Quasar: Mistral, mastering mathematical reasoning.
import time

import torch  # , json, os, sys
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    LlamaForCausalLM,
    LlamaTokenizer,
    MistralForCausalLM,
)

# import bitsandbytes

tokenizer = LlamaTokenizer.from_pretrained(
    "./collectivecognition-run6", trust_remote_code=True
)
model = MistralForCausalLM.from_pretrained(
    "./collectivecognition-run6",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    use_flash_attention_2=True,
)
benchmarks = [
    "Hello, tell me about the history of the United States",
    "Roleplay as a scientist, who just discovered artificial general intelligence. What do you think about this discovery? What possibilities are there now?",
]

index = 0
for obj in benchmarks:
    index += 1
    if index < 1:
        continue
    else:
        start_time = time.time()  # Start timing
        prompt = f"USER:\n{obj}\n\nASSISTANT:\n"
        input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")
        generated_ids = model.generate(
            input_ids, max_new_tokens=2048, temperature=None
        )  # , do_sample=True, eos_token_id=tokenizer.eos_token_id)
        response = tokenizer.decode(
            generated_ids[0][input_ids.shape[-1] :],
            skip_special_tokens=True,
            clean_up_tokenization_space=True,
        )
        print(f"Response  {index}: {response}")

        end_time = time.time()  # End timing
        elapsed_time = end_time - start_time  # Calculate time taken for the iteration
        print(f"Time taken for Response {index}: {elapsed_time:.4f} seconds")
        print(f"tokens total: {len(tokenizer.encode(response))}")

inference.py
kyegomez/Quasar
import os
from datetime import datetime

import torch
import transformers
import wandb
from accelerate import Accelerator, FullyShardedDataParallelPlugin
from datasets import load_dataset
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from torch.distributed.fsdp.fully_sharded_data_parallel import (
    FullOptimStateDictConfig,
    FullStateDictConfig,
)
from transformers import AutoModelForCausalLM, AutoTokenizer  # BitsAndBytesConfig

fsdp_plugin = FullyShardedDataParallelPlugin(
    state_dict_config=FullStateDictConfig(offload_to_cpu=True, rank0_only=False),
    optim_state_dict_config=FullOptimStateDictConfig(
        offload_to_cpu=True, rank0_only=False
    ),
)

accelerator = Accelerator(fsdp_plugin=fsdp_plugin)


## load the dataset
train_dataset = load_dataset(
    "kye/metamath-mistal-tokenized-16384", split="train"
)  # split='train')

base_model_id = "mistralai/Mistral-7B-Instruct-v0.1"
# bnb_config = BitsAndBytesConfig(
#     load_in_4bit=True,
#     bnb_4bit_use_double_quant=True,
#     bnb_4bit_quant_type="nf4",
#     bnb_4bit_compute_dtype=torch.bfloat16
# )

model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
)  # quantization_config=bnb_config)

tokenizer = AutoTokenizer.from_pretrained(
    base_model_id, model_max_length=8192, padding_side="left", add_eos_token=True
)

tokenizer.pad_token = tokenizer.eos_token


def tokenize(prompt):
    result = tokenizer(
        prompt,
        truncation=True,
        max_length=8192,
        padding="max_length",
    )
    result["labels"] = result["input_ids"].copy()
    return result


# def generate_and_tokenize_prompt(data_point):
#     full_prompt =f"""Given a target sentence construct the underlying meaning representation of the input sentence as a single function with attributes and attribute values.
#     This function should describe the target string accurately and the function must be one of the following ['inform', 'request', 'give_opinion', 'confirm', 'verify_attribute', 'suggest', 'request_explanation', 'recommend', 'request_attribute'].
#     The attributes must be one of the following: ['name', 'exp_release_date', 'release_year', 'developer', 'esrb', 'rating', 'genres', 'player_perspective', 'has_multiplayer', 'platforms', 'available_on_steam', 'has_linux_release', 'has_mac_release', 'specifier']

#     ### Target sentence:
#     {data_point["target"]}

#     ### Meaning representation:
#     {data_point["meaning_representation"]}
#     """
#     return tokenize(full_prompt)

# tokenized_train_dataset = train_dataset.map(generate_and_tokenize_prompt)
# tokenized_val_dataset = eval_dataset.map(generate_and_tokenize_prompt)


model.gradient_checkpointing_enable()
model = prepare_model_for_kbit_training(model)


def print_trainable_parameters(model):
    """
    Prints the number of trainable parameters in the model.
    """
    trainable_params = 0
    all_param = 0
    for _, param in model.named_parameters():
        all_param += param.numel()
        if param.requires_grad:
            trainable_params += param.numel()
    print(
        f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
    )


config = LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=[
        "q_proj",
        "k_proj",
        "v_proj",
        "o_proj",
        "gate_proj",
        "up_proj",
        "down_proj",
        "lm_head",
    ],
    bias="none",
    lora_dropout=0.05,  # Conventional
    task_type="CAUSAL_LM",
)

model = get_peft_model(model, config)
print_trainable_parameters(model)

