_batch_size: 4
+gradient_accumulation_steps: 3
+report_to: "tensorboard"
+logging_dir: "/home/dshteyma/target_draft_coupling_code/target_draft_training/training_outputs"
+logging_steps: 10000
+save_steps: 10000
+eval_strategy: "steps"
+eval_steps: 10000
+learning_rate: 0.0001
+weight_decay: 0.01
+warmup_ratio: 0.05
+push_to_hub: False
+hub_model_id: "DorinSht/llama_68M_redpajama"
+hub_strategy: "all_checkpoints"

experiment_code/prepare_sharegpt.py

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+"""
+This script is largely copied from the Vicuna repo: https://github.com/lm-sys/FastChat/blob/main/fastchat/data/split_long_conversation.py
+We fixed a bug in `split_one_sample`, which previously includes long conversations in the processed data. Now we skip these long conversations.
+"""
+import argparse
+from concurrent.futures import ProcessPoolExecutor
+import json
+import transformers
+from tqdm import tqdm
+
+def shareGPT_pipeline(tokenizer, raw_datasets, overwrite_cache):
+    
+    def preprocess_conversation(convo):
+        key_mapping = {"role" : "from", "content" : "value"}
+        value_mapping = {"user" : "user", "human" : "user", "gpt" : "assistant", 'system': 'assitant', 'bing': 'assitant', 'chatgpt': 'assitant', 'bard': 'assitant'}
+        # mapping = {"human" : "user", "gpt" : "assitant"}
+        if value_mapping[convo[0][key_mapping['role']]] != 'user': 
+            convo = convo[1:]
+        preproc_convos_user = [{"role": 'user', "content": convo_elem[key_mapping['content']]} for i, convo_elem in enumerate(convo) if (i % 2 == 0 and value_mapping[convo_elem[key_mapping['role']]] == 'user')]
+        preproc_convos_assistant = [{"role": 'assistant', "content": convo_elem[key_mapping['content']]} for i, convo_elem in enumerate(convo) if (i % 2 == 1 and value_mapping[convo_elem[key_mapping['role']]] == 'assistant')]
+        if len(preproc_convos_user) != len(preproc_convos_assistant):
+            return []
+        preproc_convos = [conv_elem for pair in zip(preproc_convos_user, preproc_convos_assistant) for conv_elem in pair]
+        return preproc_convos
+
+    def filter_incorrect_conversations(examples):
+        convos = examples["conversations"]
+        ids_to_remove = [True if preprocess_conversation(convo) == [] else False for convo in convos]
+        return { "ids_to_remove" : ids_to_remove, }
+
+    def formatting_prompts_func(examples):
+        convos = examples["conversations"]
+        # preproc_convos = [convo for convo in convos if (convo[0]['from'] == 'human' or convo[0]['from'] == 'user')]
+        preproc_convos = [preprocess_conversation(convo) for convo in convos]
+        # preproc_convos2 = [preproc_convo for preproc_convo in preproc_convos if preproc_convo[0]['role'] == 'user']
+        texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for i, convo in enumerate(preproc_convos)]
+        return { "text" : texts,}
+
+    filtered_datasets = raw_datasets.filter(lambda example: example['conversations'] != [], load_from_cache_file=not overwrite_cache,)
+    dataset = filtered_datasets.map(filter_incorrect_conversations, batched = True, load_from_cache_file=not overwrite_cache,)
+    filtered_datasets2 = dataset.filter(lambda example: example['ids_to_remove'] == False, load_from_cache_file=not overwrite_cache,)
+    raw_datasets_preprocessed = filtered_datasets2.map(formatting_prompts_func, batched = True, load_from_cache_file=not overwrite_cache,)    
+    
+    return raw_datasets_preprocessed

experiment_code/requirements.txt

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+huggingface-hub==0.22.2
+-e git+https://github.com/huggingface/transformers.git@bbaa8ceff696c479aecdb4575b2deb1349efd3aa#egg=transformers

experiment_code/run_clm.py

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+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2020 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.
+"""
+Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
+
+Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
+https://huggingface.co/models?filter=text-generation
+"""
+# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
+import random
+import logging
+import math
+import os
+from datetime import datetime
+import sys
+import warnings
+from dataclasses import dataclass, field
+from itertools import chain
+from typing import Optional
+import datasets
+import evaluate
+import torch
+from datasets import load_dataset
+import argparse
+import transformers
+from prepare_sharegpt import shareGPT_pipeline
+from transformers import (
+    CONFIG_MAPPING,
+    MODEL_FOR_CAUSAL_LM_MAPPING,
+    AutoConfig,
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    default_data_collator,
+    set_seed,
+)
+from transformers.testing_utils import CaptureLogger
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+from functools import partial
+
+from omegaconf import DictConfig, OmegaConf
+import hydra 
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.41.0.dev0")
+
+require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
+
+logger = logging.getLogger(__name__)
+
+MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
+
+random.seed(42)
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
+    """
+
+    model_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
+            )
+        },
+    )
+    model_type: Optional[str] = field(
+        default=None,
+        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
+    )
+    padding_side: str = field(
+        default="right", metadata={"help": "The padding side in tokenizer"}
+    ) 
+    config_overrides: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Override some existing default config settings when a model is trained from scratch. Example: "
+                "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
+            )
+        },
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+    use_auth_token: bool = field(
+        default=None,
+        metadata={
+            "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
+        },
+    )
+    trust_remote_code: bool = field(
+        default=True,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
+    torch_dtype: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
+                "dtype will be automatically derived from the model's weights."
