File: web_demo2.py

from transformers import AutoModel, AutoTokenizer
import streamlit as st
from streamlit_chat import message


st.set_page_config(
    page_title="ChatGLM-6b 演示",
    page_icon=":robot:"
)


@st.cache_resource
def get_model():
    tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
    model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
    model = model.eval()
    return tokenizer, model


MAX_TURNS = 20
MAX_BOXES = MAX_TURNS * 2


def predict(input, max_length, top_p, temperature, history=None):
    tokenizer, model = get_model()
    if history is None:
        history = []

    with container:
        if len(history) > 0:
            if len(history)>MAX_BOXES:
                history = history[-MAX_TURNS:]
            for i, (query, response) in enumerate(history):
                message(query, avatar_style="big-smile", key=str(i) + "_user")
                message(response, avatar_style="bottts", key=str(i))

        message(input, avatar_style="big-smile", key=str(len(history)) + "_user")
        st.write("AI正在回复:")
        with st.empty():
            for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,
                                               temperature=temperature):
                query, response = history[-1]
                st.write(response)

    return history


container = st.container()

# create a prompt text for the text generation
prompt_text = st.text_area(label="用户命令输入",
            height = 100,
            placeholder="请在这儿输入您的命令")

max_length = st.sidebar.slider(
    'max_length', 0, 4096, 2048, step=1
)
top_p = st.sidebar.slider(
    'top_p', 0.0, 1.0, 0.6, step=0.01
)
temperature = st.sidebar.slider(
    'temperature', 0.0, 1.0, 0.95, step=0.01
)

if 'state' not in st.session_state:
    st.session_state['state'] = []

if st.button("发送", key="predict"):
    with st.spinner("AI正在思考，请稍等........"):
        # text generation
        st.session_state["state"] = predict(prompt_text, max_length, top_p, temperature, st.session_state["state"])



File: web_demo_old.py

from transformers import AutoModel, AutoTokenizer
import gradio as gr

tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()

MAX_TURNS = 20
MAX_BOXES = MAX_TURNS * 2


def predict(input, max_length, top_p, temperature, history=None):
    if history is None:
        history = []
    for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,
                                               temperature=temperature):
        updates = []
        for query, response in history:
            updates.append(gr.update(visible=True, value="用户：" + query))
            updates.append(gr.update(visible=True, value="ChatGLM-6B：" + response))
        if len(updates) < MAX_BOXES:
            updates = updates + [gr.Textbox.update(visible=False)] * (MAX_BOXES - len(updates))
        yield [history] + updates


with gr.Blocks() as demo:
    state = gr.State([])
    text_boxes = []
    for i in range(MAX_BOXES):
        if i % 2 == 0:
            text_boxes.append(gr.Markdown(visible=False, label="提问："))
        else:
            text_boxes.append(gr.Markdown(visible=False, label="回复："))

    with gr.Row():
        with gr.Column(scale=4):
            txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter", lines=11).style(
                container=False)
        with gr.Column(scale=1):
            max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
            top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
            temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)
            button = gr.Button("Generate")
    button.click(predict, [txt, max_length, top_p, temperature, state], [state] + text_boxes)
demo.queue().launch(share=False, inbrowser=True)



File: web_demo_vision.py

from transformers import AutoModel, AutoTokenizer
import gradio as gr
import mdtex2html

tokenizer = AutoTokenizer.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()

"""Override Chatbot.postprocess"""


def postprocess(self, y):
    if y is None:
        return []
    for i, (message, response) in enumerate(y):
        y[i] = (
            None if message is None else mdtex2html.convert((message)),
            None if response is None else mdtex2html.convert(response),
        )
    return y


gr.Chatbot.postprocess = postprocess


def parse_text(text):
    """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
    lines = text.split("\n")
    lines = [line for line in lines if line != ""]
    count = 0
    for i, line in enumerate(lines):
        if "```" in line:
            count += 1
            items = line.split('`')
            if count % 2 == 1:
                lines[i] = f'<pre><code class="language-{items[-1]}">'
            else:
                lines[i] = f'<br></code></pre>'
        else:
            if i > 0:
                if count % 2 == 1:
                    line = line.replace("`", "\`")
                    line = line.replace("<", "&lt;")
                    line = line.replace(">", "&gt;")
                    line = line.replace(" ", "&nbsp;")
                    line = line.replace("*", "&ast;")
                    line = line.replace("_", "&lowbar;")
                    line = line.replace("-", "&#45;")
                    line = line.replace(".", "&#46;")
                    line = line.replace("!", "&#33;")
                    line = line.replace("(", "&#40;")
                    line = line.replace(")", "&#41;")
                    line = line.replace("$", "&#36;")
                lines[i] = "<br>"+line
    text = "".join(lines)
    return text


def predict(input, image_path, chatbot, max_length, top_p, temperature, history):
    if image_path is None:
        return [(input, "图片为空！请重新上传图片并重试。")]
    chatbot.append((parse_text(input), ""))
    for response, history in model.stream_chat(tokenizer, image_path, input, history, max_length=max_length, top_p=top_p,
                                               temperature=temperature):
        chatbot[-1] = (parse_text(input), parse_text(response))

        yield chatbot, history


def predict_new_image(image_path, chatbot, max_length, top_p, temperature):
    input, history = "描述这张图片。", []
    chatbot.append((parse_text(input), ""))
    for response, history in model.stream_chat(tokenizer, image_path, input, history, max_length=max_length,
                                               top_p=top_p,
                                               temperature=temperature):
        chatbot[-1] = (parse_text(input), parse_text(response))

        yield chatbot, history


def reset_user_input():
    return gr.update(value='')


def reset_state():
    return None, [], []


with gr.Blocks() as demo:
    gr.HTML("""<h1 align="center">VisualGLM</h1>""")

    image_path = gr.Image(type="filepath", label="Image Prompt", value=None)
    chatbot = gr.Chatbot()
    with gr.Row():
        with gr.Column(scale=4):
            with gr.Column(scale=12):
                user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(
                    container=False)
            with gr.Column(min_width=32, scale=1):
                submitBtn = gr.Button("Submit", variant="primary")
        with gr.Column(scale=1):
            emptyBtn = gr.Button("Clear History")
            max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
            top_p = gr.Slider(0, 1, value=0.4, step=0.01, label="Top P", interactive=True)
            temperature = gr.Slider(0, 1, value=0.8, step=0.01, label="Temperature", interactive=True)

    history = gr.State([])

    submitBtn.click(predict, [user_input, image_path, chatbot, max_length, top_p, temperature, history], [chatbot, history],
                    show_progress=True)

    image_path.upload(predict_new_image, [image_path, chatbot, max_length, top_p, temperature], [chatbot, history],
                      show_progress=True)
    image_path.clear(reset_state, outputs=[image_path, chatbot, history], show_progress=True)

    submitBtn.click(reset_user_input, [], [user_input])

    emptyBtn.click(reset_state, outputs=[image_path, chatbot, history], show_progress=True)

demo.queue().launch(share=False, inbrowser=True)



File: cli_demo_vision.py

import os
import platform
import signal
import sys

from transformers import AutoTokenizer, AutoModel
import readline

tokenizer = AutoTokenizer.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()

os_name = platform.system()
clear_command = 'cls' if os_name == 'Windows' else 'clear'
stop_stream = False


def build_prompt(history, prefix):
    prompt = prefix
    for query, response in history:
        prompt += f"\n\n用户：{query}"
        prompt += f"\n\nChatGLM-6B：{response}"
    return prompt


def signal_handler(signal, frame):
    global stop_stream
    stop_stream = True


def main():
    global stop_stream
    while True:
        history = []
        prefix = "欢迎使用 VisualGLM-6B 模型，输入图片路径和内容即可进行对话，clear 清空对话历史，stop 终止程序"
        print(prefix)
        image_path = input("\n请输入图片路径：")
        if image_path == "stop":
            break
        prefix = prefix + "\n" + image_path
        query = "描述这张图片。"
        while True:
            count = 0
            for response, history in model.stream_chat(tokenizer, image_path, query, history=history):
                if stop_stream:
                    stop_stream = False
                    break
                else:
                    count += 1
                    if count % 8 == 0:
                        os.system(clear_command)
                        print(build_prompt(history, prefix), flush=True)
                        signal.signal(signal.SIGINT, signal_handler)
            os.system(clear_command)
            print(build_prompt(history, prefix), flush=True)
            query = input("\n用户：")
            if query.strip() == "clear":
                break
            if query.strip() == "stop":
                sys.exit(0)


if __name__ == "__main__":
    main()



File: cli_demo.py

import os
import platform
import signal
from transformers import AutoTokenizer, AutoModel
import readline

tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()

os_name = platform.system()
clear_command = 'cls' if os_name == 'Windows' else 'clear'
stop_stream = False


def build_prompt(history):
    prompt = "欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序"
    for query, response in history:
        prompt += f"\n\n用户：{query}"
        prompt += f"\n\nChatGLM-6B：{response}"
    return prompt


def signal_handler(signal, frame):
    global stop_stream
    stop_stream = True


def main():
    history = []
    global stop_stream
    print("欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
    while True:
        query = input("\n用户：")
        if query.strip() == "stop":
            break
        if query.strip() == "clear":
            history = []
            os.system(clear_command)
            print("欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
            continue
        count = 0
        for response, history in model.stream_chat(tokenizer, query, history=history):
            if stop_stream:
                stop_stream = False
                break
            else:
                count += 1
                if count % 8 == 0:
                    os.system(clear_command)
                    print(build_prompt(history), flush=True)
                    signal.signal(signal.SIGINT, signal_handler)
        os.system(clear_command)
        print(build_prompt(history), flush=True)


if __name__ == "__main__":
    main()



File: api.py

from fastapi import FastAPI, Request
from transformers import AutoTokenizer, AutoModel
import uvicorn, json, datetime
import torch

DEVICE = "cuda"
DEVICE_ID = "0"
CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE


def torch_gc():
    if torch.cuda.is_available():
        with torch.cuda.device(CUDA_DEVICE):
            torch.cuda.empty_cache()
            torch.cuda.ipc_collect()


app = FastAPI()


@app.post("/")
async def create_item(request: Request):
    global model, tokenizer
    json_post_raw = await request.json()
    json_post = json.dumps(json_post_raw)
    json_post_list = json.loads(json_post)
    prompt = json_post_list.get('prompt')
    history = json_post_list.get('history')
    max_length = json_post_list.get('max_length')
    top_p = json_post_list.get('top_p')
    temperature = json_post_list.get('temperature')
    response, history = model.chat(tokenizer,
                                   prompt,
                                   history=history,
                                   max_length=max_length if max_length else 2048,
                                   top_p=top_p if top_p else 0.7,
                                   temperature=temperature if temperature else 0.95)
    now = datetime.datetime.now()
    time = now.strftime("%Y-%m-%d %H:%M:%S")
    answer = {
        "response": response,
        "history": history,
        "status": 200,
        "time": time
    }
    log = "[" + time + "] " + '", prompt:"' + prompt + '", response:"' + repr(response) + '"'
    print(log)
    torch_gc()
    return answer


if __name__ == '__main__':
    tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
    model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
    model.eval()
    uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)



File: utils.py

import os
from typing import Dict, Tuple, Union, Optional

from torch.nn import Module
from transformers import AutoModel


def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
    # transformer.word_embeddings 占用1层
    # transformer.final_layernorm 和 lm_head 占用1层
    # transformer.layers 占用 28 层
    # 总共30层分配到num_gpus张卡上
    num_trans_layers = 28
    per_gpu_layers = 30 / num_gpus

    # bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
    # windows下 model.device 会被设置成 transformer.word_embeddings.device
    # linux下 model.device 会被设置成 lm_head.device
    # 在调用chat或者stream_chat时,input_ids会被放到model.device上
    # 如果transformer.word_embeddings.device和model.device不同,则会导致RuntimeError
    # 因此这里将transformer.word_embeddings,transformer.final_layernorm,lm_head都放到第一张卡上
    device_map = {'transformer.word_embeddings': 0,
                  'transformer.final_layernorm': 0, 'lm_head': 0}

    used = 2
    gpu_target = 0
    for i in range(num_trans_layers):
        if used >= per_gpu_layers:
            gpu_target += 1
            used = 0
        assert gpu_target < num_gpus
        device_map[f'transformer.layers.{i}'] = gpu_target
        used += 1

    return device_map


def load_model_on_gpus(checkpoint_path: Union[str, os.PathLike], num_gpus: int = 2,
                       device_map: Optional[Dict[str, int]] = None, **kwargs) -> Module:
    if num_gpus < 2 and device_map is None:
        model = AutoModel.from_pretrained(checkpoint_path, trust_remote_code=True, **kwargs).half().cuda()
    else:
        from accelerate import dispatch_model

        model = AutoModel.from_pretrained(checkpoint_path, trust_remote_code=True, **kwargs).half()

        if device_map is None:
            device_map = auto_configure_device_map(num_gpus)

        model = dispatch_model(model, device_map=device_map)

    return model





File: web_demo.py

from transformers import AutoModel, AutoTokenizer
import gradio as gr
import mdtex2html

tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()

"""Override Chatbot.postprocess"""


def postprocess(self, y):
    if y is None:
        return []
    for i, (message, response) in enumerate(y):
        y[i] = (
            None if message is None else mdtex2html.convert((message)),
            None if response is None else mdtex2html.convert(response),
        )
    return y


gr.Chatbot.postprocess = postprocess


def parse_text(text):
    """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
    lines = text.split("\n")
    lines = [line for line in lines if line != ""]
    count = 0
    for i, line in enumerate(lines):
        if "```" in line:
            count += 1
            items = line.split('`')
            if count % 2 == 1:
                lines[i] = f'<pre><code class="language-{items[-1]}">'
            else:
                lines[i] = f'<br></code></pre>'
        else:
            if i > 0:
                if count % 2 == 1:
                    line = line.replace("`", "\`")
                    line = line.replace("<", "&lt;")
                    line = line.replace(">", "&gt;")
                    line = line.replace(" ", "&nbsp;")
                    line = line.replace("*", "&ast;")
                    line = line.replace("_", "&lowbar;")
                    line = line.replace("-", "&#45;")
                    line = line.replace(".", "&#46;")
                    line = line.replace("!", "&#33;")
                    line = line.replace("(", "&#40;")
                    line = line.replace(")", "&#41;")
                    line = line.replace("$", "&#36;")
                lines[i] = "<br>"+line
    text = "".join(lines)
    return text


def predict(input, chatbot, max_length, top_p, temperature, history):
    chatbot.append((parse_text(input), ""))
    for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,
                                               temperature=temperature):
        chatbot[-1] = (parse_text(input), parse_text(response))       

        yield chatbot, history


def reset_user_input():
    return gr.update(value='')


def reset_state():
    return [], []


with gr.Blocks() as demo:
    gr.HTML("""<h1 align="center">ChatGLM</h1>""")

    chatbot = gr.Chatbot()
    with gr.Row():
        with gr.Column(scale=4):
            with gr.Column(scale=12):
                user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(
                    container=False)
            with gr.Column(min_width=32, scale=1):
                submitBtn = gr.Button("Submit", variant="primary")
        with gr.Column(scale=1):
            emptyBtn = gr.Button("Clear History")
            max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
            top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
            temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)

    history = gr.State([])

    submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history],
                    show_progress=True)
    submitBtn.click(reset_user_input, [], [user_input])

    emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)

demo.queue().launch(share=False, inbrowser=True)



File: ptuning/trainer_seq2seq.py

# Copyright 2020 The HuggingFace 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.

from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from transformers.deepspeed import is_deepspeed_zero3_enabled
from trainer import Trainer
from transformers.trainer_utils import PredictionOutput
from transformers.utils import logging


logger = logging.get_logger(__name__)


class Seq2SeqTrainer(Trainer):
    def evaluate(
        self,
        eval_dataset: Optional[Dataset] = None,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "eval",
        **gen_kwargs
    ) -> Dict[str, float]:
        """
        Run evaluation and returns metrics.