# Apply the accelerator. You can comment this out to remove the accelerator.
model = accelerator.prepare_model(model)


wandb.login()

wandb_project = "mistral-finetune"
if len(wandb_project) > 0:
    os.environ["WANDB_PROJECT"] = wandb_project


if torch.cuda.device_count() > 1:  # If more than 1 GPU
    model.is_parallelizable = True
    model.model_parallel = True


project = "mistral-finetune"
base_model_name = "mistral"
run_name = base_model_name + "-" + project
output_dir = "./" + run_name
tokenizer.pad_token = tokenizer.eos_token

trainer = transformers.Trainer(
    model=model,
    train_dataset=train_dataset,
    args=transformers.TrainingArguments(
        output_dir=output_dir,
        warmup_steps=5,
        per_device_train_batch_size=2,
        gradient_accumulation_steps=4,
        max_steps=1000,
        learning_rate=2.5e-5,  # Want about 10x smaller than the Mistral learning rate
        logging_steps=50,
        bf16=True,
        optim="paged_adamw_8bit",
        logging_dir="./logs",  # Directory for storing logs
        save_strategy="steps",  # Save the model checkpoint every logging step
        save_steps=50,  # Save checkpoints every 50 steps
        evaluation_strategy="steps",  # Evaluate the model every logging step
        eval_steps=50,  # Evaluate and save checkpoints every 50 steps
        do_eval=True,  # Perform evaluation at the end of training
        report_to="wandb",  # Comment this out if you don't want to use weights & baises
        run_name=f"{run_name}-{datetime.now().strftime('%Y-%m-%d-%H-%M')}",  # Name of the W&B run (optional)
    ),
    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
)

model.config.use_cache = False  # silence the warnings. Please re-enable for inference!
trainer.train()


# base_model = AutoModelForCausalLM.from_pretrained(
#     base_model_id,  # Mistral, same as before
#     quantization_config=bnb_config,  # Same quantization config as before
#     device_map="auto",
#     trust_remote_code=True,
#     use_auth_token=True
# )

# tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
# tokenizer.pad_token = tokenizer.eos_token

quasar/finetune.py
kyegomez/Quasar
import argparse
import copy
import importlib
import json
import logging
import os
import sys
import warnings
from collections import defaultdict
from dataclasses import dataclass, field
from os.path import exists, isdir, join
from typing import Dict, Optional, Sequence

import bitsandbytes as bnb
import evaluate
import numpy as np
import pandas as pd
import torch
import transformers
from datasets import Dataset, load_dataset
from packaging import version
from packaging.version import parse
from peft import LoraConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training
from peft.tuners.lora import LoraLayer
from torch.nn.utils.rnn import pad_sequence
from tqdm import tqdm
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    LlamaTokenizer,
    Seq2SeqTrainer,
    set_seed,
)
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR


def is_ipex_available():
    def get_major_and_minor_from_version(full_version):
        return (
            str(version.parse(full_version).major)
            + "."
            + str(version.parse(full_version).minor)
        )

    _torch_version = importlib.metadata.version("torch")
    if importlib.util.find_spec("intel_extension_for_pytorch") is None:
        return False
    _ipex_version = "N/A"
    try:
        _ipex_version = importlib.metadata.version("intel_extension_for_pytorch")
    except importlib.metadata.PackageNotFoundError:
        return False
    torch_major_and_minor = get_major_and_minor_from_version(_torch_version)
    ipex_major_and_minor = get_major_and_minor_from_version(_ipex_version)
    if torch_major_and_minor != ipex_major_and_minor:
        warnings.warn(
            f"Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"
            f" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."
        )
        return False
    return True


if torch.cuda.is_available():
    torch.backends.cuda.matmul.allow_tf32 = True

logger = logging.getLogger(__name__)

IGNORE_INDEX = -100
DEFAULT_PAD_TOKEN = "[PAD]"


@dataclass
class ModelArguments:
    model_name_or_path: Optional[str] = field(default="mistralai/Mistral-7B-v0.1")
    trust_remote_code: Optional[bool] = field(
        default=False,
        metadata={
            "help": "Enable unpickling of arbitrary code in AutoModelForCausalLM#from_pretrained."
        },
    )
    use_auth_token: Optional[bool] = field(
        default=False,
        metadata={"help": "Enables using Huggingface auth token from Git Credentials."},
    )


@dataclass
class DataArguments:
    eval_dataset_size: int = field(
        default=1024, metadata={"help": "Size of validation dataset."}
    )
    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."
        },
    )
    source_max_len: int = field(
        default=16384,
        metadata={
            "help": "Maximum source sequence length. Sequences will be right padded (and possibly truncated)."
        },
    )
    target_max_len: int = field(
        default=16384,
        metadata={
            "help": "Maximum target sequence length. Sequences will be right padded (and possibly truncated)."
        },
    )
    dataset: str = field(
        default="math",
        metadata={"help": "Which dataset to finetune on. See datamodule for options."},
    )
    dataset_format: Optional[str] = field(
        default=None,
        metadata={
            "help": "Which dataset format is used. [alpaca|chip2|self-instruct|hh-rlhf]"
        },
    )