+            ),
+            "choices": ["auto", "bfloat16", "float16", "float32"],
+        },
+    )
+    low_cpu_mem_usage: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded. "
+                "set True will benefit LLM loading time and RAM consumption."
+            )
+        },
+    )
+
+    def __post_init__(self):
+        if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
+            raise ValueError(
+                "--config_overrides can't be used in combination with --config_name or --model_name_or_path"
+            )
+            
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_name_hub: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_name_local: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset for identification."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
+    validation_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                "value if set."
+            )
+        },
+    )
+    streaming: bool = field(default=False, metadata={"help": "Enable streaming mode"})
+    block_size: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Optional input sequence length after tokenization. "
+                "The training dataset will be truncated in block of this size for training. "
+                "Default to the model max input length for single sentence inputs (take into account special tokens)."
+            )
+        },
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    validation_split_percentage: Optional[int] = field(
+        default=5,
+        metadata={
+            "help": "The percentage of the train set used as validation set in case there's no validation split"
+        },
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    keep_linebreaks: bool = field(
+        default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."}
+    )
+    lazy_preprocess: bool = False
+
+    def __post_init__(self):
+        if self.streaming:
+            require_version("datasets>=2.0.0", "The streaming feature requires `datasets>=2.0.0`")
+
+        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
+            raise ValueError("Need either a dataset name or a training/validation file.")
+        else:
+            if self.train_file is not None:
+                extension = self.train_file.split(".")[-1]
+                assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
+            if self.validation_file is not None:
+                extension = self.validation_file.split(".")[-1]
+                assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."   
+
+# @dataclass
+# class TrainingArguments(transformers.TrainingArguments):
+#     cache_dir: Optional[str] = field(default=None)
+#     optim: str = field(default="adamw_torch")
+#     model_max_length: int = field(
+#         default=2048,
+#         metadata={
+#             "help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
+#         },
+#     )
+
+def create_output_directory(dir_root_path):
+    # Get the current date and time
+    current_time = datetime.now()
+    # Format the date and time as a string
+    # Example format: YYYYMMDD_HHMMSS
+    formatted_time = current_time.strftime("%Y%m%d_%H%M%S")
+    # Define the directory name with the formatted time
+    directory_full_path = os.path.join(dir_root_path, f"training_outputs_{formatted_time}")
+    # Create the directory
+    os.makedirs(directory_full_path)
+    print(f"Directory '{directory_full_path}' created successfully.")
+    return directory_full_path
+
+def main():
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = argparse.ArgumentParser(description="parser for arguments from .py script call")
+    parser.add_argument('--output_dir', type=str, help='Path for training_args.output_dir')
+    parser.add_argument('--logging_dir', type=str, help='Path for training_args.logging_dir')
+    parser.add_argument('--config_file', type=str, help='An additional required option.')
+    args = parser.parse_args()
+    
+    parser_hf = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+    if args.config_file is not None and args.output_dir is not None and args.output_dir is not None:
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser_hf.parse_yaml_file(args.config_file)
+        training_args.output_dir = args.output_dir
+        training_args.logging_dir = args.logging_dir
+    else:
+        # use the preset config file defined in the slurm .sh script
+        # model_args, data_args, training_args = parser_hf.parse_yaml_file(os.getenv("DEFAULT_CONFIG_FILE"))
+        model_args, data_args, training_args = parser_hf.parse_yaml_file('./config/config1.yaml')
+
+
+    if model_args.use_auth_token is not None:
+        warnings.warn(
+            "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
+            FutureWarning,
+        )
+        if model_args.token is not None:
+            raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
+        model_args.token = model_args.use_auth_token
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_clm", model_args, data_args)
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+    if training_args.should_log:
+        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
+        transformers.utils.logging.set_verbosity_info()
+
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(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.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
+    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
+    # (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
+    # 'text' is found. You can easily tweak this behavior (see below).
+    #
+    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
+    # download the dataset.