        The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
        (pass it to the init `compute_metrics` argument).

        You can also subclass and override this method to inject custom behavior.

        Args:
            eval_dataset (`Dataset`, *optional*):
                Pass a dataset if you wish to override `self.eval_dataset`. If it is an [`~datasets.Dataset`], columns
                not accepted by the `model.forward()` method are automatically removed. It must implement the `__len__`
                method.
            ignore_keys (`List[str]`, *optional*):
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions.
            metric_key_prefix (`str`, *optional*, defaults to `"eval"`):
                An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
                "eval_bleu" if the prefix is `"eval"` (default)
            max_length (`int`, *optional*):
                The maximum target length to use when predicting with the generate method.
            num_beams (`int`, *optional*):
                Number of beams for beam search that will be used when predicting with the generate method. 1 means no
                beam search.
            gen_kwargs:
                Additional `generate` specific kwargs.

        Returns:
            A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The
            dictionary also contains the epoch number which comes from the training state.
        """

        gen_kwargs = gen_kwargs.copy()
        if gen_kwargs.get("max_length") is None and gen_kwargs.get("max_new_tokens") is None:
            gen_kwargs["max_length"] = self.args.generation_max_length
        gen_kwargs["num_beams"] = (
            gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.args.generation_num_beams
        )
        self._gen_kwargs = gen_kwargs

        return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)

    def predict(
        self,
        test_dataset: Dataset,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "test",
        **gen_kwargs
    ) -> PredictionOutput:
        """
        Run prediction and returns predictions and potential metrics.

        Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
        will also return metrics, like in `evaluate()`.

        Args:
            test_dataset (`Dataset`):
                Dataset to run the predictions on. If it is a [`~datasets.Dataset`], columns not accepted by the
                `model.forward()` method are automatically removed. Has to implement the method `__len__`
            ignore_keys (`List[str]`, *optional*):
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions.
            metric_key_prefix (`str`, *optional*, defaults to `"eval"`):
                An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
                "eval_bleu" if the prefix is `"eval"` (default)
            max_length (`int`, *optional*):
                The maximum target length to use when predicting with the generate method.
            num_beams (`int`, *optional*):
                Number of beams for beam search that will be used when predicting with the generate method. 1 means no
                beam search.
            gen_kwargs:
                Additional `generate` specific kwargs.

        <Tip>

        If your predictions or labels have different sequence lengths (for instance because you're doing dynamic
        padding in a token classification task) the predictions will be padded (on the right) to allow for
        concatenation into one array. The padding index is -100.

        </Tip>

        Returns: *NamedTuple* A namedtuple with the following keys:

            - predictions (`np.ndarray`): The predictions on `test_dataset`.
            - label_ids (`np.ndarray`, *optional*): The labels (if the dataset contained some).
            - metrics (`Dict[str, float]`, *optional*): The potential dictionary of metrics (if the dataset contained
              labels).
        """

        gen_kwargs = gen_kwargs.copy()
        if gen_kwargs.get("max_length") is None and gen_kwargs.get("max_new_tokens") is None:
            gen_kwargs["max_length"] = self.args.generation_max_length
        gen_kwargs["num_beams"] = (
            gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.args.generation_num_beams
        )
        self._gen_kwargs = gen_kwargs


        return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)

    def prediction_step(
        self,
        model: nn.Module,
        inputs: Dict[str, Union[torch.Tensor, Any]],
        prediction_loss_only: bool,
        ignore_keys: Optional[List[str]] = None,
    ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
        """
        Perform an evaluation step on `model` using `inputs`.

        Subclass and override to inject custom behavior.

        Args:
            model (`nn.Module`):
                The model to evaluate.
            inputs (`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument `labels`. Check your model's documentation for all accepted arguments.
            prediction_loss_only (`bool`):
                Whether or not to return the loss only.

        Return:
            Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, logits and
            labels (each being optional).
        """

        if not self.args.predict_with_generate or prediction_loss_only:
            return super().prediction_step(
                model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
            )

        has_labels = "labels" in inputs
        inputs = self._prepare_inputs(inputs)

        # XXX: adapt synced_gpus for fairscale as well
        gen_kwargs = self._gen_kwargs.copy()
        if gen_kwargs.get("max_length") is None and gen_kwargs.get("max_new_tokens") is None:
            gen_kwargs["max_length"] = self.model.config.max_length
        gen_kwargs["num_beams"] = (
            gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.model.config.num_beams
        )
        default_synced_gpus = True if is_deepspeed_zero3_enabled() else False
        gen_kwargs["synced_gpus"] = (
            gen_kwargs["synced_gpus"] if gen_kwargs.get("synced_gpus") is not None else default_synced_gpus
        )

        if "attention_mask" in inputs:
            gen_kwargs["attention_mask"] = inputs.get("attention_mask", None)
        if "position_ids" in inputs:
            gen_kwargs["position_ids"] = inputs.get("position_ids", None)
        if "global_attention_mask" in inputs:
            gen_kwargs["global_attention_mask"] = inputs.get("global_attention_mask", None)

        # prepare generation inputs
        # some encoder-decoder models can have varying encoder's and thus
        # varying model input names
        if hasattr(self.model, "encoder") and self.model.encoder.main_input_name != self.model.main_input_name:
            generation_inputs = inputs[self.model.encoder.main_input_name]
        else:
            generation_inputs = inputs[self.model.main_input_name]

        gen_kwargs["input_ids"] = generation_inputs
        generated_tokens = self.model.generate(**gen_kwargs)
        generated_tokens = generated_tokens[:, generation_inputs.size()[-1]:]

        # in case the batch is shorter than max length, the output should be padded
        if gen_kwargs.get("max_length") is not None and generated_tokens.shape[-1] < gen_kwargs["max_length"]:
            generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_length"])
        elif gen_kwargs.get("max_new_tokens") is not None and generated_tokens.shape[-1] < (
            gen_kwargs["max_new_tokens"] + 1
        ):
            generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_new_tokens"] + 1)

        loss = None

        if self.args.prediction_loss_only:
            return (loss, None, None)

        if has_labels:
            labels = inputs["labels"]
            if gen_kwargs.get("max_length") is not None and labels.shape[-1] < gen_kwargs["max_length"]:
                labels = self._pad_tensors_to_max_len(labels, gen_kwargs["max_length"])
            elif gen_kwargs.get("max_new_tokens") is not None and labels.shape[-1] < (
                gen_kwargs["max_new_tokens"] + 1
            ):
                labels = self._pad_tensors_to_max_len(labels, (gen_kwargs["max_new_tokens"] + 1))
        else:
            labels = None

        return (loss, generated_tokens, labels)

    def _pad_tensors_to_max_len(self, tensor, max_length):
        if self.tokenizer is not None and hasattr(self.tokenizer, "pad_token_id"):
            # If PAD token is not defined at least EOS token has to be defined
            pad_token_id = (
                self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
            )
        else:
            if self.model.config.pad_token_id is not None:
                pad_token_id = self.model.config.pad_token_id
            else:
                raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors")

        padded_tensor = pad_token_id * torch.ones(
            (tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device
        )
        padded_tensor[:, : tensor.shape[-1]] = tensor
        return padded_tensor



File: ptuning/arguments.py

from dataclasses import dataclass, field
from typing import Optional


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """

    model_name_or_path: str = field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
    )
    ptuning_checkpoint: str = field(
        default=None, metadata={"help": "Path to p-tuning v2 checkpoints"}
    )
    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 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)."},
    )
    use_auth_token: bool = field(
        default=False,
        metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        },
    )
    resize_position_embeddings: Optional[bool] = field(
        default=None,
        metadata={
            "help": (
                "Whether to automatically resize the position embeddings if `max_source_length` exceeds "
                "the model's position embeddings."
            )
        },
    )
    quantization_bit: Optional[int] = field(
        default=None
    )
    pre_seq_len: Optional[int] = field(
        default=None
    )
    prefix_projection: bool = field(
        default=False
    )


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    lang: Optional[str] = field(default=None, metadata={"help": "Language id for summarization."})

    dataset_name: Optional[str] = field(
        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
    )
    dataset_config_name: Optional[str] = field(
        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
    )
    prompt_column: Optional[str] = field(
        default=None,
        metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
    )
    response_column: Optional[str] = field(
        default=None,
        metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."},
    )
    history_column: Optional[str] = field(
        default=None,
        metadata={"help": "The name of the column in the datasets containing the history of chat."},
    )
    train_file: Optional[str] = field(
        default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."}
    )
    validation_file: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "An optional input evaluation data file to evaluate the metrics (rouge) on (a jsonlines or csv file)."
            )
        },
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={
            "help": "An optional input test data file to evaluate the metrics (rouge) on (a jsonlines or csv file)."
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    max_source_length: Optional[int] = field(
        default=1024,
        metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    max_target_length: Optional[int] = field(
        default=128,
        metadata={
            "help": (
                "The maximum total sequence length for target text after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    val_max_target_length: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "The maximum total sequence length for validation target text after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
                "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
                "during ``evaluate`` and ``predict``."
            )
        },
    )
    pad_to_max_length: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether to pad all samples to model maximum sentence length. "
                "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
                "efficient on GPU but very bad for TPU."
            )
        },
    )
    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."
            )
        },
    )
    max_predict_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of prediction examples to this "
                "value if set."
            )
        },
    )
    num_beams: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
                "which is used during ``evaluate`` and ``predict``."
            )
        },
    )
    ignore_pad_token_for_loss: bool = field(
        default=True,
        metadata={
            "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
        },
    )
    source_prefix: Optional[str] = field(
        default="", metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
    )

    forced_bos_token: Optional[str] = field(
        default=None,
        metadata={
            "help": (
                "The token to force as the first generated token after the decoder_start_token_id."
                "Useful for multilingual models like mBART where the first generated token"
                "needs to be the target language token (Usually it is the target language token)"
            )
        },
    )

    

    def __post_init__(self):
        if self.dataset_name is None and self.train_file is None and self.validation_file is None and self.test_file is None:
            raise ValueError("Need either a dataset name or a training/validation/test file.")
        else:
            if self.train_file is not None:
                extension = self.train_file.split(".")[-1]
                assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
            if self.validation_file is not None:
                extension = self.validation_file.split(".")[-1]
                assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
        if self.val_max_target_length is None:
            self.val_max_target_length = self.max_target_length




File: ptuning/trainer.py

# coding=utf-8
# Copyright 2020-present the HuggingFace Inc. team.
#
# 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.
"""
The Trainer class, to easily train a 🤗 Transformers from scratch or finetune it on a new task.
"""

import contextlib
import functools
import glob
import inspect
import math
import os
import random
import re
import shutil
import sys
import time
import warnings
from collections.abc import Mapping
from distutils.util import strtobool
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

from tqdm.auto import tqdm


# Integrations must be imported before ML frameworks:
# isort: off
from transformers.integrations import (
    default_hp_search_backend,
    get_reporting_integration_callbacks,
    hp_params,
    is_fairscale_available,
    is_optuna_available,
    is_ray_tune_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)

# isort: on

import numpy as np
import torch
import torch.distributed as dist
from huggingface_hub import Repository, create_repo
from packaging import version
from torch import nn
from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler

from transformers import __version__
from transformers.configuration_utils import PretrainedConfig
from transformers.data.data_collator import DataCollator, DataCollatorWithPadding, default_data_collator
from transformers.debug_utils import DebugOption, DebugUnderflowOverflow
from transformers.deepspeed import deepspeed_init, is_deepspeed_zero3_enabled
from transformers.dependency_versions_check import dep_version_check
from transformers.modelcard import TrainingSummary
from transformers.modeling_utils import PreTrainedModel, load_sharded_checkpoint, unwrap_model
from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.optimization import Adafactor, get_scheduler
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_10, is_torch_less_than_1_11
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_callback import (
    CallbackHandler,
    DefaultFlowCallback,
    PrinterCallback,
    ProgressCallback,
    TrainerCallback,
    TrainerControl,
    TrainerState,
)
from transformers.trainer_pt_utils import (
    DistributedLengthGroupedSampler,
    DistributedSamplerWithLoop,
    DistributedTensorGatherer,
    IterableDatasetShard,
    LabelSmoother,
    LengthGroupedSampler,
    SequentialDistributedSampler,
    ShardSampler,
    distributed_broadcast_scalars,
    distributed_concat,
    find_batch_size,
    get_module_class_from_name,
    get_parameter_names,
    nested_concat,
    nested_detach,
    nested_numpify,
    nested_truncate,
    nested_xla_mesh_reduce,
    reissue_pt_warnings,
)
from transformers.trainer_utils import (
    PREFIX_CHECKPOINT_DIR,
    BestRun,
    EvalLoopOutput,
    EvalPrediction,
    FSDPOption,
    HPSearchBackend,
    HubStrategy,
    IntervalStrategy,
    PredictionOutput,
    RemoveColumnsCollator,
    ShardedDDPOption,
    TrainerMemoryTracker,
    TrainOutput,
    default_compute_objective,
    default_hp_space,
    denumpify_detensorize,
    enable_full_determinism,
    find_executable_batch_size,
    get_last_checkpoint,
    has_length,
    number_of_arguments,
    seed_worker,
    set_seed,
    speed_metrics,
)
from transformers.training_args import OptimizerNames, ParallelMode, TrainingArguments
from transformers.utils import (
    CONFIG_NAME,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    can_return_loss,
    find_labels,
    get_full_repo_name,
    is_accelerate_available,
    is_apex_available,
    is_datasets_available,
    is_in_notebook,
    is_ipex_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_torch_compile_available,
    is_torch_neuroncore_available,
    is_torch_tpu_available,
    logging,
)
from transformers.utils.generic import ContextManagers