@dataclass
class TrainingArguments(transformers.Seq2SeqTrainingArguments):
    cache_dir: Optional[str] = field(default=None)
    train_on_source: Optional[bool] = field(
        default=False,
        metadata={
            "help": "Whether to train on the input in addition to the target text."
        },
    )
    mmlu_split: Optional[str] = field(
        default="eval", metadata={"help": "The MMLU split to run on"}
    )
    mmlu_dataset: Optional[str] = field(
        default="mmlu-fs",
        metadata={
            "help": "MMLU dataset to use: options are `mmlu-zs` for zero-shot or `mmlu-fs` for few shot."
        },
    )
    do_mmlu_eval: Optional[bool] = field(
        default=False, metadata={"help": "Whether to run the MMLU evaluation."}
    )
    max_mmlu_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "If set, only evaluates on `max_mmlu_samples` of the MMMLU dataset."
        },
    )
    mmlu_source_max_len: int = field(
        default=2048, metadata={"help": "Maximum source sequence length for mmlu."}
    )
    full_finetune: bool = field(
        default=False, metadata={"help": "Finetune the entire model without adapters."}
    )
    adam8bit: bool = field(default=False, metadata={"help": "Use 8-bit adam."})
    double_quant: bool = field(
        default=True,
        metadata={
            "help": "Compress the quantization statistics through double quantization."
        },
    )
    quant_type: str = field(
        default="nf4",
        metadata={
            "help": "Quantization data type to use. Should be one of `fp4` or `nf4`."
        },
    )
    bits: int = field(default=4, metadata={"help": "How many bits to use."})
    lora_r: int = field(default=64, metadata={"help": "Lora R dimension."})
    lora_alpha: float = field(default=16, metadata={"help": " Lora alpha."})
    lora_dropout: float = field(default=0.0, metadata={"help": "Lora dropout."})
    max_memory_MB: int = field(default=80000, metadata={"help": "Free memory per gpu."})
    report_to: str = field(
        default="wandb",
        metadata={"help": "To use wandb or something else for reporting."},
    )
    output_dir: str = field(
        default="./output", metadata={"help": "The output dir for logs and checkpoints"}
    )
    optim: str = field(
        default="paged_adamw_32bit", metadata={"help": "The optimizer to be used"}
    )
    per_device_train_batch_size: int = field(
        default=1,
        metadata={
            "help": "The training batch size per GPU. Increase for better speed."
        },
    )
    gradient_accumulation_steps: int = field(
        default=16,
        metadata={
            "help": "How many gradients to accumulate before to perform an optimizer step"
        },
    )
    max_steps: int = field(
        default=10000, metadata={"help": "How many optimizer update steps to take"}
    )
    weight_decay: float = field(
        default=0.0, metadata={"help": "The L2 weight decay rate of AdamW"}
    )  # use lora dropout instead for regularization if needed
    learning_rate: float = field(default=0.0002, metadata={"help": "The learnign rate"})
    remove_unused_columns: bool = field(
        default=False,
        metadata={"help": "Removed unused columns. Needed to make this codebase work."},
    )
    max_grad_norm: float = field(
        default=0.3,
        metadata={
            "help": "Gradient clipping max norm. This is tuned and works well for all models tested."
        },
    )
    gradient_checkpointing: bool = field(
        default=True,
        metadata={"help": "Use gradient checkpointing. You want to use this."},
    )
    do_train: bool = field(
        default=True,
        metadata={"help": "To train or not to train, that is the question?"},
    )
    lr_scheduler_type: str = field(
        default="constant",
        metadata={
            "help": "Learning rate schedule. Constant a bit better than cosine, and has advantage for analysis"
        },
    )
    warmup_ratio: float = field(
        default=0.03, metadata={"help": "Fraction of steps to do a warmup for"}
    )
    logging_steps: int = field(
        default=10,
        metadata={"help": "The frequency of update steps after which to log the loss"},
    )
    group_by_length: bool = field(
        default=True,
        metadata={
            "help": "Group sequences into batches with same length. Saves memory and speeds up training considerably."
        },
    )
    save_strategy: str = field(
        default="steps", metadata={"help": "When to save checkpoints"}
    )
    save_steps: int = field(default=250, metadata={"help": "How often to save a model"})
    save_total_limit: int = field(
        default=40,
        metadata={
            "help": "How many checkpoints to save before the oldest is overwritten"
        },
    )


@dataclass
class GenerationArguments:
    # For more hyperparameters check:
    # https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig
    # Length arguments
    max_new_tokens: Optional[int] = field(
        default=256,
        metadata={
            "help": "Maximum number of new tokens to be generated in evaluation or prediction loops"
            "if predict_with_generate is set."
        },
    )
    min_new_tokens: Optional[int] = field(
        default=None, metadata={"help": "Minimum number of new tokens to generate."}
    )

    # Generation strategy
    do_sample: Optional[bool] = field(default=False)
    num_beams: Optional[int] = field(default=1)
    num_beam_groups: Optional[int] = field(default=1)
    penalty_alpha: Optional[float] = field(default=None)
    use_cache: Optional[bool] = field(default=True)