+    if data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+            streaming=data_args.streaming,
+        )
+        if "validation" not in raw_datasets.keys():
+            raw_datasets["validation"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[:{data_args.validation_split_percentage}%]",
+                cache_dir=model_args.cache_dir,
+                token=model_args.token,
+                streaming=data_args.streaming,
+            )
+            raw_datasets["train"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[{data_args.validation_split_percentage}%:]",
+                cache_dir=model_args.cache_dir,
+                token=model_args.token,
+                streaming=data_args.streaming,
+            )
+    else:
+        data_files = {}
+        dataset_args = {}
+        if data_args.train_file is not None:
+            data_files["train"] = data_args.train_file
+        if data_args.validation_file is not None:
+            data_files["validation"] = data_args.validation_file
+        extension = (
+            data_args.train_file.split(".")[-1]
+            if data_args.train_file is not None
+            else data_args.validation_file.split(".")[-1]
+        )
+        if extension == "txt":
+            extension = "text"
+            dataset_args["keep_linebreaks"] = data_args.keep_linebreaks
+        raw_datasets = load_dataset(
+            extension,
+            data_files=data_files,
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+            **dataset_args,
+        )
+        # If no validation data is there, validation_split_percentage will be used to divide the dataset.
+        if "validation" not in raw_datasets.keys():
+            raw_datasets["validation"] = load_dataset(
+                extension,
+                data_files=data_files,
+                split=f"train[:{data_args.validation_split_percentage}%]",
+                cache_dir=model_args.cache_dir,
+                token=model_args.token,
+                **dataset_args,
+            )
+            raw_datasets["train"] = load_dataset(
+                extension,
+                data_files=data_files,
+                split=f"train[{data_args.validation_split_percentage}%:]",
+                cache_dir=model_args.cache_dir,
+                token=model_args.token,
+                **dataset_args,
+            )
+    
+    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
+    # https://huggingface.co/docs/datasets/loading_datasets.
+
+    # Load pretrained model and tokenizer
+    #
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+
+    config_kwargs = {
+        "cache_dir": model_args.cache_dir,
+        "revision": model_args.model_revision,
+        "token": model_args.token,
+        "trust_remote_code": model_args.trust_remote_code,
+    }
+    if model_args.config_name:
+        config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
+    elif model_args.model_name_or_path:
+        config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
+    else:
+        config = CONFIG_MAPPING[model_args.model_type]()
+        logger.warning("You are instantiating a new config instance from scratch.")
+        if model_args.config_overrides is not None:
+            logger.info(f"Overriding config: {model_args.config_overrides}")
+            config.update_from_string(model_args.config_overrides)
+            logger.info(f"New config: {config}")
+
+    tokenizer_kwargs = {
+        "cache_dir": model_args.cache_dir,
+        "use_fast": model_args.use_fast_tokenizer,
+        "revision": model_args.model_revision,
+        "token": model_args.token,
+        "padding": 'max_length',
+        "trust_remote_code": model_args.trust_remote_code,
+        "model_max_length": config.max_position_embeddings,
+        "return_tensors":'pt'
+    }
+    if model_args.tokenizer_name:
+        tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
+    elif model_args.model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)
+    else:
+        raise ValueError(
+            "You are instantiating a new tokenizer from scratch. This is not supported by this script. "
+            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
+        )
+    if tokenizer.pad_token != tokenizer.unk_token:
+        tokenizer.pad_token = tokenizer.unk_token
+
+    if model_args.model_name_or_path:
+        torch_dtype = (
+            model_args.torch_dtype
+            if model_args.torch_dtype in ["auto", None]
+            else getattr(torch, model_args.torch_dtype)
+        )
+        model = AutoModelForCausalLM.from_pretrained(
+            model_args.model_name_or_path,
+            from_tf=bool(".ckpt" in model_args.model_name_or_path),
+            config=config,
+            cache_dir=model_args.cache_dir,
+            revision=model_args.model_revision,
+            token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
+            torch_dtype=torch_dtype,
+            low_cpu_mem_usage=model_args.low_cpu_mem_usage,
+        )
+    else:
+        model = AutoModelForCausalLM.from_config(config, trust_remote_code=model_args.trust_remote_code)
+        n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())
+        logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params")
+
+    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
+    # on a small vocab and want a smaller embedding size, remove this test.
+    embedding_size = model.get_input_embeddings().weight.shape[0]
+    if len(tokenizer) > embedding_size:
+        model.resize_token_embeddings(len(tokenizer))    
+    
+    if "ShareGPT" == data_args.dataset_name_local:
+        raw_datasets_preprocessed = shareGPT_pipeline(tokenizer=tokenizer, raw_datasets=raw_datasets, overwrite_cache=data_args.overwrite_cache) 
+    if "RedPajama" == data_args.dataset_name_local:
+        raw_datasets_preprocessed = raw_datasets
+    
+### HEREE
+    # Preprocessing the datasets.
+    # First we tokenize all the texts.
+    if training_args.do_train:
+        column_names = list(raw_datasets_preprocessed["train"].features)
+    else:
+        column_names = list(raw_datasets_preprocessed["validation"].features)
+    text_column_name = "text"
+
+
+    # since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
+    tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
+    
+    def tokenize_function(examples):
+        with CaptureLogger(tok_logger) as cl:
+            # print(tokenizer(examples[text_column_name]))
+            # output = tokenizer(examples[text_column_name])
+            output = tokenizer(
+                            examples[text_column_name],
+                            return_tensors="pt",
+                            padding="max_length",
+                            max_length=tokenizer.model_max_length,
+                            truncation=True,
+                        )
+            # output = input_ids.clone() 
+        # clm input could be much much longer than block_size
+        if "Token indices sequence length is longer than the" in cl.out:
+            tok_logger.warning(
+                "^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits"
+                " before being passed to the model."