_is_native_cpu_amp_available = is_torch_greater_or_equal_than_1_10

DEFAULT_CALLBACKS = [DefaultFlowCallback]
DEFAULT_PROGRESS_CALLBACK = ProgressCallback

if is_in_notebook():
    from transformers.utils.notebook import NotebookProgressCallback

    DEFAULT_PROGRESS_CALLBACK = NotebookProgressCallback

if is_apex_available():
    from apex import amp

if is_datasets_available():
    import datasets

if is_torch_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm
    import torch_xla.debug.metrics as met
    import torch_xla.distributed.parallel_loader as pl

if is_fairscale_available():
    dep_version_check("fairscale")
    import fairscale
    from fairscale.nn.data_parallel import FullyShardedDataParallel as FullyShardedDDP
    from fairscale.nn.data_parallel import ShardedDataParallel as ShardedDDP
    from fairscale.nn.wrap import auto_wrap
    from fairscale.optim import OSS
    from fairscale.optim.grad_scaler import ShardedGradScaler


if is_sagemaker_mp_enabled():
    import smdistributed.modelparallel.torch as smp
    from smdistributed.modelparallel import __version__ as SMP_VERSION

    IS_SAGEMAKER_MP_POST_1_10 = version.parse(SMP_VERSION) >= version.parse("1.10")

    from transformers.trainer_pt_utils import smp_forward_backward, smp_forward_only, smp_gather, smp_nested_concat
else:
    IS_SAGEMAKER_MP_POST_1_10 = False


skip_first_batches = None
if is_accelerate_available():
    from accelerate import __version__ as accelerate_version

    if version.parse(accelerate_version) >= version.parse("0.16"):
        from accelerate import skip_first_batches


if TYPE_CHECKING:
    import optuna

logger = logging.get_logger(__name__)


# Name of the files used for checkpointing
TRAINING_ARGS_NAME = "training_args.bin"
TRAINER_STATE_NAME = "trainer_state.json"
OPTIMIZER_NAME = "optimizer.pt"
SCHEDULER_NAME = "scheduler.pt"
SCALER_NAME = "scaler.pt"


class Trainer:
    """
    Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.

    Args:
        model ([`PreTrainedModel`] or `torch.nn.Module`, *optional*):
            The model to train, evaluate or use for predictions. If not provided, a `model_init` must be passed.

            <Tip>

            [`Trainer`] is optimized to work with the [`PreTrainedModel`] provided by the library. You can still use
            your own models defined as `torch.nn.Module` as long as they work the same way as the 🤗 Transformers
            models.

            </Tip>

        args ([`TrainingArguments`], *optional*):
            The arguments to tweak for training. Will default to a basic instance of [`TrainingArguments`] with the
            `output_dir` set to a directory named *tmp_trainer* in the current directory if not provided.
        data_collator (`DataCollator`, *optional*):
            The function to use to form a batch from a list of elements of `train_dataset` or `eval_dataset`. Will
            default to [`default_data_collator`] if no `tokenizer` is provided, an instance of
            [`DataCollatorWithPadding`] otherwise.
        train_dataset (`torch.utils.data.Dataset` or `torch.utils.data.IterableDataset`, *optional*):
            The dataset to use for training. If it is a [`~datasets.Dataset`], columns not accepted by the
            `model.forward()` method are automatically removed.

            Note that if it's a `torch.utils.data.IterableDataset` with some randomization and you are training in a
            distributed fashion, your iterable dataset should either use a internal attribute `generator` that is a
            `torch.Generator` for the randomization that must be identical on all processes (and the Trainer will
            manually set the seed of this `generator` at each epoch) or have a `set_epoch()` method that internally
            sets the seed of the RNGs used.
        eval_dataset (Union[`torch.utils.data.Dataset`, Dict[str, `torch.utils.data.Dataset`]), *optional*):
             The dataset to use for evaluation. If it is a [`~datasets.Dataset`], columns not accepted by the
             `model.forward()` method are automatically removed. If it is a dictionary, it will evaluate on each
             dataset prepending the dictionary key to the metric name.
        tokenizer ([`PreTrainedTokenizerBase`], *optional*):
            The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs to the
            maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an
            interrupted training or reuse the fine-tuned model.
        model_init (`Callable[[], PreTrainedModel]`, *optional*):
            A function that instantiates the model to be used. If provided, each call to [`~Trainer.train`] will start
            from a new instance of the model as given by this function.

            The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to
            be able to choose different architectures according to hyper parameters (such as layer count, sizes of
            inner layers, dropout probabilities etc).
        compute_metrics (`Callable[[EvalPrediction], Dict]`, *optional*):
            The function that will be used to compute metrics at evaluation. Must take a [`EvalPrediction`] and return
            a dictionary string to metric values.
        callbacks (List of [`TrainerCallback`], *optional*):
            A list of callbacks to customize the training loop. Will add those to the list of default callbacks
            detailed in [here](callback).

            If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method.
        optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*): A tuple
            containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your model
            and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`.
        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*):
            A function that preprocess the logits right before caching them at each evaluation step. Must take two
            tensors, the logits and the labels, and return the logits once processed as desired. The modifications made
            by this function will be reflected in the predictions received by `compute_metrics`.

            Note that the labels (second parameter) will be `None` if the dataset does not have them.

    Important attributes:

        - **model** -- Always points to the core model. If using a transformers model, it will be a [`PreTrainedModel`]
          subclass.
        - **model_wrapped** -- Always points to the most external model in case one or more other modules wrap the
          original model. This is the model that should be used for the forward pass. For example, under `DeepSpeed`,
          the inner model is wrapped in `DeepSpeed` and then again in `torch.nn.DistributedDataParallel`. If the inner
          model hasn't been wrapped, then `self.model_wrapped` is the same as `self.model`.
        - **is_model_parallel** -- Whether or not a model has been switched to a model parallel mode (different from
          data parallelism, this means some of the model layers are split on different GPUs).
        - **place_model_on_device** -- Whether or not to automatically place the model on the device - it will be set
          to `False` if model parallel or deepspeed is used, or if the default
          `TrainingArguments.place_model_on_device` is overridden to return `False` .
        - **is_in_train** -- Whether or not a model is currently running `train` (e.g. when `evaluate` is called while
          in `train`)

    """

    from transformers.trainer_pt_utils import _get_learning_rate, log_metrics, metrics_format, save_metrics, save_state

    def __init__(
        self,
        model: Union[PreTrainedModel, nn.Module] = None,
        args: TrainingArguments = None,
        data_collator: Optional[DataCollator] = None,
        train_dataset: Optional[Dataset] = None,
        eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,
        tokenizer: Optional[PreTrainedTokenizerBase] = None,
        model_init: Optional[Callable[[], PreTrainedModel]] = None,
        compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
        callbacks: Optional[List[TrainerCallback]] = None,
        optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
        save_prefixencoder: bool = False,
    ):
        self.save_prefixencoder = save_prefixencoder
        if args is None:
            output_dir = "tmp_trainer"
            logger.info(f"No `TrainingArguments` passed, using `output_dir={output_dir}`.")
            args = TrainingArguments(output_dir=output_dir)
        self.args = args
        # Seed must be set before instantiating the model when using model
        enable_full_determinism(self.args.seed) if self.args.full_determinism else set_seed(self.args.seed)
        self.hp_name = None
        self.deepspeed = None
        self.is_in_train = False

        # memory metrics - must set up as early as possible
        self._memory_tracker = TrainerMemoryTracker(self.args.skip_memory_metrics)
        self._memory_tracker.start()

        # set the correct log level depending on the node
        log_level = args.get_process_log_level()
        logging.set_verbosity(log_level)

        # force device and distributed setup init explicitly
        args._setup_devices

        if model is None:
            if model_init is not None:
                self.model_init = model_init
                model = self.call_model_init()
            else:
                raise RuntimeError("`Trainer` requires either a `model` or `model_init` argument")
        else:
            if model_init is not None:
                warnings.warn(
                    "`Trainer` requires either a `model` or `model_init` argument, but not both. `model_init` will"
                    " overwrite your model when calling the `train` method. This will become a fatal error in the next"
                    " release.",
                    FutureWarning,
                )
            self.model_init = model_init

        if model.__class__.__name__ in MODEL_MAPPING_NAMES:
            raise ValueError(
                f"The model you have picked ({model.__class__.__name__}) cannot be used as is for training: it only "
                "computes hidden states and does not accept any labels. You should choose a model with a head "
                "suitable for your task like any of the `AutoModelForXxx` listed at "
                "https://huggingface.co/docs/transformers/model_doc/auto."
            )

        if hasattr(model, "is_parallelizable") and model.is_parallelizable and model.model_parallel:
            self.is_model_parallel = True
        else:
            self.is_model_parallel = False

        # At this stage the model is already loaded
        if getattr(model, "is_loaded_in_8bit", False):
            if getattr(model, "_is_int8_training_enabled", False):
                logger.info(
                    "The model is loaded in 8-bit precision. To train this model you need to add additional modules"
                    " inside the model such as adapters using `peft` library and freeze the model weights. Please"
                    " check "
                    " the examples in https://github.com/huggingface/peft for more details."
                )
            else:
                raise ValueError(
                    "The model you want to train is loaded in 8-bit precision.  if you want to fine-tune an 8-bit"
                    " model, please make sure that you have installed `bitsandbytes>=0.37.0`. "
                )

        # Setup Sharded DDP training
        self.sharded_ddp = None
        if len(args.sharded_ddp) > 0:
            if args.deepspeed:
                raise ValueError(
                    "Using --sharded_ddp xxx together with --deepspeed is not possible, deactivate one of those flags."
                )
            if len(args.fsdp) > 0:
                raise ValueError(
                    "Using --sharded_ddp xxx together with --fsdp is not possible, deactivate one of those flags."
                )

            if args.local_rank == -1:
                raise ValueError("Using sharded DDP only works in distributed training.")
            elif not is_fairscale_available():
                raise ImportError("Sharded DDP training requires fairscale: `pip install fairscale`.")
            elif ShardedDDPOption.SIMPLE not in args.sharded_ddp and FullyShardedDDP is None:
                raise ImportError(
                    "Sharded DDP in a mode other than simple training requires fairscale version >= 0.3, found "
                    f"{fairscale.__version__}. Upgrade your fairscale library: `pip install --upgrade fairscale`."
                )
            elif ShardedDDPOption.SIMPLE in args.sharded_ddp:
                self.sharded_ddp = ShardedDDPOption.SIMPLE
            elif ShardedDDPOption.ZERO_DP_2 in args.sharded_ddp:
                self.sharded_ddp = ShardedDDPOption.ZERO_DP_2
            elif ShardedDDPOption.ZERO_DP_3 in args.sharded_ddp:
                self.sharded_ddp = ShardedDDPOption.ZERO_DP_3

        self.fsdp = None
        if len(args.fsdp) > 0:
            if args.deepspeed:
                raise ValueError(
                    "Using --fsdp xxx together with --deepspeed is not possible, deactivate one of those flags."
                )
            if not args.fsdp_config["xla"] and args.local_rank == -1:
                raise ValueError("Using fsdp only works in distributed training.")

            # dep_version_check("torch>=1.12.0")
            # Would have to update setup.py with torch>=1.12.0
            # which isn't ideally given that it will force people not using FSDP to also use torch>=1.12.0
            # below is the current alternative.
            if version.parse(version.parse(torch.__version__).base_version) < version.parse("1.12.0"):
                raise ValueError("FSDP requires PyTorch >= 1.12.0")

            from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch, ShardingStrategy

            if FSDPOption.FULL_SHARD in args.fsdp:
                self.fsdp = ShardingStrategy.FULL_SHARD
            elif FSDPOption.SHARD_GRAD_OP in args.fsdp:
                self.fsdp = ShardingStrategy.SHARD_GRAD_OP
            elif FSDPOption.NO_SHARD in args.fsdp:
                self.fsdp = ShardingStrategy.NO_SHARD

            self.backward_prefetch = BackwardPrefetch.BACKWARD_PRE
            if "backward_prefetch" in self.args.fsdp_config and "backward_pos" not in self.backward_prefetch:
                self.backward_prefetch = BackwardPrefetch.BACKWARD_POST

            self.forword_prefetch = False
            if self.args.fsdp_config.get("forword_prefect", False):
                self.forword_prefetch = True

            self.limit_all_gathers = False
            if self.args.fsdp_config.get("limit_all_gathers", False):
                self.limit_all_gathers = True

        # one place to sort out whether to place the model on device or not
        # postpone switching model to cuda when:
        # 1. MP - since we are trying to fit a much bigger than 1 gpu model
        # 2. fp16-enabled DeepSpeed loads the model in half the size and it doesn't need .to() anyway,
        #    and we only use deepspeed for training at the moment
        # 3. full bf16 or fp16 eval - since the model needs to be cast to the right dtype first
        # 4. Sharded DDP - same as MP
        # 5. FSDP - same as MP
        self.place_model_on_device = args.place_model_on_device
        if (
            self.is_model_parallel
            or args.deepspeed
            or ((args.fp16_full_eval or args.bf16_full_eval) and not args.do_train)
            or (self.sharded_ddp in [ShardedDDPOption.ZERO_DP_2, ShardedDDPOption.ZERO_DP_3])
            or (self.fsdp is not None)
        ):
            self.place_model_on_device = False

        default_collator = default_data_collator if tokenizer is None else DataCollatorWithPadding(tokenizer)
        self.data_collator = data_collator if data_collator is not None else default_collator
        self.train_dataset = train_dataset
        self.eval_dataset = eval_dataset
        self.tokenizer = tokenizer

        if self.place_model_on_device and not getattr(model, "is_loaded_in_8bit", False):
            self._move_model_to_device(model, args.device)

        # Force n_gpu to 1 to avoid DataParallel as MP will manage the GPUs
        if self.is_model_parallel:
            self.args._n_gpu = 1