    # Hyperparameters for logit manipulation
    temperature: Optional[float] = field(default=1.0)
    top_k: Optional[int] = field(default=50)
    top_p: Optional[float] = field(default=1.0)
    typical_p: Optional[float] = field(default=1.0)
    diversity_penalty: Optional[float] = field(default=0.0)
    repetition_penalty: Optional[float] = field(default=1.0)
    length_penalty: Optional[float] = field(default=1.0)
    no_repeat_ngram_size: Optional[int] = field(default=0)


def find_all_linear_names(args, model):
    cls = (
        bnb.nn.Linear4bit
        if args.bits == 4
        else (bnb.nn.Linear8bitLt if args.bits == 8 else torch.nn.Linear)
    )
    lora_module_names = set()
    for name, module in model.named_modules():
        if isinstance(module, cls):
            names = name.split(".")
            lora_module_names.add(names[0] if len(names) == 1 else names[-1])

    if "lm_head" in lora_module_names:  # needed for 16-bit
        lora_module_names.remove("lm_head")
    return list(lora_module_names)


class SavePeftModelCallback(transformers.TrainerCallback):
    def save_model(self, args, state, kwargs):
        print("Saving PEFT checkpoint...")
        if state.best_model_checkpoint is not None:
            checkpoint_folder = os.path.join(
                state.best_model_checkpoint, "adapter_model"
            )
        else:
            checkpoint_folder = os.path.join(
                args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}"
            )

        peft_model_path = os.path.join(checkpoint_folder, "adapter_model")
        kwargs["model"].save_pretrained(peft_model_path)

        pytorch_model_path = os.path.join(checkpoint_folder, "pytorch_model.bin")
        if os.path.exists(pytorch_model_path):
            os.remove(pytorch_model_path)

    def on_save(self, args, state, control, **kwargs):
        self.save_model(args, state, kwargs)
        return control

    def on_train_end(self, args, state, control, **kwargs):
        def touch(fname, times=None):
            with open(fname, "a"):
                os.utime(fname, times)

        touch(join(args.output_dir, "completed"))
        self.save_model(args, state, kwargs)


def get_accelerate_model(args, checkpoint_dir):
    # if torch.cuda.is_available():
    #     n_gpus = torch.cuda()
    # if is_ipex_available() and torch.xpu.is_available():
    #     n_gpus = torch.xpu.device_count()

    n_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 1

    max_memory = f"{args.max_memory_MB}MB"
    max_memory = {i: max_memory for i in range(n_gpus)}
    device_map = "auto"

    # if we are in a distributed setting, we need to set the device map and max memory per device
    if os.environ.get("LOCAL_RANK") is not None:
        local_rank = int(os.environ.get("LOCAL_RANK", "0"))
        device_map = {"": local_rank}
        max_memory = {"": max_memory[local_rank]}

    if args.full_finetune:
        assert args.bits in [16, 32]

    print(f"loading base model {args.model_name_or_path}...")
    compute_dtype = (
        torch.float16 if args.fp16 else (torch.bfloat16 if args.bf16 else torch.float32)
    )

    model = AutoModelForCausalLM.from_pretrained(
        args.model_name_or_path,
        cache_dir=args.cache_dir,
        load_in_4bit=args.bits == 4,
        load_in_8bit=args.bits == 8,
        device_map=device_map,
        max_memory=max_memory,
        quantization_config=BitsAndBytesConfig(
            load_in_4bit=args.bits == 4,
            load_in_8bit=args.bits == 8,
            llm_int8_threshold=6.0,
            llm_int8_has_fp16_weight=False,
            bnb_4bit_compute_dtype=compute_dtype,
            bnb_4bit_use_double_quant=args.double_quant,
            bnb_4bit_quant_type=args.quant_type,
        ),
        torch_dtype=(
            torch.float32
            if args.fp16
            else (torch.bfloat16 if args.bf16 else torch.float32)
        ),
        trust_remote_code=args.trust_remote_code,
        use_auth_token=args.use_auth_token,
    )
    if compute_dtype == torch.float16 and args.bits == 4:
        if torch.cuda.is_bf16_supported():
            print("=" * 80)
            print(
                "Your GPU supports bfloat16, you can accelerate training with the argument --bf16"
            )
            print("=" * 80)

    if compute_dtype == torch.float16 and (
        is_ipex_available() and torch.xpu.is_available()
    ):
        compute_dtype = torch.bfloat16
        print("Intel XPU does not support float16 yet, so switching to bfloat16")

    setattr(model, "model_parallel", True)
    setattr(model, "is_parallelizable", True)

    model.config.torch_dtype = (
        torch.float32 if args.fp16 else (torch.bfloat16 if args.bf16 else torch.float32)
    )

    # Tokenizer
    tokenizer = AutoTokenizer.from_pretrained(
        args.model_name_or_path,
        cache_dir=args.cache_dir,
        padding_side="right",
        use_fast=False,  # Fast tokenizer giving issues.
        tokenizer_type="llama"
        if "llama" in args.model_name_or_path
        else None,  # Needed for HF name change
        trust_remote_code=args.trust_remote_code,
        use_auth_token=args.use_auth_token,
    )
    if tokenizer._pad_token is None:
        smart_tokenizer_and_embedding_resize(
            special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN),
            tokenizer=tokenizer,
            model=model,
        )
    if "llama" in args.model_name_or_path or isinstance(tokenizer, LlamaTokenizer):
        # LLaMA tokenizer may not have correct special tokens set.
        # Check and add them if missing to prevent them from being parsed into different tokens.
        # Note that these are present in the vocabulary.
        # Note also that `model.config.pad_token_id` is 0 which corresponds to `<unk>` token.
        print("Adding special tokens.")
        tokenizer.add_special_tokens(
            {
                "eos_token": tokenizer.convert_ids_to_tokens(model.config.eos_token_id),
                "bos_token": tokenizer.convert_ids_to_tokens(model.config.bos_token_id),
                "unk_token": tokenizer.convert_ids_to_tokens(
                    model.config.pad_token_id
                    if model.config.pad_token_id is not None
                    and model.config.pad_token_id != -1
                    else tokenizer.pad_token_id
                ),
            }
        )