+            )
+        return output
+    
+    with training_args.main_process_first(desc="dataset map tokenization"):
+        if not data_args.streaming:
+            tokenized_datasets = raw_datasets_preprocessed.map(
+                tokenize_function,
+                batched=True,
+                num_proc=data_args.preprocessing_num_workers,
+                remove_columns=column_names,
+                load_from_cache_file=not data_args.overwrite_cache,
+                desc="Running tokenizer on dataset",
+            )
+        else:
+            tokenized_datasets = raw_datasets_preprocessed.map(
+                tokenize_function,
+                batched=True,
+                remove_columns=column_names,
+                load_from_cache_file=not data_args.overwrite_cache,
+            )
+    if hasattr(config, "max_position_embeddings"):
+        max_pos_embeddings = config.max_position_embeddings
+    else:
+        # Define a default value if the attribute is missing in the config.
+        max_pos_embeddings = 1024
+
+    if data_args.block_size is None:
+        block_size = tokenizer.model_max_length
+        if block_size > max_pos_embeddings:
+            logger.warning(
+                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
+                f"Using block_size={min(1024, max_pos_embeddings)} instead. You can change that default value by passing --block_size xxx."
+            )
+            if max_pos_embeddings > 0:
+                block_size = min(1024, max_pos_embeddings)
+            else:
+                block_size = 1024
+    else:
+        if data_args.block_size > tokenizer.model_max_length:
+            logger.warning(
+                f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model "
+                f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
+            )
+        block_size = min(data_args.block_size, tokenizer.model_max_length)
+
+    # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
+    def group_texts(examples):
+        # Concatenate all texts.
+        concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
+        total_length = len(concatenated_examples[list(examples.keys())[0]])
+        # We drop the small remainder, and if the total_length < block_size  we exclude this batch and return an empty dict.
+        # We could add padding if the model supported it instead of this drop, you can customize this part to your needs.
+        total_length = (total_length // block_size) * block_size
+        # Split by chunks of max_len.
+        result = {
+            k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
+            for k, t in concatenated_examples.items()
+        }
+        result["labels"] = result["input_ids"].copy()
+        return result
+
+    # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
+    # for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
+    # to preprocess.
+    #
+    # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
+    # https://huggingface.co/docs/datasets/process#map
+
+    with training_args.main_process_first(desc="grouping texts together"):
+        if not data_args.streaming:
+            lm_datasets = tokenized_datasets.map(
+                group_texts,
+                batched=True,
+                num_proc=data_args.preprocessing_num_workers,
+                load_from_cache_file=not data_args.overwrite_cache,
+                desc=f"Grouping texts in chunks of {block_size}",
+            )
+        else:
+            lm_datasets = tokenized_datasets.map(
+                group_texts,
+                batched=True,
+                load_from_cache_file=not data_args.overwrite_cache,
+            )
+
+    if training_args.do_train:
+        if "train" not in tokenized_datasets:
+            raise ValueError("--do_train requires a train dataset")
+        train_dataset = lm_datasets["train"]
+        if data_args.max_train_samples is not None:
+            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
+            train_dataset = train_dataset.select(range(max_train_samples))
+
+    if training_args.do_eval:
+        if "validation" not in tokenized_datasets:
+            raise ValueError("--do_eval requires a validation dataset")
+        eval_dataset = lm_datasets["validation"]
+        if data_args.max_eval_samples is not None:
+            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
+            eval_dataset = eval_dataset.select(range(max_eval_samples))
+
+        def preprocess_logits_for_metrics(logits, labels):
+            if isinstance(logits, tuple):
+                # Depending on the model and config, logits may contain extra tensors,
+                # like past_key_values, but logits always come first
+                logits = logits[0]
+            return logits.argmax(dim=-1)
+
+        
+        def compute_metrics(eval_preds):
+            accuracy = evaluate.load("accuracy", cache_dir=model_args.cache_dir)
+            perplexity = evaluate.load("perplexity", module_type="metric")
+            preds, labels = eval_preds
+            # preds have the same shape as the labels, after the argmax(-1) has been calculated
+            # by preprocess_logits_for_metrics but we need to shift the labels
+            labels = labels[:, 1:].reshape(-1)
+            preds = preds[:, :-1].reshape(-1)
+            accuracy = accuracy.compute(predictions=preds, references=labels)
+            # perplexity = perplexity.compute(predictions=preds, model_id='llama')
+            return accuracy
+
+    # Initialize the optimizer
+    optimizer = torch.optim.AdamW(model.parameters(), lr=training_args.learning_rate, weight_decay=training_args.weight_decay)
+    # Calculate the number of training steps
+    train_steps = (len(train_dataset) // (training_args.per_device_train_batch_size * training_args._n_gpu)) * training_args.num_train_epochs
+
+    # Initialize the scheduler
+    linear_scheduler = transformers.get_linear_schedule_with_warmup(
+        optimizer,
+        num_warmup_steps=train_steps*training_args.warmup_ratio,
+        num_training_steps=train_steps
+    )
+        
+    # Initialize our Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
+        tokenizer=tokenizer,
+        optimizers=(optimizer, linear_scheduler),
+        # Data collator will default to DataCollatorWithPadding, so we change it.