        # later use `self.model is self.model_wrapped` to check if it's wrapped or not
        self.model_wrapped = model
        self.model = model

        self.compute_metrics = compute_metrics
        self.preprocess_logits_for_metrics = preprocess_logits_for_metrics
        self.optimizer, self.lr_scheduler = optimizers
        if model_init is not None and (self.optimizer is not None or self.lr_scheduler is not None):
            raise RuntimeError(
                "Passing a `model_init` is incompatible with providing the `optimizers` argument. "
                "You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method."
            )
        if is_torch_tpu_available() and self.optimizer is not None:
            for param in self.model.parameters():
                model_device = param.device
                break
            for param_group in self.optimizer.param_groups:
                if len(param_group["params"]) > 0:
                    optimizer_device = param_group["params"][0].device
                    break
            if model_device != optimizer_device:
                raise ValueError(
                    "The model and the optimizer parameters are not on the same device, which probably means you"
                    " created an optimizer around your model **before** putting on the device and passing it to the"
                    " `Trainer`. Make sure the lines `import torch_xla.core.xla_model as xm` and"
                    " `model.to(xm.xla_device())` is performed before the optimizer creation in your script."
                )
        if ((self.sharded_ddp is not None) or args.deepspeed or (self.fsdp is not None)) and (
            self.optimizer is not None or self.lr_scheduler is not None
        ):
            raise RuntimeError(
                "Passing `optimizers` is not allowed if Fairscale, Deepspeed or PyTorch FSDP is enabled."
                "You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method."
            )
        default_callbacks = DEFAULT_CALLBACKS + get_reporting_integration_callbacks(self.args.report_to)
        callbacks = default_callbacks if callbacks is None else default_callbacks + callbacks
        self.callback_handler = CallbackHandler(
            callbacks, self.model, self.tokenizer, self.optimizer, self.lr_scheduler
        )
        self.add_callback(PrinterCallback if self.args.disable_tqdm else DEFAULT_PROGRESS_CALLBACK)

        # Will be set to True by `self._setup_loggers()` on first call to `self.log()`.
        self._loggers_initialized = False

        # Create clone of distant repo and output directory if needed
        if self.args.push_to_hub:
            self.init_git_repo(at_init=True)
            # In case of pull, we need to make sure every process has the latest.
            if is_torch_tpu_available():
                xm.rendezvous("init git repo")
            elif args.local_rank != -1:
                dist.barrier()

        if self.args.should_save:
            os.makedirs(self.args.output_dir, exist_ok=True)

        if not callable(self.data_collator) and callable(getattr(self.data_collator, "collate_batch", None)):
            raise ValueError("The `data_collator` should be a simple callable (function, class with `__call__`).")

        if args.max_steps > 0:
            logger.info("max_steps is given, it will override any value given in num_train_epochs")

        if train_dataset is not None and not has_length(train_dataset) and args.max_steps <= 0:
            raise ValueError("train_dataset does not implement __len__, max_steps has to be specified")

        if (
            train_dataset is not None
            and isinstance(train_dataset, torch.utils.data.IterableDataset)
            and args.group_by_length
        ):
            raise ValueError("the `--group_by_length` option is only available for `Dataset`, not `IterableDataset")

        self._signature_columns = None

        # Mixed precision setup
        self.use_apex = False
        self.use_cuda_amp = False
        self.use_cpu_amp = False

        # Mixed precision setup for SageMaker Model Parallel
        if is_sagemaker_mp_enabled():
            # BF16 + model parallelism in SageMaker: currently not supported, raise an error
            if args.bf16:
                raise ValueError("SageMaker Model Parallelism does not support BF16 yet. Please use FP16 instead ")

            if IS_SAGEMAKER_MP_POST_1_10:
                # When there's mismatch between SMP config and trainer argument, use SMP config as truth
                if args.fp16 != smp.state.cfg.fp16:
                    logger.warning(
                        f"FP16 provided in SM_HP_MP_PARAMETERS is {smp.state.cfg.fp16},"
                        f"but FP16 provided in trainer argument is {args.fp16},"
                        f"setting to {smp.state.cfg.fp16}"
                    )
                    args.fp16 = smp.state.cfg.fp16
            else:
                # smp < 1.10 does not support fp16 in trainer.
                if hasattr(smp.state.cfg, "fp16"):
                    logger.warning(
                        f"FP16 provided in SM_HP_MP_PARAMETERS is {smp.state.cfg.fp16}, "
                        "but SageMaker Model Parallelism < 1.10 does not support FP16 in trainer."
                    )

        if args.fp16 or args.bf16:
            if args.half_precision_backend == "auto":
                if args.device == torch.device("cpu"):
                    if args.fp16:
                        raise ValueError("Tried to use `fp16` but it is not supported on cpu")
                    elif _is_native_cpu_amp_available:
                        args.half_precision_backend = "cpu_amp"
                    else:
                        raise ValueError("Tried to use cpu amp but native cpu amp is not available")
                else:
                    args.half_precision_backend = "cuda_amp"

            logger.info(f"Using {args.half_precision_backend} half precision backend")

        self.do_grad_scaling = False
        if (args.fp16 or args.bf16) and not (args.deepspeed or is_sagemaker_mp_enabled() or is_torch_tpu_available()):
            # deepspeed and SageMaker Model Parallel manage their own half precision
            if args.half_precision_backend == "cuda_amp":
                self.use_cuda_amp = True
                self.amp_dtype = torch.float16 if args.fp16 else torch.bfloat16
                #  bf16 does not need grad scaling
                self.do_grad_scaling = self.amp_dtype == torch.float16
                if self.do_grad_scaling:
                    if self.sharded_ddp is not None:
                        self.scaler = ShardedGradScaler()
                    elif self.fsdp is not None:
                        from torch.distributed.fsdp.sharded_grad_scaler import (
                            ShardedGradScaler as FSDPShardedGradScaler,
                        )

                        self.scaler = FSDPShardedGradScaler()
                    elif is_torch_tpu_available():
                        from torch_xla.amp import GradScaler

                        self.scaler = GradScaler()
                    else:
                        self.scaler = torch.cuda.amp.GradScaler()
            elif args.half_precision_backend == "cpu_amp":
                self.use_cpu_amp = True
                self.amp_dtype = torch.bfloat16
            else:
                if not is_apex_available():
                    raise ImportError(
                        "Using FP16 with APEX but APEX is not installed, please refer to"
                        " https://www.github.com/nvidia/apex."
                    )
                self.use_apex = True

        # FP16 + model parallelism in SageMaker: gradient clipping does not work for now so we raise a helpful error.
        if (
            is_sagemaker_mp_enabled()
            and self.use_cuda_amp
            and args.max_grad_norm is not None
            and args.max_grad_norm > 0
        ):
            raise ValueError(
                "SageMaker Model Parallelism in mixed precision mode does not support gradient clipping yet. Pass "
                "along 'max_grad_norm': 0 in your hyperparameters."
            )

        # Label smoothing
        if self.args.label_smoothing_factor != 0:
            self.label_smoother = LabelSmoother(epsilon=self.args.label_smoothing_factor)
        else:
            self.label_smoother = None

        self.state = TrainerState(
            is_local_process_zero=self.is_local_process_zero(),
            is_world_process_zero=self.is_world_process_zero(),
        )

        self.control = TrainerControl()
        # Internal variable to count flos in each process, will be accumulated in `self.state.total_flos` then
        # returned to 0 every time flos need to be logged
        self.current_flos = 0
        self.hp_search_backend = None
        self.use_tune_checkpoints = False
        default_label_names = find_labels(self.model.__class__)
        self.label_names = default_label_names if self.args.label_names is None else self.args.label_names
        self.can_return_loss = can_return_loss(self.model.__class__)
        self.control = self.callback_handler.on_init_end(self.args, self.state, self.control)

        # Internal variables to keep track of the original batch size
        self._train_batch_size = args.train_batch_size

        # very last
        self._memory_tracker.stop_and_update_metrics()

        # torch.compile
        if args.torch_compile and not is_torch_compile_available():
            raise RuntimeError("Using torch.compile requires PyTorch 2.0 or higher.")

    def add_callback(self, callback):
        """
        Add a callback to the current list of [`~transformer.TrainerCallback`].

        Args:
           callback (`type` or [`~transformer.TrainerCallback`]):
               A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the
               first case, will instantiate a member of that class.
        """
        self.callback_handler.add_callback(callback)

    def pop_callback(self, callback):
        """
        Remove a callback from the current list of [`~transformer.TrainerCallback`] and returns it.

        If the callback is not found, returns `None` (and no error is raised).

        Args:
           callback (`type` or [`~transformer.TrainerCallback`]):
               A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the
               first case, will pop the first member of that class found in the list of callbacks.

        Returns:
            [`~transformer.TrainerCallback`]: The callback removed, if found.
        """
        return self.callback_handler.pop_callback(callback)

    def remove_callback(self, callback):
        """
        Remove a callback from the current list of [`~transformer.TrainerCallback`].

        Args:
           callback (`type` or [`~transformer.TrainerCallback`]):
               A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the
               first case, will remove the first member of that class found in the list of callbacks.
        """
        self.callback_handler.remove_callback(callback)

    def _move_model_to_device(self, model, device):
        model = model.to(device)
        # Moving a model to an XLA device disconnects the tied weights, so we have to retie them.
        if self.args.parallel_mode == ParallelMode.TPU and hasattr(model, "tie_weights"):
            model.tie_weights()

    def _set_signature_columns_if_needed(self):
        if self._signature_columns is None:
            # Inspect model forward signature to keep only the arguments it accepts.
            signature = inspect.signature(self.model.forward)
            self._signature_columns = list(signature.parameters.keys())
            # Labels may be named label or label_ids, the default data collator handles that.
            self._signature_columns += list(set(["label", "label_ids"] + self.label_names))

    def _remove_unused_columns(self, dataset: "datasets.Dataset", description: Optional[str] = None):
        if not self.args.remove_unused_columns:
            return dataset
        self._set_signature_columns_if_needed()
        signature_columns = self._signature_columns

        ignored_columns = list(set(dataset.column_names) - set(signature_columns))
        if len(ignored_columns) > 0:
            dset_description = "" if description is None else f"in the {description} set"
            logger.info(
                f"The following columns {dset_description} don't have a corresponding argument in "
                f"`{self.model.__class__.__name__}.forward` and have been ignored: {', '.join(ignored_columns)}."
                f" If {', '.join(ignored_columns)} are not expected by `{self.model.__class__.__name__}.forward`, "
                " you can safely ignore this message."
            )

        columns = [k for k in signature_columns if k in dataset.column_names]

        if version.parse(datasets.__version__) < version.parse("1.4.0"):
            dataset.set_format(
                type=dataset.format["type"], columns=columns, format_kwargs=dataset.format["format_kwargs"]
            )
            return dataset
        else:
            return dataset.remove_columns(ignored_columns)

    def _get_collator_with_removed_columns(
        self, data_collator: Callable, description: Optional[str] = None
    ) -> Callable:
        """Wrap the data collator in a callable removing unused columns."""
        if not self.args.remove_unused_columns:
            return data_collator
        self._set_signature_columns_if_needed()
        signature_columns = self._signature_columns

        remove_columns_collator = RemoveColumnsCollator(
            data_collator=data_collator,
            signature_columns=signature_columns,
            logger=logger,
            description=description,
            model_name=self.model.__class__.__name__,
        )
        return remove_columns_collator

    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
        if self.train_dataset is None or not has_length(self.train_dataset):
            return None

        generator = None
        if self.args.world_size <= 1:
            generator = torch.Generator()
            # for backwards compatibility, we generate a seed here (which is sampled from a generator seeded with
            # `args.seed`) if data_seed isn't provided.
            # Further on in this method, we default to `args.seed` instead.
            if self.args.data_seed is None:
                seed = int(torch.empty((), dtype=torch.int64).random_().item())
            else:
                seed = self.args.data_seed
            generator.manual_seed(seed)

        seed = self.args.data_seed if self.args.data_seed is not None else self.args.seed

        # Build the sampler.
        if self.args.group_by_length:
            if is_datasets_available() and isinstance(self.train_dataset, datasets.Dataset):
                lengths = (
                    self.train_dataset[self.args.length_column_name]
                    if self.args.length_column_name in self.train_dataset.column_names
                    else None
                )
            else:
                lengths = None
            model_input_name = self.tokenizer.model_input_names[0] if self.tokenizer is not None else None
            if self.args.world_size <= 1:
                return LengthGroupedSampler(
                    self.args.train_batch_size * self.args.gradient_accumulation_steps,
                    dataset=self.train_dataset,
                    lengths=lengths,
                    model_input_name=model_input_name,
                    generator=generator,
                )
            else:
                return DistributedLengthGroupedSampler(
                    self.args.train_batch_size * self.args.gradient_accumulation_steps,
                    dataset=self.train_dataset,
                    num_replicas=self.args.world_size,
                    rank=self.args.process_index,
                    lengths=lengths,
                    model_input_name=model_input_name,
                    seed=seed,
                )

        else:
            if self.args.world_size <= 1:
                return RandomSampler(self.train_dataset, generator=generator)
            elif (
                self.args.parallel_mode in [ParallelMode.TPU, ParallelMode.SAGEMAKER_MODEL_PARALLEL]
                and not self.args.dataloader_drop_last
            ):
                # Use a loop for TPUs when drop_last is False to have all batches have the same size.
                return DistributedSamplerWithLoop(
                    self.train_dataset,
                    batch_size=self.args.per_device_train_batch_size,
                    num_replicas=self.args.world_size,
                    rank=self.args.process_index,
                    seed=seed,
                )
            else:
                return DistributedSampler(
                    self.train_dataset,
                    num_replicas=self.args.world_size,
                    rank=self.args.process_index,
                    seed=seed,
                )

    def get_train_dataloader(self) -> DataLoader:
        """
        Returns the training [`~torch.utils.data.DataLoader`].

        Will use no sampler if `train_dataset` does not implement `__len__`, a random sampler (adapted to distributed
        training if necessary) otherwise.