    if not args.full_finetune:
        model = prepare_model_for_kbit_training(
            model, use_gradient_checkpointing=args.gradient_checkpointing
        )

    if not args.full_finetune:
        if checkpoint_dir is not None:
            print("Loading adapters from checkpoint.")
            model = PeftModel.from_pretrained(
                model, join(checkpoint_dir, "adapter_model"), is_trainable=True
            )
        else:
            print(f"adding LoRA modules...")
            modules = find_all_linear_names(args, model)
            config = LoraConfig(
                r=args.lora_r,
                lora_alpha=args.lora_alpha,
                target_modules=modules,
                lora_dropout=args.lora_dropout,
                bias="none",
                task_type="CAUSAL_LM",
            )
            model = get_peft_model(model, config)

    for name, module in model.named_modules():
        if isinstance(module, LoraLayer):
            if args.bf16:
                module = module.to(torch.bfloat16)
        if "norm" in name:
            module = module.to(torch.float32)
        if "lm_head" in name or "embed_tokens" in name:
            if hasattr(module, "weight"):
                if args.bf16 and module.weight.dtype == torch.float32:
                    module = module.to(torch.bfloat16)
    return model, tokenizer


def print_trainable_parameters(args, model):
    """
    Prints the number of trainable parameters in the model.
    """
    trainable_params = 0
    all_param = 0
    for _, param in model.named_parameters():
        all_param += param.numel()
        if param.requires_grad:
            trainable_params += param.numel()
    if args.bits == 4:
        trainable_params /= 2
    print(
        f"trainable params: {trainable_params} || "
        f"all params: {all_param} || "
        f"trainable: {100 * trainable_params / all_param}"
    )


def smart_tokenizer_and_embedding_resize(
    special_tokens_dict: Dict,
    tokenizer: transformers.PreTrainedTokenizer,
    model: transformers.PreTrainedModel,
):
    """Resize tokenizer and embedding.

    Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
    """
    num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
    model.resize_token_embeddings(len(tokenizer))

    if num_new_tokens > 0:
        input_embeddings_data = model.get_input_embeddings().weight.data
        output_embeddings_data = model.get_output_embeddings().weight.data

        input_embeddings_avg = input_embeddings_data[:-num_new_tokens].mean(
            dim=0, keepdim=True
        )
        output_embeddings_avg = output_embeddings_data[:-num_new_tokens].mean(
            dim=0, keepdim=True
        )

        input_embeddings_data[-num_new_tokens:] = input_embeddings_avg
        output_embeddings_data[-num_new_tokens:] = output_embeddings_avg


@dataclass
class DataCollatorForCausalLM(object):
    tokenizer: transformers.PreTrainedTokenizer
    source_max_len: int
    target_max_len: int
    train_on_source: bool
    predict_with_generate: bool

    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
        # Extract elements
        sources = [
            f"{self.tokenizer.bos_token}{example['input']}" for example in instances
        ]
        targets = [
            f"{example['output']}{self.tokenizer.eos_token}" for example in instances
        ]
        # Tokenize
        tokenized_sources_with_prompt = self.tokenizer(
            sources,
            max_length=self.source_max_len,
            truncation=True,
            add_special_tokens=False,
        )
        tokenized_targets = self.tokenizer(
            targets,
            max_length=self.target_max_len,
            truncation=True,
            add_special_tokens=False,
        )
        # Build the input and labels for causal LM
        input_ids = []
        labels = []
        for tokenized_source, tokenized_target in zip(
            tokenized_sources_with_prompt["input_ids"], tokenized_targets["input_ids"]
        ):
            if not self.predict_with_generate:
                input_ids.append(torch.tensor(tokenized_source + tokenized_target))
                if not self.train_on_source:
                    labels.append(
                        torch.tensor(
                            [IGNORE_INDEX for _ in range(len(tokenized_source))]
                            + copy.deepcopy(tokenized_target)
                        )
                    )
                else:
                    labels.append(
                        torch.tensor(copy.deepcopy(tokenized_source + tokenized_target))
                    )
            else:
                input_ids.append(torch.tensor(tokenized_source))
        # Apply padding
        input_ids = pad_sequence(
            input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
        )
        labels = (
            pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
            if not self.predict_with_generate
            else None
        )
        data_dict = {
            "input_ids": input_ids,
            "attention_mask": input_ids.ne(self.tokenizer.pad_token_id),
        }
        if labels is not None:
            data_dict["labels"] = labels
        return data_dict


def extract_unnatural_instructions_data(examples, extract_reformulations=False):
    out = {
        "input": [],
        "output": [],
    }
    for example_instances in examples["instances"]:
        for instance in example_instances:
            out["input"].append(instance["instruction_with_input"])
            out["output"].append(instance["output"])
    if extract_reformulations:
        for example_reformulations in examples["reformulations"]:
            if example_reformulations is not None:
                for instance in example_reformulations:
                    out["input"].append(instance["instruction_with_input"])
                    out["output"].append(instance["output"])
    return out