+        data_collator=default_data_collator,
+        compute_metrics=compute_metrics if training_args.do_eval else None,
+        preprocess_logits_for_metrics=preprocess_logits_for_metrics
+        if training_args.do_eval else None,
+    )
+
+    # Training
+    if training_args.do_train:
+        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)
+        trainer.save_model()  # Saves the tokenizer too for easy upload
+
+        metrics = train_result.metrics
+
+        max_train_samples = (
+            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+        )
+        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+        try:
+            torch.save([vars(a) for a in [training_args, data_args, model_args]], os.path.join(training_args.output_dir, "args.bin"))
+        except:
+            logger.info("Failed to save arguments")
+
+    # Evaluation
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+
+        metrics = trainer.evaluate()
+
+        max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
+        metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
+        try:
+            perplexity = math.exp(metrics["eval_loss"])
+        except OverflowError:
+            perplexity = float("inf")
+        metrics["perplexity"] = perplexity
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"}
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset_args"] = data_args.dataset_config_name
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
+    elif data_args.dataset_name_hub is not None:
+        kwargs["dataset"] = data_args.dataset_name_hub
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+if __name__ == "__main__":
+    main()

experiment_code/submit_job.sh

@@ -0,0 +1,70 @@
+#!/bin/bash
+#SBATCH -p g24
+#SBATCH --job-name=myjob_shareGPT
+#SBATCH --qos=high
+#SBATCH --nodes=1                 # Number of nodes
+#SBATCH --ntasks=1         # Number of tasks (one for each script)
+#SBATCH --cpus-per-task=60
+#SBATCH --gres=gpu:6
+#SBATCH --array=1-1                      # Array range
+# #SBATCH --output=./slurm_outputs/run_clm_job_%A_task_%a.out  # Standard output
+#SBATCH --output=/dev/null     # Discard standard output  # Because we write to the log.txt file
+
+# # Get the current date and time
+current_time=$(date +"%d-%m_%H-%M")
+OUTPUT_DIR="./training_outputs_job_${SLURM_ARRAY_JOB_ID}_${SLURM_ARRAY_TASK_ID}_${current_time}"
+export DEFAULT_CONFIG_FILE="./config/config1.yaml"
+
+while test $# -gt 0; do
+    echo $1
+    case "$1" in 
+        --output_dir)
+            shift
+            OUTPUT_DIR=$1
+            shift
+            ;;
+    esac
+done
+
+mkdir_is_exists() {
+    if [ -d "$1" ]; then
+        echo "Directory '$1' already exists."
+    else
+        mkdir -p "$1"
+        echo "Directory '$1' created."
+    fi
+}
+
+
+mkdir_is_exists $OUTPUT_DIR
+mkdir_is_exists $OUTPUT_DIR/experiment_code
+git log -n 1 > $OUTPUT_DIR/commit.txt
+pip freeze > $OUTPUT_DIR/pip_freeze.txt
+echo $0 $ARGS $current_time > $OUTPUT_DIR/cmd.txt
+cp -r ./run_clm.py $OUTPUT_DIR/experiment_code
+cp -r ./prepare_sharegpt.py $OUTPUT_DIR/experiment_code
+cp -r config $OUTPUT_DIR/experiment_code
+cp -r ./submit_job.sh $OUTPUT_DIR/experiment_code
+cp -r ./requirements.txt $OUTPUT_DIR/experiment_code
+
+# Define the Python scripts and their corresponding input files
+declare -A scripts_and_inputs=(
+        ["1"]="./config/config1.yaml"
+    #  ["2"]="./config/config_redpajama.yaml"
+    # ["3"]="./config/config1.yaml"
+    # ["4"]="./config/config1.yaml"
+    # ["5"]="./config/config1.yaml"
+)
+
+# Launch each script with its corresponding input file as a separate task
+echo "Starting job array task: $SLURM_ARRAY_TASK_ID"
+PARAMS="--output_dir $OUTPUT_DIR --logging_dir $OUTPUT_DIR --config_file ${scripts_and_inputs[$SLURM_ARRAY_TASK_ID]}"
+
+srun --exclusive python run_clm.py $PARAMS 2>&1 | tee $OUTPUT_DIR/log.txt
+
+
+# Wait for all background jobs to complete
+wait
+
+# Print a message indicating completion
+echo "All Python scripts have been executed."