        Subclass and override this method if you want to inject some custom behavior.
        """
        if self.train_dataset is None:
            raise ValueError("Trainer: training requires a train_dataset.")

        train_dataset = self.train_dataset
        data_collator = self.data_collator
        if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):
            train_dataset = self._remove_unused_columns(train_dataset, description="training")
        else:
            data_collator = self._get_collator_with_removed_columns(data_collator, description="training")

        if isinstance(train_dataset, torch.utils.data.IterableDataset):
            if self.args.world_size > 1:
                train_dataset = IterableDatasetShard(
                    train_dataset,
                    batch_size=self._train_batch_size,
                    drop_last=self.args.dataloader_drop_last,
                    num_processes=self.args.world_size,
                    process_index=self.args.process_index,
                )

            return DataLoader(
                train_dataset,
                batch_size=self._train_batch_size,
                collate_fn=data_collator,
                num_workers=self.args.dataloader_num_workers,
                pin_memory=self.args.dataloader_pin_memory,
            )

        train_sampler = self._get_train_sampler()

        return DataLoader(
            train_dataset,
            batch_size=self._train_batch_size,
            sampler=train_sampler,
            collate_fn=data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
            pin_memory=self.args.dataloader_pin_memory,
            worker_init_fn=seed_worker,
        )

    def _get_eval_sampler(self, eval_dataset: Dataset) -> Optional[torch.utils.data.Sampler]:
        # Deprecated code
        if self.args.use_legacy_prediction_loop:
            if is_torch_tpu_available():
                return SequentialDistributedSampler(
                    eval_dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal()
                )
            elif is_sagemaker_mp_enabled():
                return SequentialDistributedSampler(
                    eval_dataset,
                    num_replicas=smp.dp_size(),
                    rank=smp.dp_rank(),
                    batch_size=self.args.per_device_eval_batch_size,
                )
            elif self.args.local_rank != -1:
                return SequentialDistributedSampler(eval_dataset)
            else:
                return SequentialSampler(eval_dataset)

        if self.args.world_size <= 1:
            return SequentialSampler(eval_dataset)
        else:
            return ShardSampler(
                eval_dataset,
                batch_size=self.args.per_device_eval_batch_size,
                num_processes=self.args.world_size,
                process_index=self.args.process_index,
            )

    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
        """
        Returns the evaluation [`~torch.utils.data.DataLoader`].

        Subclass and override this method if you want to inject some custom behavior.

        Args:
            eval_dataset (`torch.utils.data.Dataset`, *optional*):
                If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted
                by the `model.forward()` method are automatically removed. It must implement `__len__`.
        """
        if eval_dataset is None and self.eval_dataset is None:
            raise ValueError("Trainer: evaluation requires an eval_dataset.")
        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
        data_collator = self.data_collator

        if is_datasets_available() and isinstance(eval_dataset, datasets.Dataset):
            eval_dataset = self._remove_unused_columns(eval_dataset, description="evaluation")
        else:
            data_collator = self._get_collator_with_removed_columns(data_collator, description="evaluation")

        if isinstance(eval_dataset, torch.utils.data.IterableDataset):
            if self.args.world_size > 1:
                eval_dataset = IterableDatasetShard(
                    eval_dataset,
                    batch_size=self.args.per_device_eval_batch_size,
                    drop_last=self.args.dataloader_drop_last,
                    num_processes=self.args.world_size,
                    process_index=self.args.process_index,
                )
            return DataLoader(
                eval_dataset,
                batch_size=self.args.eval_batch_size,
                collate_fn=data_collator,
                num_workers=self.args.dataloader_num_workers,
                pin_memory=self.args.dataloader_pin_memory,
            )

        eval_sampler = self._get_eval_sampler(eval_dataset)

        return DataLoader(
            eval_dataset,
            sampler=eval_sampler,
            batch_size=self.args.eval_batch_size,
            collate_fn=data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
            pin_memory=self.args.dataloader_pin_memory,
        )

    def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
        """
        Returns the test [`~torch.utils.data.DataLoader`].

        Subclass and override this method if you want to inject some custom behavior.

        Args:
            test_dataset (`torch.utils.data.Dataset`, *optional*):
                The test dataset to use. If it is a [`~datasets.Dataset`], columns not accepted by the
                `model.forward()` method are automatically removed. It must implement `__len__`.
        """
        data_collator = self.data_collator

        if is_datasets_available() and isinstance(test_dataset, datasets.Dataset):
            test_dataset = self._remove_unused_columns(test_dataset, description="test")
        else:
            data_collator = self._get_collator_with_removed_columns(data_collator, description="test")

        if isinstance(test_dataset, torch.utils.data.IterableDataset):
            if self.args.world_size > 1:
                test_dataset = IterableDatasetShard(
                    test_dataset,
                    batch_size=self.args.eval_batch_size,
                    drop_last=self.args.dataloader_drop_last,
                    num_processes=self.args.world_size,
                    process_index=self.args.process_index,
                )
            return DataLoader(
                test_dataset,
                batch_size=self.args.eval_batch_size,
                collate_fn=data_collator,
                num_workers=self.args.dataloader_num_workers,
                pin_memory=self.args.dataloader_pin_memory,
            )

        test_sampler = self._get_eval_sampler(test_dataset)

        # We use the same batch_size as for eval.
        return DataLoader(
            test_dataset,
            sampler=test_sampler,
            batch_size=self.args.eval_batch_size,
            collate_fn=data_collator,
            drop_last=self.args.dataloader_drop_last,
            num_workers=self.args.dataloader_num_workers,
            pin_memory=self.args.dataloader_pin_memory,
        )

    def create_optimizer_and_scheduler(self, num_training_steps: int):
        """
        Setup the optimizer and the learning rate scheduler.

        We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
        Trainer's init through `optimizers`, or subclass and override this method (or `create_optimizer` and/or
        `create_scheduler`) in a subclass.
        """
        self.create_optimizer()
        if IS_SAGEMAKER_MP_POST_1_10 and smp.state.cfg.fp16:
            # If smp >= 1.10 and fp16 is enabled, we unwrap the optimizer
            optimizer = self.optimizer.optimizer
        else:
            optimizer = self.optimizer
        self.create_scheduler(num_training_steps=num_training_steps, optimizer=optimizer)

    def create_optimizer(self):
        """
        Setup the optimizer.

        We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
        Trainer's init through `optimizers`, or subclass and override this method in a subclass.
        """
        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model

        if self.optimizer is None:
            decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)
            decay_parameters = [name for name in decay_parameters if "bias" not in name]
            optimizer_grouped_parameters = [
                {
                    "params": [
                        p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad)
                    ],
                    "weight_decay": self.args.weight_decay,
                },
                {
                    "params": [
                        p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad)
                    ],
                    "weight_decay": 0.0,
                },
            ]

            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(self.args)

            if self.sharded_ddp == ShardedDDPOption.SIMPLE:
                self.optimizer = OSS(
                    params=optimizer_grouped_parameters,
                    optim=optimizer_cls,
                    **optimizer_kwargs,
                )
            else:
                self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
                if optimizer_cls.__name__ == "Adam8bit":
                    import bitsandbytes

                    manager = bitsandbytes.optim.GlobalOptimManager.get_instance()

                    skipped = 0
                    for module in opt_model.modules():
                        if isinstance(module, nn.Embedding):
                            skipped += sum({p.data_ptr(): p.numel() for p in module.parameters()}.values())
                            print(f"skipped {module}: {skipped/2**20}M params")
                            manager.register_module_override(module, "weight", {"optim_bits": 32})
                            logger.debug(f"bitsandbytes: will optimize {module} in fp32")
                    print(f"skipped: {skipped/2**20}M params")

        if is_sagemaker_mp_enabled():
            self.optimizer = smp.DistributedOptimizer(self.optimizer)

        return self.optimizer

    @staticmethod
    def get_optimizer_cls_and_kwargs(args: TrainingArguments) -> Tuple[Any, Any]:
        """
        Returns the optimizer class and optimizer parameters based on the training arguments.

        Args:
            args (`transformers.training_args.TrainingArguments`):
                The training arguments for the training session.

        """

        # parse args.optim_args
        optim_args = {}
        if args.optim_args:
            for mapping in args.optim_args.replace(" ", "").split(","):
                key, value = mapping.split("=")
                optim_args[key] = value

        optimizer_kwargs = {"lr": args.learning_rate}

        adam_kwargs = {
            "betas": (args.adam_beta1, args.adam_beta2),
            "eps": args.adam_epsilon,
        }
        if args.optim == OptimizerNames.ADAFACTOR:
            optimizer_cls = Adafactor
            optimizer_kwargs.update({"scale_parameter": False, "relative_step": False})
        elif args.optim == OptimizerNames.ADAMW_HF:
            from transformers.optimization import AdamW

            optimizer_cls = AdamW
            optimizer_kwargs.update(adam_kwargs)
        elif args.optim in [OptimizerNames.ADAMW_TORCH, OptimizerNames.ADAMW_TORCH_FUSED]:
            from torch.optim import AdamW

            optimizer_cls = AdamW
            optimizer_kwargs.update(adam_kwargs)
            if args.optim == OptimizerNames.ADAMW_TORCH_FUSED:
                optimizer_kwargs.update({"fused": True})
        elif args.optim == OptimizerNames.ADAMW_TORCH_XLA:
            try:
                from torch_xla.amp.syncfree import AdamW

                optimizer_cls = AdamW
                optimizer_kwargs.update(adam_kwargs)
            except ImportError:
                raise ValueError("Trainer failed to import syncfree AdamW from torch_xla.")
        elif args.optim == OptimizerNames.ADAMW_APEX_FUSED:
            try:
                from apex.optimizers import FusedAdam

                optimizer_cls = FusedAdam
                optimizer_kwargs.update(adam_kwargs)
            except ImportError:
                raise ValueError("Trainer tried to instantiate apex FusedAdam but apex is not installed!")
        elif args.optim == OptimizerNames.ADAMW_BNB:
            try:
                from bitsandbytes.optim import Adam8bit

                optimizer_cls = Adam8bit
                optimizer_kwargs.update(adam_kwargs)
            except ImportError:
                raise ValueError("Trainer tried to instantiate bnb Adam8bit but bnb is not installed!")
        elif args.optim == OptimizerNames.ADAMW_ANYPRECISION:
            try:
                from torchdistx.optimizers import AnyPrecisionAdamW

                optimizer_cls = AnyPrecisionAdamW
                optimizer_kwargs.update(adam_kwargs)

                # TODO Change dtypes back to M=FP32, Var = BF16, Kahan = False once they can be cast together in torchdistx.
                optimizer_kwargs.update(
                    {
                        "use_kahan_summation": strtobool(optim_args.get("use_kahan_summation", "False")),
                        "momentum_dtype": getattr(torch, optim_args.get("momentum_dtype", "float32")),
                        "variance_dtype": getattr(torch, optim_args.get("variance_dtype", "float32")),
                        "compensation_buffer_dtype": getattr(
                            torch, optim_args.get("compensation_buffer_dtype", "bfloat16")
                        ),
                    }
                )
            except ImportError:
                raise ValueError("Please install https://github.com/pytorch/torchdistx")
        elif args.optim == OptimizerNames.SGD:
            optimizer_cls = torch.optim.SGD
        elif args.optim == OptimizerNames.ADAGRAD:
            optimizer_cls = torch.optim.Adagrad
        else:
            raise ValueError(f"Trainer cannot instantiate unsupported optimizer: {args.optim}")
        return optimizer_cls, optimizer_kwargs

    def create_scheduler(self, num_training_steps: int, optimizer: torch.optim.Optimizer = None):
        """
        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
        passed as an argument.

        Args:
            num_training_steps (int): The number of training steps to do.
        """
        if self.lr_scheduler is None:
            self.lr_scheduler = get_scheduler(
                self.args.lr_scheduler_type,
                optimizer=self.optimizer if optimizer is None else optimizer,
                num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
                num_training_steps=num_training_steps,
            )
        return self.lr_scheduler

    def num_examples(self, dataloader: DataLoader) -> int:
        """
        Helper to get number of samples in a [`~torch.utils.data.DataLoader`] by accessing its dataset. When
        dataloader.dataset does not exist or has no length, estimates as best it can
        """
        try:
            dataset = dataloader.dataset
            # Special case for IterableDatasetShard, we need to dig deeper
            if isinstance(dataset, IterableDatasetShard):
                return len(dataloader.dataset.dataset)
            return len(dataloader.dataset)
        except (NameError, AttributeError, TypeError):  # no dataset or length, estimate by length of dataloader
            return len(dataloader) * self.args.per_device_train_batch_size

    def _hp_search_setup(self, trial: Union["optuna.Trial", Dict[str, Any]]):
        """HP search setup code"""
        self._trial = trial

        if self.hp_search_backend is None or trial is None:
            return
        if self.hp_search_backend == HPSearchBackend.OPTUNA:
            params = self.hp_space(trial)
        elif self.hp_search_backend == HPSearchBackend.RAY:
            params = trial
            params.pop("wandb", None)
        elif self.hp_search_backend == HPSearchBackend.SIGOPT:
            params = {k: int(v) if isinstance(v, str) else v for k, v in trial.assignments.items()}
        elif self.hp_search_backend == HPSearchBackend.WANDB:
            params = trial

        for key, value in params.items():
            if not hasattr(self.args, key):
                logger.warning(
                    f"Trying to set {key} in the hyperparameter search but there is no corresponding field in"
                    " `TrainingArguments`."
                )
                continue
            old_attr = getattr(self.args, key, None)
            # Casting value to the proper type
            if old_attr is not None:
                value = type(old_attr)(value)
            setattr(self.args, key, value)
        if self.hp_search_backend == HPSearchBackend.OPTUNA:
            logger.info(f"Trial: {trial.params}")
        if self.hp_search_backend == HPSearchBackend.SIGOPT:
            logger.info(f"SigOpt Assignments: {trial.assignments}")
        if self.hp_search_backend == HPSearchBackend.WANDB:
            logger.info(f"W&B Sweep parameters: {trial}")
        if self.args.deepspeed:
            # Rebuild the deepspeed config to reflect the updated training parameters
            from transformers.deepspeed import HfTrainerDeepSpeedConfig

            self.args.hf_deepspeed_config = HfTrainerDeepSpeedConfig(self.args.deepspeed)
            self.args.hf_deepspeed_config.trainer_config_process(self.args)

    def _report_to_hp_search(self, trial: Union["optuna.Trial", Dict[str, Any]], step: int, metrics: Dict[str, float]):
        if self.hp_search_backend is None or trial is None:
            return
        self.objective = self.compute_objective(metrics.copy())
        if self.hp_search_backend == HPSearchBackend.OPTUNA:
            import optuna

            trial.report(self.objective, step)
            if trial.should_prune():
                self.callback_handler.on_train_end(self.args, self.state, self.control)
                raise optuna.TrialPruned()
        elif self.hp_search_backend == HPSearchBackend.RAY:
            from ray import tune

            if self.control.should_save:
                self._tune_save_checkpoint()
            tune.report(objective=self.objective, **metrics)

    def _tune_save_checkpoint(self):
        from ray import tune

        if not self.use_tune_checkpoints:
            return
        with tune.checkpoint_dir(step=self.state.global_step) as checkpoint_dir:
            output_dir = os.path.join(checkpoint_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}")
            self.save_model(output_dir, _internal_call=True)
            if self.args.should_save:
                self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))
                torch.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))
                torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))

    def call_model_init(self, trial=None):
        model_init_argcount = number_of_arguments(self.model_init)
        if model_init_argcount == 0:
            model = self.model_init()
        elif model_init_argcount == 1:
            model = self.model_init(trial)
        else:
            raise RuntimeError("model_init should have 0 or 1 argument.")