ALPACA_PROMPT_DICT = {
    "prompt_input": (
        "Below is an instruction that describes a task, paired with an input that provides further context. "
        "Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response: "
    ),
    "prompt_no_input": (
        "Below is an instruction that describes a task. "
        "Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Response: "
    ),
}


def extract_alpaca_dataset(example):
    if example.get("input", "") != "":
        prompt_format = ALPACA_PROMPT_DICT["prompt_input"]
    else:
        prompt_format = ALPACA_PROMPT_DICT["prompt_no_input"]
    return {"input": prompt_format.format(**example)}


def local_dataset(dataset_name):
    if dataset_name.endswith(".json") or dataset_name.endswith(".jsonl"):
        full_dataset = Dataset.from_json(path_or_paths=dataset_name)
    elif dataset_name.endswith(".csv"):
        full_dataset = Dataset.from_pandas(pd.read_csv(dataset_name))
    elif dataset_name.endswith(".tsv"):
        full_dataset = Dataset.from_pandas(pd.read_csv(dataset_name, delimiter="\t"))
    else:
        raise ValueError(f"Unsupported dataset format: {dataset_name}")

    split_dataset = full_dataset.train_test_split(test_size=0.1)
    return split_dataset


def make_data_module(tokenizer: transformers.PreTrainedTokenizer, args) -> Dict:
    """
    Make dataset and collator for supervised fine-tuning.
    Datasets are expected to have the following columns: { `input`, `output` }

    Available datasets to be selected with `dataset` argument:
        - alpaca, 52002 examples
        - alpaca cleaned, 51942 examples
        - chip2 (OIG), 210289 examples
        - self-instruct, 82612 examples
        - hh-rlhf (Anthropic), 160800 examples
        - longform, 23.7k examples
        - oasst1 (OpenAssistant) primary message tree only, 9,846 examples

    Coming soon:
        - unnatural instructions core, 66010 examples
        - unnatural instructions full, 240670 examples
        - alpaca-gpt4, 52002 examples
        - unnatural-instructions-gpt4, 9000 examples
        - supernatural-instructions, 69624 examples (same as paper with 100 ex/task more can be used)
        - flan (FLAN v2), up to 20M examples available
        - vicuna

    """

    def load_data(dataset_name):
        if dataset_name == "alpaca":
            return load_dataset("tatsu-lab/alpaca")
        elif dataset_name == "alpaca-clean":
            return load_dataset("yahma/alpaca-cleaned")
        elif dataset_name == "chip2":
            return load_dataset("laion/OIG", data_files="unified_chip2.jsonl")
        elif dataset_name == "self-instruct":
            return load_dataset("yizhongw/self_instruct", name="self_instruct")
        elif dataset_name == "hh-rlhf":
            return load_dataset("Anthropic/hh-rlhf")
        elif dataset_name == "longform":
            return load_dataset("akoksal/LongForm")
        elif dataset_name == "oasst1":
            return load_dataset("timdettmers/openassistant-guanaco")
        elif dataset_name == "vicuna":
            raise NotImplementedError("Vicuna data was not released.")
        elif dataset_name == "math":
            return load_dataset("kye/metamath-mistal-tokenized-16384")
        else:
            if os.path.exists(dataset_name):
                try:
                    args.dataset_format = (
                        args.dataset_format if args.dataset_format else "input-output"
                    )
                    full_dataset = local_dataset(dataset_name)
                    return full_dataset
                except:
                    raise ValueError(f"Error loading dataset from {dataset_name}")
            else:
                raise NotImplementedError(
                    f"Dataset {dataset_name} not implemented yet."
                )

    def format_dataset(dataset, dataset_format):
        if (
            dataset_format == "alpaca"
            or dataset_format == "alpaca-clean"
            or (dataset_format is None and args.dataset in ["alpaca", "alpaca-clean"])
        ):
            dataset = dataset.map(
                extract_alpaca_dataset, remove_columns=["instruction"]
            )
        elif dataset_format == "chip2" or (
            dataset_format is None and args.dataset == "chip2"
        ):
            dataset = dataset.map(
                lambda x: {
                    "input": x["text"].split("\n<bot>: ")[0].replace("<human>: ", ""),
                    "output": x["text"].split("\n<bot>: ")[1],
                }
            )
        elif dataset_format == "self-instruct" or (
            dataset_format is None and args.dataset == "self-instruct"
        ):
            for old, new in [["prompt", "input"], ["completion", "output"]]:
                dataset = dataset.rename_column(old, new)
        elif dataset_format == "hh-rlhf" or (
            dataset_format is None and args.dataset == "hh-rlhf"
        ):
            dataset = dataset.map(lambda x: {"input": "", "output": x["chosen"]})
        elif dataset_format == "oasst1" or (
            dataset_format is None and args.dataset == "oasst1"
        ):
            dataset = dataset.map(
                lambda x: {
                    "input": "",
                    "output": x["text"],
                }
            )
        elif dataset_format == "input-output":
            # leave as is
            pass
        # Remove unused columns.
        dataset = dataset.remove_columns(
            [
                col
                for col in dataset.column_names["train"]
                if col not in ["input", "output"]
            ]
        )
        return dataset