log.txt

@@ -0,0 +1,306 @@
+2024-06-05 03:05:23.315324: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
+2024-06-05 03:05:23.315399: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
+2024-06-05 03:05:23.317038: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-06-05 03:05:23.326431: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-06-05 03:05:26.483871: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+06/05/2024 03:05:35 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 6, distributed training: False, 16-bits training: False
+06/05/2024 03:05:35 - INFO - __main__ - Training/evaluation parameters TrainingArguments(
+_n_gpu=6,
+accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None},
+adafactor=False,
+adam_beta1=0.9,
+adam_beta2=0.999,
+adam_epsilon=1e-08,
+auto_find_batch_size=False,
+bf16=False,
+bf16_full_eval=False,
+data_seed=None,
+dataloader_drop_last=False,
+dataloader_num_workers=0,
+dataloader_persistent_workers=False,
+dataloader_pin_memory=True,
+dataloader_prefetch_factor=None,
+ddp_backend=None,
+ddp_broadcast_buffers=None,
+ddp_bucket_cap_mb=None,
+ddp_find_unused_parameters=None,
+ddp_timeout=1800,
+debug=[],
+deepspeed=None,
+disable_tqdm=False,
+dispatch_batches=None,
+do_eval=True,
+do_predict=False,
+do_train=True,
+eval_accumulation_steps=None,
+eval_delay=0,
+eval_do_concat_batches=True,
+eval_steps=1000,
+eval_strategy=steps,
+evaluation_strategy=None,
+fp16=False,
+fp16_backend=auto,
+fp16_full_eval=False,
+fp16_opt_level=O1,
+fsdp=[],
+fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False},
+fsdp_min_num_params=0,
+fsdp_transformer_layer_cls_to_wrap=None,
+full_determinism=False,
+gradient_accumulation_steps=1,
+gradient_checkpointing=False,
+gradient_checkpointing_kwargs=None,
+greater_is_better=None,
+group_by_length=False,
+half_precision_backend=auto,
+hub_always_push=False,
+hub_model_id=DorinSht/ShareGPT_llama2_68M,
+hub_private_repo=False,
+hub_strategy=all_checkpoints,
+hub_token=<HUB_TOKEN>,
+ignore_data_skip=False,
+include_inputs_for_metrics=False,
+include_num_input_tokens_seen=False,
+include_tokens_per_second=False,
+jit_mode_eval=False,
+label_names=None,
+label_smoothing_factor=0.0,
+learning_rate=0.0001,
+length_column_name=length,
+load_best_model_at_end=False,
+local_rank=0,
+log_level=passive,
+log_level_replica=warning,
+log_on_each_node=True,
+logging_dir=./training_outputs_job_117568_1_05-06_03-05,
+logging_first_step=False,
+logging_nan_inf_filter=True,
+logging_steps=500,
+logging_strategy=steps,
+lr_scheduler_kwargs={},
+lr_scheduler_type=linear,
+max_grad_norm=1.0,
+max_steps=-1,
+metric_for_best_model=None,
+mp_parameters=,
+neftune_noise_alpha=None,
+no_cuda=False,
+num_train_epochs=3.0,
+optim=adamw_torch,
+optim_args=None,
+optim_target_modules=None,
+output_dir=./training_outputs_job_117568_1_05-06_03-05,
+overwrite_output_dir=True,
+past_index=-1,
+per_device_eval_batch_size=8,
+per_device_train_batch_size=4,
+prediction_loss_only=False,
+push_to_hub=True,
+push_to_hub_model_id=None,
+push_to_hub_organization=None,
+push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
+ray_scope=last,
+remove_unused_columns=True,
+report_to=['tensorboard'],
+restore_callback_states_from_checkpoint=False,
+resume_from_checkpoint=None,
+run_name=/home/dshteyma/target_draft_coupling_code/target_draft_training/training_outputs,
+save_on_each_node=False,
+save_only_model=False,
+save_safetensors=True,
+save_steps=1000,
+save_strategy=steps,
+save_total_limit=None,
+seed=42,
+skip_memory_metrics=True,
+split_batches=None,
+tf32=None,
+torch_compile=False,
+torch_compile_backend=None,
+torch_compile_mode=None,
+torchdynamo=None,
+tpu_metrics_debug=False,
+tpu_num_cores=None,
+use_cpu=False,
+use_ipex=False,
+use_legacy_prediction_loop=False,
+use_mps_device=False,
+warmup_ratio=0.05,
+warmup_steps=0,
+weight_decay=0.01,
+)
+Using custom data configuration default-afe4b27d28cbdcb1
+06/05/2024 03:05:35 - INFO - datasets.builder - Using custom data configuration default-afe4b27d28cbdcb1
+Loading Dataset Infos from /home/dshteyma/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json
+06/05/2024 03:05:35 - INFO - datasets.info - Loading Dataset Infos from /home/dshteyma/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json
+Overwrite dataset info from restored data version if exists.