        if model is None:
            raise RuntimeError("model_init should not return None.")

        return model

    def torch_jit_model_eval(self, model, dataloader, training=False):
        if not training:
            if dataloader is None:
                logger.warning("failed to use PyTorch jit mode due to current dataloader is none.")
                return model
            example_batch = next(iter(dataloader))
            example_batch = self._prepare_inputs(example_batch)
            try:
                jit_model = model.eval()
                with ContextManagers([self.autocast_smart_context_manager(cache_enabled=False), torch.no_grad()]):
                    if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.14.0"):
                        if isinstance(example_batch, dict):
                            jit_model = torch.jit.trace(jit_model, example_kwarg_inputs=example_batch, strict=False)
                        else:
                            jit_model = torch.jit.trace(
                                jit_model,
                                example_kwarg_inputs={key: example_batch[key] for key in example_batch},
                                strict=False,
                            )
                    else:
                        jit_inputs = []
                        for key in example_batch:
                            example_tensor = torch.ones_like(example_batch[key])
                            jit_inputs.append(example_tensor)
                        jit_inputs = tuple(jit_inputs)
                        jit_model = torch.jit.trace(jit_model, jit_inputs, strict=False)
                jit_model = torch.jit.freeze(jit_model)
                with torch.no_grad():
                    jit_model(**example_batch)
                    jit_model(**example_batch)
                model = jit_model
                self.use_cpu_amp = False
                self.use_cuda_amp = False
            except (RuntimeError, TypeError, ValueError, NameError, IndexError) as e:
                logger.warning(f"failed to use PyTorch jit mode due to: {e}.")

        return model

    def ipex_optimize_model(self, model, training=False, dtype=torch.float32):
        if not is_ipex_available():
            raise ImportError(
                "Using IPEX but IPEX is not installed or IPEX's version does not match current PyTorch, please refer"
                " to https://github.com/intel/intel-extension-for-pytorch."
            )

        import intel_extension_for_pytorch as ipex

        if not training:
            model.eval()
            dtype = torch.bfloat16 if not self.is_in_train and self.args.bf16_full_eval else dtype
            # conv_bn_folding is disabled as it fails in symbolic tracing, resulting in ipex warnings
            model = ipex.optimize(model, dtype=dtype, level="O1", conv_bn_folding=False, inplace=not self.is_in_train)
        else:
            if not model.training:
                model.train()
            model, self.optimizer = ipex.optimize(
                model, dtype=dtype, optimizer=self.optimizer, inplace=True, level="O1"
            )

        return model

    def _wrap_model(self, model, training=True, dataloader=None):
        if self.args.torch_compile:
            model = torch.compile(model, backend=self.args.torch_compile_backend, mode=self.args.torch_compile_mode)

        if self.args.use_ipex:
            dtype = torch.bfloat16 if self.use_cpu_amp else torch.float32
            model = self.ipex_optimize_model(model, training, dtype=dtype)

        if is_sagemaker_mp_enabled():
            # Wrapping the base model twice in a DistributedModel will raise an error.
            if isinstance(self.model_wrapped, smp.model.DistributedModel):
                return self.model_wrapped
            return smp.DistributedModel(model, backward_passes_per_step=self.args.gradient_accumulation_steps)

        # already initialized its own DDP and AMP
        if self.deepspeed:
            return self.deepspeed

        # train/eval could be run multiple-times - if already wrapped, don't re-wrap it again
        if unwrap_model(model) is not model:
            return model

        # Mixed precision training with apex (torch < 1.6)
        if self.use_apex and training:
            model, self.optimizer = amp.initialize(model, self.optimizer, opt_level=self.args.fp16_opt_level)

        # Multi-gpu training (should be after apex fp16 initialization)
        if self.args.n_gpu > 1:
            model = nn.DataParallel(model)

        if self.args.jit_mode_eval:
            start_time = time.time()
            model = self.torch_jit_model_eval(model, dataloader, training)
            self.jit_compilation_time = round(time.time() - start_time, 4)

        # Note: in torch.distributed mode, there's no point in wrapping the model
        # inside a DistributedDataParallel as we'll be under `no_grad` anyways.
        if not training:
            return model

        # Distributed training (should be after apex fp16 initialization)
        if self.sharded_ddp is not None:
            # Sharded DDP!
            if self.sharded_ddp == ShardedDDPOption.SIMPLE:
                model = ShardedDDP(model, self.optimizer)
            else:
                mixed_precision = self.args.fp16 or self.args.bf16
                cpu_offload = ShardedDDPOption.OFFLOAD in self.args.sharded_ddp
                zero_3 = self.sharded_ddp == ShardedDDPOption.ZERO_DP_3
                # XXX: Breaking the self.model convention but I see no way around it for now.
                if ShardedDDPOption.AUTO_WRAP in self.args.sharded_ddp:
                    model = auto_wrap(model)
                self.model = model = FullyShardedDDP(
                    model,
                    mixed_precision=mixed_precision,
                    reshard_after_forward=zero_3,
                    cpu_offload=cpu_offload,
                ).to(self.args.device)
        # Distributed training using PyTorch FSDP
        elif self.fsdp is not None:
            if not self.args.fsdp_config["xla"]:
                # PyTorch FSDP!
                from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload, MixedPrecision
                from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
                from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy, transformer_auto_wrap_policy

                if FSDPOption.OFFLOAD in self.args.fsdp:
                    cpu_offload = CPUOffload(offload_params=True)
                else:
                    cpu_offload = CPUOffload(offload_params=False)

                auto_wrap_policy = None

                if FSDPOption.AUTO_WRAP in self.args.fsdp:
                    if self.args.fsdp_config["fsdp_min_num_params"] > 0:
                        auto_wrap_policy = functools.partial(
                            size_based_auto_wrap_policy, min_num_params=self.args.fsdp_config["fsdp_min_num_params"]
                        )
                    elif self.args.fsdp_config.get("fsdp_transformer_layer_cls_to_wrap", None) is not None:
                        transformer_cls_to_wrap = set()
                        for layer_class in self.args.fsdp_config["fsdp_transformer_layer_cls_to_wrap"]:
                            transformer_cls = get_module_class_from_name(model, layer_class)
                            if transformer_cls is None:
                                raise Exception("Could not find the transformer layer class to wrap in the model.")
                            else:
                                transformer_cls_to_wrap.add(transformer_cls)
                        auto_wrap_policy = functools.partial(
                            transformer_auto_wrap_policy,
                            # Transformer layer class to wrap
                            transformer_layer_cls=transformer_cls_to_wrap,
                        )
                mixed_precision_policy = None
                dtype = None
                if self.args.fp16:
                    dtype = torch.float16
                elif self.args.bf16:
                    dtype = torch.bfloat16
                if dtype is not None:
                    mixed_precision_policy = MixedPrecision(param_dtype=dtype, reduce_dtype=dtype, buffer_dtype=dtype)
                if type(model) != FSDP:
                    # XXX: Breaking the self.model convention but I see no way around it for now.
                    self.model = model = FSDP(
                        model,
                        sharding_strategy=self.fsdp,
                        cpu_offload=cpu_offload,
                        auto_wrap_policy=auto_wrap_policy,
                        mixed_precision=mixed_precision_policy,
                        device_id=self.args.device,
                        backward_prefetch=self.backward_prefetch,
                        forward_prefetch=self.forword_prefetch,
                        limit_all_gathers=self.limit_all_gathers,
                    )
            else:
                try:
                    from torch_xla.distributed.fsdp import XlaFullyShardedDataParallel as FSDP
                    from torch_xla.distributed.fsdp import checkpoint_module
                    from torch_xla.distributed.fsdp.wrap import (
                        size_based_auto_wrap_policy,
                        transformer_auto_wrap_policy,
                    )
                except ImportError:
                    raise ImportError("Missing XLA FSDP related module; please make sure to use torch-xla >= 2.0.")
                auto_wrap_policy = None
                auto_wrapper_callable = None
                if self.args.fsdp_config["fsdp_min_num_params"] > 0:
                    auto_wrap_policy = functools.partial(
                        size_based_auto_wrap_policy, min_num_params=self.args.fsdp_config["fsdp_min_num_params"]
                    )
                elif self.args.fsdp_config.get("fsdp_transformer_layer_cls_to_wrap", None) is not None:
                    transformer_cls_to_wrap = set()
                    for layer_class in self.args.fsdp_config["fsdp_transformer_layer_cls_to_wrap"]:
                        transformer_cls = get_module_class_from_name(model, layer_class)
                        if transformer_cls is None:
                            raise Exception("Could not find the transformer layer class to wrap in the model.")
                        else:
                            transformer_cls_to_wrap.add(transformer_cls)
                    auto_wrap_policy = functools.partial(
                        transformer_auto_wrap_policy,
                        # Transformer layer class to wrap
                        transformer_layer_cls=transformer_cls_to_wrap,
                    )
                fsdp_kwargs = self.args.xla_fsdp_config
                if self.args.fsdp_config["xla_fsdp_grad_ckpt"]:
                    # Apply gradient checkpointing to auto-wrapped sub-modules if specified
                    def auto_wrapper_callable(m, *args, **kwargs):
                        return FSDP(checkpoint_module(m), *args, **kwargs)

                # Wrap the base model with an outer FSDP wrapper
                self.model = model = FSDP(
                    model,
                    auto_wrap_policy=auto_wrap_policy,
                    auto_wrapper_callable=auto_wrapper_callable,
                    **fsdp_kwargs,
                )

                # Patch `xm.optimizer_step` should not reduce gradients in this case,
                # as FSDP does not need gradient reduction over sharded parameters.
                def patched_optimizer_step(optimizer, barrier=False, optimizer_args={}):
                    loss = optimizer.step(**optimizer_args)
                    if barrier:
                        xm.mark_step()
                    return loss

                xm.optimizer_step = patched_optimizer_step
        elif is_sagemaker_dp_enabled():
            model = nn.parallel.DistributedDataParallel(
                model, device_ids=[int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"))]
            )
        elif self.args.local_rank != -1:
            kwargs = {}
            if self.args.ddp_find_unused_parameters is not None:
                kwargs["find_unused_parameters"] = self.args.ddp_find_unused_parameters
            elif isinstance(model, PreTrainedModel):
                # find_unused_parameters breaks checkpointing as per
                # https://github.com/huggingface/transformers/pull/4659#issuecomment-643356021
                kwargs["find_unused_parameters"] = not model.is_gradient_checkpointing
            else:
                kwargs["find_unused_parameters"] = True

            if self.args.ddp_bucket_cap_mb is not None:
                kwargs["bucket_cap_mb"] = self.args.ddp_bucket_cap_mb
            if is_torch_neuroncore_available():
                return model
            model = nn.parallel.DistributedDataParallel(
                model,
                device_ids=[self.args.local_rank] if self.args._n_gpu != 0 else None,
                output_device=self.args.local_rank if self.args._n_gpu != 0 else None,
                **kwargs,
            )

        return model

    def train(
        self,
        resume_from_checkpoint: Optional[Union[str, bool]] = None,
        trial: Union["optuna.Trial", Dict[str, Any]] = None,
        ignore_keys_for_eval: Optional[List[str]] = None,
        **kwargs,
    ):
        """
        Main training entry point.

        Args:
            resume_from_checkpoint (`str` or `bool`, *optional*):
                If a `str`, local path to a saved checkpoint as saved by a previous instance of [`Trainer`]. If a
                `bool` and equals `True`, load the last checkpoint in *args.output_dir* as saved by a previous instance
                of [`Trainer`]. If present, training will resume from the model/optimizer/scheduler states loaded here.
            trial (`optuna.Trial` or `Dict[str, Any]`, *optional*):
                The trial run or the hyperparameter dictionary for hyperparameter search.
            ignore_keys_for_eval (`List[str]`, *optional*)
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions for evaluation during the training.
            kwargs:
                Additional keyword arguments used to hide deprecated arguments
        """
        if resume_from_checkpoint is False:
            resume_from_checkpoint = None

        # memory metrics - must set up as early as possible
        self._memory_tracker.start()

        args = self.args

        self.is_in_train = True

        # do_train is not a reliable argument, as it might not be set and .train() still called, so
        # the following is a workaround:
        if (args.fp16_full_eval or args.bf16_full_eval) and not args.do_train:
            self._move_model_to_device(self.model, args.device)

        if "model_path" in kwargs:
            resume_from_checkpoint = kwargs.pop("model_path")
            warnings.warn(
                "`model_path` is deprecated and will be removed in a future version. Use `resume_from_checkpoint` "
                "instead.",
                FutureWarning,
            )
        if len(kwargs) > 0:
            raise TypeError(f"train() received got unexpected keyword arguments: {', '.join(list(kwargs.keys()))}.")
        # This might change the seed so needs to run first.
        self._hp_search_setup(trial)
        self._train_batch_size = self.args.train_batch_size

        # Model re-init
        model_reloaded = False
        if self.model_init is not None:
            # Seed must be set before instantiating the model when using model_init.
            enable_full_determinism(self.args.seed) if self.args.full_determinism else set_seed(self.args.seed)
            self.model = self.call_model_init(trial)
            model_reloaded = True
            # Reinitializes optimizer and scheduler
            self.optimizer, self.lr_scheduler = None, None

        # Load potential model checkpoint
        if isinstance(resume_from_checkpoint, bool) and resume_from_checkpoint:
            resume_from_checkpoint = get_last_checkpoint(args.output_dir)
            if resume_from_checkpoint is None:
                raise ValueError(f"No valid checkpoint found in output directory ({args.output_dir})")

        if resume_from_checkpoint is not None and not is_sagemaker_mp_enabled() and args.deepspeed is None:
            self._load_from_checkpoint(resume_from_checkpoint)

        # If model was re-initialized, put it on the right device and update self.model_wrapped
        if model_reloaded:
            if self.place_model_on_device:
                self._move_model_to_device(self.model, args.device)
            self.model_wrapped = self.model

        inner_training_loop = find_executable_batch_size(
            self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size
        )
        return inner_training_loop(
            args=args,
            resume_from_checkpoint=resume_from_checkpoint,
            trial=trial,
            ignore_keys_for_eval=ignore_keys_for_eval,
        )

    def _inner_training_loop(
        self, batch_size=None, args=None, resume_from_checkpoint=None, trial=None, ignore_keys_for_eval=None
    ):
        self._train_batch_size = batch_size
        # Data loader and number of training steps
        train_dataloader = self.get_train_dataloader()