    # Load dataset.
    dataset = load_data(args.dataset)
    dataset = format_dataset(dataset, args.dataset_format)

    # Split train/eval, reduce size
    if args.do_eval or args.do_predict:
        if "eval" in dataset:
            eval_dataset = dataset["eval"]
        else:
            print(
                "Splitting train dataset in train and validation according to `eval_dataset_size`"
            )
            dataset = dataset["train"].train_test_split(
                test_size=args.eval_dataset_size, shuffle=True, seed=42
            )
            eval_dataset = dataset["test"]
        if (
            args.max_eval_samples is not None
            and len(eval_dataset) > args.max_eval_samples
        ):
            eval_dataset = eval_dataset.select(range(args.max_eval_samples))
        if args.group_by_length:
            eval_dataset = eval_dataset.map(
                lambda x: {"length": len(x["input"]) + len(x["output"])}
            )
    if args.do_train:
        train_dataset = dataset["train"]
        if (
            args.max_train_samples is not None
            and len(train_dataset) > args.max_train_samples
        ):
            train_dataset = train_dataset.select(range(args.max_train_samples))
        if args.group_by_length:
            train_dataset = train_dataset.map(
                lambda x: {"length": len(x["input"]) + len(x["output"])}
            )

    data_collator = DataCollatorForCausalLM(
        tokenizer=tokenizer,
        source_max_len=args.source_max_len,
        target_max_len=args.target_max_len,
        train_on_source=args.train_on_source,
        predict_with_generate=args.predict_with_generate,
    )
    return dict(
        train_dataset=train_dataset if args.do_train else None,
        eval_dataset=eval_dataset if args.do_eval else None,
        predict_dataset=eval_dataset if args.do_predict else None,
        data_collator=data_collator,
    )


def get_last_checkpoint(checkpoint_dir):
    if isdir(checkpoint_dir):
        is_completed = exists(join(checkpoint_dir, "completed"))
        if is_completed:
            return None, True  # already finished
        max_step = 0
        for filename in os.listdir(checkpoint_dir):
            if isdir(join(checkpoint_dir, filename)) and filename.startswith(
                "checkpoint"
            ):
                max_step = max(max_step, int(filename.replace("checkpoint-", "")))
        if max_step == 0:
            return None, is_completed  # training started, but no checkpoint
        checkpoint_dir = join(checkpoint_dir, f"checkpoint-{max_step}")
        print(f"Found a previous checkpoint at: {checkpoint_dir}")
        return checkpoint_dir, is_completed  # checkpoint found!
    return None, False  # first training


def train():
    hfparser = transformers.HfArgumentParser(
        (ModelArguments, DataArguments, TrainingArguments, GenerationArguments)
    )
    (
        model_args,
        data_args,
        training_args,
        generation_args,
        extra_args,
    ) = hfparser.parse_args_into_dataclasses(return_remaining_strings=True)
    training_args.generation_config = transformers.GenerationConfig(
        **vars(generation_args)
    )
    args = argparse.Namespace(
        **vars(model_args), **vars(data_args), **vars(training_args)
    )
    print(args)

    checkpoint_dir, completed_training = get_last_checkpoint(args.output_dir)
    if completed_training:
        print("Detected that training was already completed!")

    model, tokenizer = get_accelerate_model(args, checkpoint_dir)

    model.config.use_cache = False
    print("loaded model")
    set_seed(args.seed)

    data_module = make_data_module(tokenizer=tokenizer, args=args)

    trainer = Seq2SeqTrainer(
        model=model,
        tokenizer=tokenizer,
        args=training_args,
        **{k: v for k, v in data_module.items() if k != "predict_dataset"},
    )

    # Callbacks
    if not args.full_finetune:
        trainer.add_callback(SavePeftModelCallback)
    if args.do_mmlu_eval:
        if args.mmlu_dataset == "mmlu-zs":
            mmlu_dataset = load_dataset(
                "json",
                data_files={
                    "eval": "data/mmlu/zero_shot_mmlu_val.json",
                    "test": "data/mmlu/zero_shot_mmlu_test.json",
                },
            )
            mmlu_dataset = mmlu_dataset.remove_columns("subject")
        # MMLU Five-shot (Eval/Test only)
        elif args.mmlu_dataset == "mmlu" or args.mmlu_dataset == "mmlu-fs":
            mmlu_dataset = load_dataset(
                "json",
                data_files={
                    "eval": "data/mmlu/five_shot_mmlu_val.json",
                    "test": "data/mmlu/five_shot_mmlu_test.json",
                },
            )
            # mmlu_dataset = mmlu_dataset.remove_columns('subject')
        mmlu_dataset = mmlu_dataset[args.mmlu_split]
        if args.max_mmlu_samples is not None:
            mmlu_dataset = mmlu_dataset.select(range(args.max_mmlu_samples))
        abcd_idx = [
            tokenizer("A", add_special_tokens=False).input_ids[0],
            tokenizer("B", add_special_tokens=False).input_ids[0],
            tokenizer("C", add_special_tokens=False).input_ids[0],
            tokenizer("D", add_special_tokens=False).input_ids[0],
        ]
        accuracy = evaluate.load("accuracy")