+06/05/2024 03:05:36 - INFO - datasets.builder - Overwrite dataset info from restored data version if exists.
+Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
+06/05/2024 03:05:36 - INFO - datasets.info - Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
+06/05/2024 03:05:36 - INFO - datasets.builder - Found cached dataset json (/home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7)
+Found cached dataset json (/home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7)
+Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
+06/05/2024 03:05:36 - INFO - datasets.info - Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
+Using custom data configuration default-afe4b27d28cbdcb1
+06/05/2024 03:05:36 - INFO - datasets.builder - Using custom data configuration default-afe4b27d28cbdcb1
+Loading Dataset Infos from /home/dshteyma/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json
+06/05/2024 03:05:36 - INFO - datasets.info - Loading Dataset Infos from /home/dshteyma/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json
+06/05/2024 03:05:36 - INFO - datasets.builder - Overwrite dataset info from restored data version if exists.
+Overwrite dataset info from restored data version if exists.
+Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
+06/05/2024 03:05:36 - INFO - datasets.info - Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
+Found cached dataset json (/home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7)
+06/05/2024 03:05:36 - INFO - datasets.builder - Found cached dataset json (/home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7)
+Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
+06/05/2024 03:05:36 - INFO - datasets.info - Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
+Using custom data configuration default-afe4b27d28cbdcb1
+06/05/2024 03:05:37 - INFO - datasets.builder - Using custom data configuration default-afe4b27d28cbdcb1
+Loading Dataset Infos from /home/dshteyma/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json
+06/05/2024 03:05:37 - INFO - datasets.info - Loading Dataset Infos from /home/dshteyma/miniconda3/lib/python3.9/site-packages/datasets/packaged_modules/json
+Overwrite dataset info from restored data version if exists.
+06/05/2024 03:05:37 - INFO - datasets.builder - Overwrite dataset info from restored data version if exists.
+Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
+06/05/2024 03:05:37 - INFO - datasets.info - Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
+06/05/2024 03:05:37 - INFO - datasets.builder - Found cached dataset json (/home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7)
+Found cached dataset json (/home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7)
+Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
+06/05/2024 03:05:37 - INFO - datasets.info - Loading Dataset info from /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7
+[INFO|configuration_utils.py:726] 2024-06-05 03:05:37,626 >> loading configuration file config.json from cache at /home/dshteyma/.cache/huggingface/hub/models--JackFram--llama-68m/snapshots/964a5d77df908b69f8d6476fb70e940425b04cb5/config.json
+[INFO|configuration_utils.py:789] 2024-06-05 03:05:37,628 >> Model config LlamaConfig {
+  "_name_or_path": "JackFram/llama-68m",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 2,
+  "num_key_value_heads": 12,
+  "pad_token_id": 1,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32000
+}
+
+[INFO|tokenization_utils_base.py:2102] 2024-06-05 03:05:37,771 >> loading file tokenizer.model from cache at /home/dshteyma/.cache/huggingface/hub/models--JackFram--llama-68m/snapshots/964a5d77df908b69f8d6476fb70e940425b04cb5/tokenizer.model
+[INFO|tokenization_utils_base.py:2102] 2024-06-05 03:05:37,771 >> loading file tokenizer.json from cache at None
+[INFO|tokenization_utils_base.py:2102] 2024-06-05 03:05:37,771 >> loading file added_tokens.json from cache at None
+[INFO|tokenization_utils_base.py:2102] 2024-06-05 03:05:37,771 >> loading file special_tokens_map.json from cache at /home/dshteyma/.cache/huggingface/hub/models--JackFram--llama-68m/snapshots/964a5d77df908b69f8d6476fb70e940425b04cb5/special_tokens_map.json
+[INFO|tokenization_utils_base.py:2102] 2024-06-05 03:05:37,771 >> loading file tokenizer_config.json from cache at /home/dshteyma/.cache/huggingface/hub/models--JackFram--llama-68m/snapshots/964a5d77df908b69f8d6476fb70e940425b04cb5/tokenizer_config.json
+[WARNING|logging.py:329] 2024-06-05 03:05:37,772 >> You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
+[WARNING|logging.py:329] 2024-06-05 03:05:37,868 >> You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
+[INFO|configuration_utils.py:936] 2024-06-05 03:05:38,449 >> Generate config GenerationConfig {
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "pad_token_id": 1
+}
+
+06/05/2024 03:05:39 - INFO - __main__ - Training new model from scratch - Total size=64.88M params
+06/05/2024 03:05:39 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-988d048fea8d2473.arrow
+Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-988d048fea8d2473.arrow
+06/05/2024 03:05:39 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-4e281c930893bca9.arrow
+Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-4e281c930893bca9.arrow