        # Setting up training control variables:
        # number of training epochs: num_train_epochs
        # number of training steps per epoch: num_update_steps_per_epoch
        # total number of training steps to execute: max_steps
        total_train_batch_size = args.train_batch_size * args.gradient_accumulation_steps * args.world_size

        len_dataloader = None
        if has_length(train_dataloader):
            len_dataloader = len(train_dataloader)
            num_update_steps_per_epoch = len_dataloader // args.gradient_accumulation_steps
            num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)
            num_examples = self.num_examples(train_dataloader)
            if args.max_steps > 0:
                max_steps = args.max_steps
                num_train_epochs = args.max_steps // num_update_steps_per_epoch + int(
                    args.max_steps % num_update_steps_per_epoch > 0
                )
                # May be slightly incorrect if the last batch in the training dataloader has a smaller size but it's
                # the best we can do.
                num_train_samples = args.max_steps * total_train_batch_size
            else:
                max_steps = math.ceil(args.num_train_epochs * num_update_steps_per_epoch)
                num_train_epochs = math.ceil(args.num_train_epochs)
                num_train_samples = self.num_examples(train_dataloader) * args.num_train_epochs
        elif args.max_steps > 0:  # Rely on max_steps when dataloader does not have a working size
            max_steps = args.max_steps
            # Setting a very large number of epochs so we go as many times as necessary over the iterator.
            num_train_epochs = sys.maxsize
            num_update_steps_per_epoch = max_steps
            num_examples = total_train_batch_size * args.max_steps
            num_train_samples = args.max_steps * total_train_batch_size
        else:
            raise ValueError(
                "args.max_steps must be set to a positive value if dataloader does not have a length, was"
                f" {args.max_steps}"
            )

        if DebugOption.UNDERFLOW_OVERFLOW in self.args.debug:
            if self.args.n_gpu > 1:
                # nn.DataParallel(model) replicates the model, creating new variables and module
                # references registered here no longer work on other gpus, breaking the module
                raise ValueError(
                    "Currently --debug underflow_overflow is not supported under DP. Please use DDP"
                    " (torch.distributed.launch)."
                )
            else:
                debug_overflow = DebugUnderflowOverflow(self.model)  # noqa

        delay_optimizer_creation = (
            self.sharded_ddp is not None
            and self.sharded_ddp != ShardedDDPOption.SIMPLE
            or is_sagemaker_mp_enabled()
            or self.fsdp is not None
        )
        if args.deepspeed:
            deepspeed_engine, optimizer, lr_scheduler = deepspeed_init(
                self, num_training_steps=max_steps, resume_from_checkpoint=resume_from_checkpoint
            )
            self.model = deepspeed_engine.module
            self.model_wrapped = deepspeed_engine
            self.deepspeed = deepspeed_engine
            self.optimizer = optimizer
            self.lr_scheduler = lr_scheduler
        elif not delay_optimizer_creation:
            self.create_optimizer_and_scheduler(num_training_steps=max_steps)

        self.state = TrainerState()
        self.state.is_hyper_param_search = trial is not None

        # Activate gradient checkpointing if needed
        if args.gradient_checkpointing:
            self.model.gradient_checkpointing_enable()

        model = self._wrap_model(self.model_wrapped)

        if is_sagemaker_mp_enabled() and resume_from_checkpoint is not None:
            self._load_from_checkpoint(resume_from_checkpoint, model)

        # for the rest of this function `model` is the outside model, whether it was wrapped or not
        if model is not self.model:
            self.model_wrapped = model

        if delay_optimizer_creation:
            self.create_optimizer_and_scheduler(num_training_steps=max_steps)

        # Check if saved optimizer or scheduler states exist
        self._load_optimizer_and_scheduler(resume_from_checkpoint)

        # important: at this point:
        # self.model         is the Transformers Model
        # self.model_wrapped is DDP(Transformers Model), Deepspeed(Transformers Model), etc.

        # Train!
        logger.info("***** Running training *****")
        logger.info(f"  Num examples = {num_examples}")
        logger.info(f"  Num Epochs = {num_train_epochs}")
        logger.info(f"  Instantaneous batch size per device = {args.per_device_train_batch_size}")
        logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size}")
        logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
        logger.info(f"  Total optimization steps = {max_steps}")
        logger.info(
            f"  Number of trainable parameters = {sum(p.numel() for p in model.parameters() if p.requires_grad)}"
        )

        self.state.epoch = 0
        start_time = time.time()
        epochs_trained = 0
        steps_trained_in_current_epoch = 0
        steps_trained_progress_bar = None

        # Check if continuing training from a checkpoint
        if resume_from_checkpoint is not None and os.path.isfile(
            os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME)
        ):
            self.state = TrainerState.load_from_json(os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME))
            epochs_trained = self.state.global_step // num_update_steps_per_epoch
            if not args.ignore_data_skip:
                steps_trained_in_current_epoch = self.state.global_step % (num_update_steps_per_epoch)
                steps_trained_in_current_epoch *= args.gradient_accumulation_steps
            else:
                steps_trained_in_current_epoch = 0

            logger.info("  Continuing training from checkpoint, will skip to saved global_step")
            logger.info(f"  Continuing training from epoch {epochs_trained}")
            logger.info(f"  Continuing training from global step {self.state.global_step}")
            if not args.ignore_data_skip:
                if skip_first_batches is None:
                    logger.info(
                        f"  Will skip the first {epochs_trained} epochs then the first"
                        f" {steps_trained_in_current_epoch} batches in the first epoch. If this takes a lot of time,"
                        " you can install the latest version of Accelerate with `pip install -U accelerate`.You can"
                        " also add the `--ignore_data_skip` flag to your launch command, but you will resume the"
                        " training on data already seen by your model."
                    )
                else:
                    logger.info(
                        f"  Will skip the first {epochs_trained} epochs then the first"
                        f" {steps_trained_in_current_epoch} batches in the first epoch."
                    )
                if self.is_local_process_zero() and not args.disable_tqdm and skip_first_batches is None:
                    steps_trained_progress_bar = tqdm(total=steps_trained_in_current_epoch)
                    steps_trained_progress_bar.set_description("Skipping the first batches")

        # Update the references
        self.callback_handler.model = self.model
        self.callback_handler.optimizer = self.optimizer
        self.callback_handler.lr_scheduler = self.lr_scheduler
        self.callback_handler.train_dataloader = train_dataloader
        if self.hp_name is not None and self._trial is not None:
            # use self._trial because the SigOpt/Optuna hpo only call `_hp_search_setup(trial)` instead of passing trial
            # parameter to Train when using DDP.
            self.state.trial_name = self.hp_name(self._trial)
        if trial is not None:
            assignments = trial.assignments if self.hp_search_backend == HPSearchBackend.SIGOPT else trial
            self.state.trial_params = hp_params(assignments)
        else:
            self.state.trial_params = None
        # This should be the same if the state has been saved but in case the training arguments changed, it's safer
        # to set this after the load.
        self.state.max_steps = max_steps
        self.state.num_train_epochs = num_train_epochs
        self.state.is_local_process_zero = self.is_local_process_zero()
        self.state.is_world_process_zero = self.is_world_process_zero()

        # tr_loss is a tensor to avoid synchronization of TPUs through .item()
        tr_loss = torch.tensor(0.0).to(args.device)
        # _total_loss_scalar is updated everytime .item() has to be called on tr_loss and stores the sum of all losses
        self._total_loss_scalar = 0.0
        self._globalstep_last_logged = self.state.global_step
        model.zero_grad()

        self.control = self.callback_handler.on_train_begin(args, self.state, self.control)

        # Skip the first epochs_trained epochs to get the random state of the dataloader at the right point.
        if not args.ignore_data_skip:
            for epoch in range(epochs_trained):
                is_random_sampler = hasattr(train_dataloader, "sampler") and isinstance(
                    train_dataloader.sampler, RandomSampler
                )
                if is_torch_less_than_1_11 or not is_random_sampler:
                    # We just need to begin an iteration to create the randomization of the sampler.
                    # That was before PyTorch 1.11 however...
                    for _ in train_dataloader:
                        break
                else:
                    # Otherwise we need to call the whooooole sampler cause there is some random operation added
                    # AT THE VERY END!
                    _ = list(train_dataloader.sampler)

        total_batched_samples = 0
        for epoch in range(epochs_trained, num_train_epochs):
            if isinstance(train_dataloader, DataLoader) and isinstance(train_dataloader.sampler, DistributedSampler):
                train_dataloader.sampler.set_epoch(epoch)
            elif hasattr(train_dataloader, "dataset") and isinstance(train_dataloader.dataset, IterableDatasetShard):
                train_dataloader.dataset.set_epoch(epoch)

            if is_torch_tpu_available():
                parallel_loader = pl.ParallelLoader(train_dataloader, [args.device]).per_device_loader(args.device)
                epoch_iterator = parallel_loader
            else:
                epoch_iterator = train_dataloader

            # Reset the past mems state at the beginning of each epoch if necessary.
            if args.past_index >= 0:
                self._past = None

            steps_in_epoch = (
                len(epoch_iterator)
                if len_dataloader is not None
                else args.max_steps * args.gradient_accumulation_steps
            )
            self.control = self.callback_handler.on_epoch_begin(args, self.state, self.control)

            if epoch == epochs_trained and resume_from_checkpoint is not None and steps_trained_in_current_epoch == 0:
                self._load_rng_state(resume_from_checkpoint)

            rng_to_sync = False
            steps_skipped = 0
            if skip_first_batches is not None and steps_trained_in_current_epoch > 0:
                epoch_iterator = skip_first_batches(epoch_iterator, steps_trained_in_current_epoch)
                steps_skipped = steps_trained_in_current_epoch
                steps_trained_in_current_epoch = 0
                rng_to_sync = True

            step = -1
            for step, inputs in enumerate(epoch_iterator):
                total_batched_samples += 1
                if rng_to_sync:
                    self._load_rng_state(resume_from_checkpoint)
                    rng_to_sync = False

                # Skip past any already trained steps if resuming training
                if steps_trained_in_current_epoch > 0:
                    steps_trained_in_current_epoch -= 1
                    if steps_trained_progress_bar is not None:
                        steps_trained_progress_bar.update(1)
                    if steps_trained_in_current_epoch == 0:
                        self._load_rng_state(resume_from_checkpoint)
                    continue
                elif steps_trained_progress_bar is not None:
                    steps_trained_progress_bar.close()
                    steps_trained_progress_bar = None

                if step % args.gradient_accumulation_steps == 0:
                    self.control = self.callback_handler.on_step_begin(args, self.state, self.control)

                if (
                    (total_batched_samples % args.gradient_accumulation_steps != 0)
                    and args.local_rank != -1
                    and args._no_sync_in_gradient_accumulation
                ):
                    # Avoid unnecessary DDP synchronization since there will be no backward pass on this example.
                    with model.no_sync():
                        tr_loss_step = self.training_step(model, inputs)
                else:
                    tr_loss_step = self.training_step(model, inputs)

                if (
                    args.logging_nan_inf_filter
                    and not is_torch_tpu_available()
                    and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step))
                ):
                    # if loss is nan or inf simply add the average of previous logged losses
                    tr_loss += tr_loss / (1 + self.state.global_step - self._globalstep_last_logged)
                else:
                    tr_loss += tr_loss_step

                self.current_flos += float(self.floating_point_ops(inputs))

                # Optimizer step for deepspeed must be called on every step regardless of the value of gradient_accumulation_steps
                if self.deepspeed:
                    self.deepspeed.step()

                if total_batched_samples % args.gradient_accumulation_steps == 0 or (
                    # last step in epoch but step is always smaller than gradient_accumulation_steps
                    steps_in_epoch <= args.gradient_accumulation_steps
                    and (step + 1) == steps_in_epoch
                ):
                    # Gradient clipping
                    if args.max_grad_norm is not None and args.max_grad_norm > 0 and not self.deepspeed:
                        # deepspeed does its own clipping

                        if self.do_grad_scaling:
                            # Reduce gradients first for XLA
                            if is_torch_tpu_available():
                                gradients = xm._fetch_gradients(self.optimizer)
                                xm.all_reduce("sum", gradients, scale=1.0 / xm.xrt_world_size())
                            # AMP: gradients need unscaling
                            self.scaler.unscale_(self.optimizer)

                        if is_sagemaker_mp_enabled() and args.fp16:
                            self.optimizer.clip_master_grads(args.max_grad_norm)
                        elif hasattr(self.optimizer, "clip_grad_norm"):
                            # Some optimizers (like the sharded optimizer) have a specific way to do gradient clipping
                            self.optimizer.clip_grad_norm(args.max_grad_norm)
                        elif hasattr(model, "clip_grad_norm_"):
                            # Some models (like FullyShardedDDP) have a specific way to do gradient clipping
                            model.clip_grad_norm_(args.max_grad_norm)
                        else:
                            # Revert to normal clipping otherwise, handling Apex or full precision
                            nn.utils.clip_grad_norm_(
                                amp.master_params(self.optimizer) if self.use_apex else model.parameters(),
                                args.max_grad_norm,
                            )

                    # Optimizer step
                    optimizer_was_run = True
                    if self.deepspeed:
                        pass  # called outside the loop
                    elif is_torch_tpu_available():
                        if self.do_grad_scaling:
                            self.scaler.step(self.optimizer)
                            self.scaler.update()
                        else:
                            xm.optimizer_step(self.optimizer)
                    elif self.do_grad_scaling:
                        scale_before = self.scaler.get_scale()
                        self.scaler.step(self.optimizer)
                        self.scaler.update()
                        scale_after = self.scaler.get_scale()
                        optimizer_was_run = scale_before <= scale_after
                    else:
                        self.optimizer.step()

                    if optimizer_was_run and not self.deepspeed:
                        self.lr_scheduler.step()

                    model.zero_grad()
                    self.state.global_step += 1
                    self.state.epoch = epoch + (step + 1 + steps_skipped) / steps_in_epoch
                    self.control = self.callback_handler.on_step_end(args, self.state, self.control)