        class MMLUEvalCallback(transformers.TrainerCallback):
            def on_evaluate(self, args, state, control, model, **kwargs):
                data_loader = trainer.get_eval_dataloader(mmlu_dataset)
                source_max_len = trainer.data_collator.source_max_len
                trainer.data_collator.source_max_len = args.mmlu_source_max_len
                trainer.model.eval()
                preds, refs = [], []
                loss_mmlu = 0
                for batch in tqdm(data_loader, total=len(data_loader)):
                    (loss, logits, labels) = trainer.prediction_step(
                        trainer.model,
                        batch,
                        prediction_loss_only=False,
                    )
                    # There are two tokens, the output, and eos token.
                    for i, logit in enumerate(logits):
                        label_non_zero_id = (batch["labels"][i] != -100).nonzero()[0][0]
                        logit_abcd = logit[label_non_zero_id - 1][abcd_idx]
                        preds.append(torch.argmax(logit_abcd).item())
                    labels = labels[labels != IGNORE_INDEX].view(-1, 2)[:, 0]
                    refs += [abcd_idx.index(label) for label in labels.tolist()]
                    loss_mmlu += loss.item()
                # Extract results by subject.
                results = {"mmlu_loss": loss_mmlu / len(data_loader)}
                subject = mmlu_dataset["subject"]
                subjects = {s: {"refs": [], "preds": []} for s in set(subject)}
                for s, p, r in zip(subject, preds, refs):
                    subjects[s]["preds"].append(p)
                    subjects[s]["refs"].append(r)
                subject_scores = []
                for subject in subjects:
                    subject_score = accuracy.compute(
                        references=subjects[subject]["refs"],
                        predictions=subjects[subject]["preds"],
                    )["accuracy"]
                    results[
                        f"mmlu_{args.mmlu_split}_accuracy_{subject}"
                    ] = subject_score
                    subject_scores.append(subject_score)
                results[f"mmlu_{args.mmlu_split}_accuracy"] = np.mean(subject_scores)
                trainer.log(results)
                trainer.data_collator.source_max_len = source_max_len

        trainer.add_callback(MMLUEvalCallback)

    # Verifying the datatypes and parameter counts before training.
    print_trainable_parameters(args, model)
    dtypes = {}
    for _, p in model.named_parameters():
        dtype = p.dtype
        if dtype not in dtypes:
            dtypes[dtype] = 0
        dtypes[dtype] += p.numel()
    total = 0
    for k, v in dtypes.items():
        total += v
    for k, v in dtypes.items():
        print(k, v, v / total)

    all_metrics = {"run_name": args.run_name}
    # Training
    if args.do_train:
        logger.info("*** Train ***")
        # Note: `resume_from_checkpoint` not supported for adapter checkpoints by HF.
        # Currently adapter checkpoint is reloaded as expected but optimizer/scheduler states are not.
        train_result = trainer.train()
        metrics = train_result.metrics
        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()
        all_metrics.update(metrics)
    # Evaluation
    if args.do_eval:
        logger.info("*** Evaluate ***")
        metrics = trainer.evaluate(metric_key_prefix="eval")
        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)
        all_metrics.update(metrics)
    # Prediction
    if args.do_predict:
        logger.info("*** Predict ***")
        prediction_output = trainer.predict(
            test_dataset=data_module["predict_dataset"], metric_key_prefix="predict"
        )
        prediction_metrics = prediction_output.metrics
        predictions = prediction_output.predictions
        predictions = np.where(predictions != -100, predictions, tokenizer.pad_token_id)
        predictions = tokenizer.batch_decode(
            predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
        )
        with open(os.path.join(args.output_dir, "predictions.jsonl"), "w") as fout:
            for i, example in enumerate(data_module["predict_dataset"]):
                example["prediction_with_input"] = predictions[i].strip()
                example["prediction"] = (
                    predictions[i].replace(example["input"], "").strip()
                )
                fout.write(json.dumps(example) + "\n")
        print(prediction_metrics)
        trainer.log_metrics("predict", prediction_metrics)
        trainer.save_metrics("predict", prediction_metrics)
        all_metrics.update(prediction_metrics)

    if args.do_train or args.do_eval or args.do_predict:
        with open(os.path.join(args.output_dir, "metrics.json"), "w") as fout:
            fout.write(json.dumps(all_metrics))


if __name__ == "__main__":
    train()

quasar/train.py
kyegomez/Quasar
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset
from trl import SFTTrainer
from peft import LoraConfig


model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-v0.1"
)

dataset = load_dataset("kye/metamath-mistal-tokenized-16384")


peft_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

trainer = SFTTrainer(
    model=model,
    trainer_dataset=dataset,
    max_seq_length=16384,
    peft_config=peft_config,
)

trainer.train()

simple.py
kyegomez/Quasar