+06/05/2024 03:05:39 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-3fe350bccdda6078.arrow
+Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-3fe350bccdda6078.arrow
+06/05/2024 03:05:39 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-35d09b588a0c62b9.arrow
+Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-35d09b588a0c62b9.arrow
+06/05/2024 03:05:39 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-4e5279ee31a5d8d3.arrow
+Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-4e5279ee31a5d8d3.arrow
+06/05/2024 03:05:39 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-63d56456928edd43.arrow
+Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-63d56456928edd43.arrow
+06/05/2024 03:05:39 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-6a784a78d9818240.arrow
+Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-6a784a78d9818240.arrow
+06/05/2024 03:05:39 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-46540f58a00a92bf.arrow
+Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-46540f58a00a92bf.arrow
+06/05/2024 03:05:39 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-73605724efaea9d2.arrow
+Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-73605724efaea9d2.arrow
+06/05/2024 03:05:39 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-83d3df87e1b82021.arrow
+Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-83d3df87e1b82021.arrow
+06/05/2024 03:05:39 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-efdbb02491aa6344.arrow
+Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-efdbb02491aa6344.arrow
+06/05/2024 03:05:39 - INFO - datasets.arrow_dataset - Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-0cf2ae38fef927f3.arrow
+Loading cached processed dataset at /home/dshteyma/.cache/huggingface/datasets/json/default-afe4b27d28cbdcb1/0.0.0/c8d2d9508a2a2067ab02cd118834ecef34c3700d143b31835ec4235bf10109f7/cache-0cf2ae38fef927f3.arrow
+06/05/2024 03:05:39 - WARNING - accelerate.utils.other - Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+[INFO|trainer.py:2068] 2024-06-05 03:05:40,316 >> ***** Running training *****
+[INFO|trainer.py:2069] 2024-06-05 03:05:40,316 >>   Num examples = 90,745
+[INFO|trainer.py:2070] 2024-06-05 03:05:40,316 >>   Num Epochs = 3
+[INFO|trainer.py:2071] 2024-06-05 03:05:40,316 >>   Instantaneous batch size per device = 4
+[INFO|trainer.py:2073] 2024-06-05 03:05:40,316 >>   Training with DataParallel so batch size has been adjusted to: 24
+[INFO|trainer.py:2074] 2024-06-05 03:05:40,316 >>   Total train batch size (w. parallel, distributed & accumulation) = 24
+[INFO|trainer.py:2075] 2024-06-05 03:05:40,316 >>   Gradient Accumulation steps = 1
+[INFO|trainer.py:2076] 2024-06-05 03:05:40,316 >>   Total optimization steps = 11,346
+[INFO|trainer.py:2077] 2024-06-05 03:05:40,317 >>   Number of trainable parameters = 68,030,208
+
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+  warnings.warn('Was asked to gather along dimension 0, but all '
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  9%|▉         | 1000/11346 [16:16<2:47:39,  1.03it/s][INFO|trainer.py:3662] 2024-06-05 03:21:57,117 >> ***** Running Evaluation *****
+[INFO|trainer.py:3664] 2024-06-05 03:21:57,117 >>   Num examples = 1840
+[INFO|trainer.py:3667] 2024-06-05 03:21:57,117 >>   Batch size = 48
+{'loss': 5.1118, 'grad_norm': 0.8546391725540161, 'learning_rate': 8.816009873931059e-05, 'epoch': 0.13}
+{'loss': 3.406, 'grad_norm': 0.8593688607215881, 'learning_rate': 9.59831475011252e-05, 'epoch': 0.26}
+
+
  0%|          | 0/39 [00:00<?, ?it/s]
+
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+
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                                               [INFO|trainer.py:3353] 2024-06-05 03:23:11,169 >> Saving model checkpoint to ./training_outputs_job_117568_1_05-06_03-05/checkpoint-1000
+[INFO|configuration_utils.py:471] 2024-06-05 03:23:11,183 >> Configuration saved in ./training_outputs_job_117568_1_05-06_03-05/checkpoint-1000/config.json
+[INFO|configuration_utils.py:705] 2024-06-05 03:23:11,189 >> Configuration saved in ./training_outputs_job_117568_1_05-06_03-05/checkpoint-1000/generation_config.json
+[INFO|modeling_utils.py:2592] 2024-06-05 03:23:12,091 >> Model weights saved in ./training_outputs_job_117568_1_05-06_03-05/checkpoint-1000/model.safetensors
+[INFO|tokenization_utils_base.py:2503] 2024-06-05 03:23:12,105 >> tokenizer config file saved in ./training_outputs_job_117568_1_05-06_03-05/checkpoint-1000/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2512] 2024-06-05 03:23:12,110 >> Special tokens file saved in ./training_outputs_job_117568_1_05-06_03-05/checkpoint-1000/special_tokens_map.json
+[INFO|tokenization_utils_base.py:2503] 2024-06-05 03:23:14,277 >> tokenizer config file saved in ./training_outputs_job_117568_1_05-06_03-05/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2512] 2024-06-05 03:23:14,282 >> Special tokens file saved in ./training_outputs_job_117568_1_05-06_03-05/special_tokens_map.json
+/home/dshteyma/miniconda3/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
+  warnings.warn('Was asked to gather along dimension 0, but all '

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