                    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
                else:
                    self.control = self.callback_handler.on_substep_end(args, self.state, self.control)

                if self.control.should_epoch_stop or self.control.should_training_stop:
                    break
            if step < 0:
                logger.warning(
                    "There seems to be not a single sample in your epoch_iterator, stopping training at step"
                    f" {self.state.global_step}! This is expected if you're using an IterableDataset and set"
                    f" num_steps ({max_steps}) higher than the number of available samples."
                )
                self.control.should_training_stop = True

            self.control = self.callback_handler.on_epoch_end(args, self.state, self.control)
            self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)

            if DebugOption.TPU_METRICS_DEBUG in self.args.debug:
                if is_torch_tpu_available():
                    # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
                    xm.master_print(met.metrics_report())
                else:
                    logger.warning(
                        "You enabled PyTorch/XLA debug metrics but you don't have a TPU "
                        "configured. Check your training configuration if this is unexpected."
                    )
            if self.control.should_training_stop:
                break

        if args.past_index and hasattr(self, "_past"):
            # Clean the state at the end of training
            delattr(self, "_past")

        logger.info("\n\nTraining completed. Do not forget to share your model on huggingface.co/models =)\n\n")
        if args.load_best_model_at_end and self.state.best_model_checkpoint is not None:
            # Wait for everyone to get here so we are sur the model has been saved by process 0.
            if is_torch_tpu_available():
                xm.rendezvous("load_best_model_at_end")
            elif args.local_rank != -1:
                dist.barrier()
            elif is_sagemaker_mp_enabled():
                smp.barrier()

            self._load_best_model()

        # add remaining tr_loss
        self._total_loss_scalar += tr_loss.item()
        train_loss = self._total_loss_scalar / self.state.global_step

        metrics = speed_metrics("train", start_time, num_samples=num_train_samples, num_steps=self.state.max_steps)
        self.store_flos()
        metrics["total_flos"] = self.state.total_flos
        metrics["train_loss"] = train_loss

        self.is_in_train = False

        self._memory_tracker.stop_and_update_metrics(metrics)

        self.log(metrics)

        run_dir = self._get_output_dir(trial)
        checkpoints_sorted = self._sorted_checkpoints(use_mtime=False, output_dir=run_dir)

        # Delete the last checkpoint when save_total_limit=1 if it's different from the best checkpoint and process allowed to save.
        if self.args.should_save and self.state.best_model_checkpoint is not None and self.args.save_total_limit == 1:
            for checkpoint in checkpoints_sorted:
                if checkpoint != self.state.best_model_checkpoint:
                    logger.info(f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit")
                    shutil.rmtree(checkpoint)

        self.control = self.callback_handler.on_train_end(args, self.state, self.control)

        return TrainOutput(self.state.global_step, train_loss, metrics)

    def _get_output_dir(self, trial):
        if self.hp_search_backend is not None and trial is not None:
            if self.hp_search_backend == HPSearchBackend.OPTUNA:
                run_id = trial.number
            elif self.hp_search_backend == HPSearchBackend.RAY:
                from ray import tune

                run_id = tune.get_trial_id()
            elif self.hp_search_backend == HPSearchBackend.SIGOPT:
                run_id = trial.id
            elif self.hp_search_backend == HPSearchBackend.WANDB:
                import wandb

                run_id = wandb.run.id
            run_name = self.hp_name(trial) if self.hp_name is not None else f"run-{run_id}"
            run_dir = os.path.join(self.args.output_dir, run_name)
        else:
            run_dir = self.args.output_dir
        return run_dir

    def _load_from_checkpoint(self, resume_from_checkpoint, model=None):
        if model is None:
            model = self.model

        if not os.path.isfile(os.path.join(resume_from_checkpoint, WEIGHTS_NAME)) and not os.path.isfile(
            os.path.join(resume_from_checkpoint, WEIGHTS_INDEX_NAME)
        ):
            raise ValueError(f"Can't find a valid checkpoint at {resume_from_checkpoint}")

        logger.info(f"Loading model from {resume_from_checkpoint}.")

        if os.path.isfile(os.path.join(resume_from_checkpoint, CONFIG_NAME)):
            config = PretrainedConfig.from_json_file(os.path.join(resume_from_checkpoint, CONFIG_NAME))
            checkpoint_version = config.transformers_version
            if checkpoint_version is not None and checkpoint_version != __version__:
                logger.warning(
                    f"You are resuming training from a checkpoint trained with {checkpoint_version} of "
                    f"Transformers but your current version is {__version__}. This is not recommended and could "
                    "yield to errors or unwanted behaviors."
                )

        if os.path.isfile(os.path.join(resume_from_checkpoint, WEIGHTS_NAME)):
            # If the model is on the GPU, it still works!
            if is_sagemaker_mp_enabled():
                if os.path.isfile(os.path.join(resume_from_checkpoint, "user_content.pt")):
                    # If the 'user_content.pt' file exists, load with the new smp api.
                    # Checkpoint must have been saved with the new smp api.
                    smp.resume_from_checkpoint(
                        path=resume_from_checkpoint, tag=WEIGHTS_NAME, partial=False, load_optimizer=False
                    )
                else:
                    # If the 'user_content.pt' file does NOT exist, load with the old smp api.
                    # Checkpoint must have been saved with the old smp api.
                    if hasattr(self.args, "fp16") and self.args.fp16 is True:
                        logger.warning(
                            "Enabling FP16 and loading from smp < 1.10 checkpoint together is not suppported."
                        )
                    state_dict = torch.load(os.path.join(resume_from_checkpoint, WEIGHTS_NAME), map_location="cpu")
                    # Required for smp to not auto-translate state_dict from hf to smp (is already smp).
                    state_dict["_smp_is_partial"] = False
                    load_result = model.load_state_dict(state_dict, strict=True)
                    # release memory
                    del state_dict
            else:
                # We load the model state dict on the CPU to avoid an OOM error.
                state_dict = torch.load(os.path.join(resume_from_checkpoint, WEIGHTS_NAME), map_location="cpu")
                # workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963
                # which takes *args instead of **kwargs
                load_result = model.load_state_dict(state_dict, False)
                # release memory
                del state_dict
                self._issue_warnings_after_load(load_result)
        else:
            # We load the sharded checkpoint
            load_result = load_sharded_checkpoint(model, resume_from_checkpoint, strict=is_sagemaker_mp_enabled())
            if not is_sagemaker_mp_enabled():
                self._issue_warnings_after_load(load_result)

    def _load_best_model(self):
        logger.info(f"Loading best model from {self.state.best_model_checkpoint} (score: {self.state.best_metric}).")
        best_model_path = os.path.join(self.state.best_model_checkpoint, WEIGHTS_NAME)
        model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
        if os.path.exists(best_model_path):
            if self.deepspeed:
                if self.model_wrapped is not None:
                    # this removes the pre-hooks from the previous engine
                    self.model_wrapped.destroy()
                    self.model_wrapped = None

                # temp hack until Deepspeed fixes the problem with resume from an existing engine that did some stepping
                deepspeed_engine, optimizer, lr_scheduler = deepspeed_init(
                    self,
                    num_training_steps=self.args.max_steps,
                    resume_from_checkpoint=self.state.best_model_checkpoint,
                )
                self.model = deepspeed_engine.module
                self.model_wrapped = deepspeed_engine
                self.deepspeed = deepspeed_engine
                self.optimizer = optimizer
                self.lr_scheduler = lr_scheduler
            else:
                if is_sagemaker_mp_enabled():
                    if os.path.isfile(os.path.join(self.state.best_model_checkpoint, "user_content.pt")):
                        # If the 'user_content.pt' file exists, load with the new smp api.
                        # Checkpoint must have been saved with the new smp api.
                        smp.resume_from_checkpoint(
                            path=self.state.best_model_checkpoint,
                            tag=WEIGHTS_NAME,
                            partial=False,
                            load_optimizer=False,
                        )
                    else:
                        # If the 'user_content.pt' file does NOT exist, load with the old smp api.
                        # Checkpoint must have been saved with the old smp api.
                        state_dict = torch.load(best_model_path, map_location="cpu")
                        state_dict["_smp_is_partial"] = False
                        load_result = model.load_state_dict(state_dict, strict=True)
                else:
                    # We load the model state dict on the CPU to avoid an OOM error.
                    state_dict = torch.load(best_model_path, map_location="cpu")
                    # If the model is on the GPU, it still works!
                    # workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963
                    # which takes *args instead of **kwargs
                    load_result = model.load_state_dict(state_dict, False)
                if not is_sagemaker_mp_enabled():
                    self._issue_warnings_after_load(load_result)
        elif os.path.exists(os.path.join(self.state.best_model_checkpoint, WEIGHTS_INDEX_NAME)):
            load_result = load_sharded_checkpoint(
                model, self.state.best_model_checkpoint, strict=is_sagemaker_mp_enabled()
            )
            if not is_sagemaker_mp_enabled():
                self._issue_warnings_after_load(load_result)
        else:
            logger.warning(
                f"Could not locate the best model at {best_model_path}, if you are running a distributed training "
                "on multiple nodes, you should activate `--save_on_each_node`."
            )

    def _issue_warnings_after_load(self, load_result):
        if len(load_result.missing_keys) != 0:
            if self.model._keys_to_ignore_on_save is not None and set(load_result.missing_keys) == set(
                self.model._keys_to_ignore_on_save
            ):
                self.model.tie_weights()
            else:
                logger.warning(f"There were missing keys in the checkpoint model loaded: {load_result.missing_keys}.")
        if len(load_result.unexpected_keys) != 0:
            logger.warning(
                f"There were unexpected keys in the checkpoint model loaded: {load_result.unexpected_keys}."
            )

    def _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch, ignore_keys_for_eval):
        if self.control.should_log:
            if is_torch_tpu_available():
                xm.mark_step()

            logs: Dict[str, float] = {}

            # all_gather + mean() to get average loss over all processes
            tr_loss_scalar = self._nested_gather(tr_loss).mean().item()

            # reset tr_loss to zero
            tr_loss -= tr_loss

            logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4)
            logs["learning_rate"] = self._get_learning_rate()

            self._total_loss_scalar += tr_loss_scalar
            self._globalstep_last_logged = self.state.global_step
            self.store_flos()

            self.log(logs)

        metrics = None
        if self.control.should_evaluate:
            if isinstance(self.eval_dataset, dict):
                for eval_dataset_name, eval_dataset in self.eval_dataset.items():
                    metrics = self.evaluate(
                        eval_dataset=eval_dataset,
                        ignore_keys=ignore_keys_for_eval,
                        metric_key_prefix=f"eval_{eval_dataset_name}",
                    )
            else:
                metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
            self._report_to_hp_search(trial, self.state.global_step, metrics)

        if self.control.should_save:
            self._save_checkpoint(model, trial, metrics=metrics)
            self.control = self.callback_handler.on_save(self.args, self.state, self.control)

    def _load_rng_state(self, checkpoint):
        # Load RNG states from `checkpoint`
        if checkpoint is None:
            return

        if self.args.world_size > 1:
            process_index = self.args.process_index
            rng_file = os.path.join(checkpoint, f"rng_state_{process_index}.pth")
            if not os.path.isfile(rng_file):
                logger.info(
                    f"Didn't find an RNG file for process {process_index}, if you are resuming a training that "
                    "wasn't launched in a distributed fashion, reproducibility is not guaranteed."
                )
                return
        else:
            rng_file = os.path.join(checkpoint, "rng_state.pth")
            if not os.path.isfile(rng_file):
                logger.info(
                    "Didn't find an RNG file, if you are resuming a training that was launched in a distributed "
                    "fashion, reproducibility is not guaranteed."
                )
                return

        checkpoint_rng_state = torch.load(rng_file)
        random.setstate(checkpoint_rng_state["python"])
        np.random.set_state(checkpoint_rng_state["numpy"])
        torch.random.set_rng_state(checkpoint_rng_state["cpu"])
        if torch.cuda.is_available():
            if self.args.local_rank != -1:
                torch.cuda.random.set_rng_state(checkpoint_rng_state["cuda"])
            else:
                try:
                    torch.cuda.random.set_rng_state_all(checkpoint_rng_state["cuda"])
                except Exception as e:
                    logger.info(
                        f"Didn't manage to set back the RNG states of the GPU because of the following error:\n {e}"
                        "\nThis won't yield the same results as if the training had not been interrupted."
                    )
        if is_torch_tpu_available():
            xm.set_rng_state(checkpoint_rng_state["xla"])

    def _save_checkpoint(self, model, trial, metrics=None):
        # In all cases, including ddp/dp/deepspeed, self.model is always a reference to the model we
        # want to save except FullyShardedDDP.
        # assert unwrap_model(model) is self.model, "internal model should be a reference to self.model"

        # Save model checkpoint
        checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"

        if self.hp_search_backend is None and trial is None:
            self.store_flos()

        run_dir = self._get_output_dir(trial=trial)
        output_dir = os.path.join(run_dir, checkpoint_folder)
        self.save_model(output_dir, _internal_call=True)
        if self.deepspeed:
            # under zero3 model file itself doesn't get saved since it's bogus! Unless deepspeed
            # config `stage3_gather_16bit_weights_on_model_save` is True
            self.deepspeed.save_checkpoint(output_dir)

        # Save optimizer and scheduler
        if self.sharded_ddp == ShardedDDPOption.SIMPLE:
            self.optimizer.consolidate_state_dict()

        if is_torch_tpu_available():
            xm.rendezvous("saving_optimizer_states")
            xm.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))
            with warnings.catch_warnings(record=True) as caught_warnings:
                xm.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
                reissue_pt_warnings(caught_warnings)
        elif is_sagemaker_mp_enabled():
            opt_state_dict = self.optimizer.local_state_dict(gather_if_shard=False)
            smp.barrier()
            if smp.rdp_rank() == 0 or smp.state.cfg.shard_optimizer_state:
                smp.save(
                    opt_state_dict,
                    os.path.join(output_dir, OPTIMIZER_NAME),
                    partial=True,
                    v3=smp.state.cfg.shard_optimizer_state,
                )
            if self.args.should_save:
                with warnings.catch_warnings(record=True) as caught_warnings:
                    torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
                reissue_pt_warnings(caught_warnings)
                if self.do_grad_scaling:
                    torch.save(self.scaler.state_dict(), os.path.join(output_dir, SCALER_NAME))
        elif self.args.should_save and not self.deepspeed:
            # deepspeed.save_checkpoint above saves model/optim/sched
            torch.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))
            with warnings.catch_warnings(record=True) as caught_warnings:
                torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
            reissue_pt_warnings(caught_warnings)
            if self.do_grad_scaling:
                torch.save(self.scaler.state_dict(), os.path.join(output_dir, SCALER_NAME))

        # Determine the new best metric / best model checkpoint
        if metrics is not None and self.args.metric_for_best_model is not None:
            metric_to_check = self.args.metric_for_best_model
            if not metric_to_check.startswith("eval_"):
                metric_to_check = f"eval_{metric_to_check}"
            metric_value = metrics[metric_to_check]

            operator = np.greater if self.args.greater_is_better else np.less
            if (
                self.state.best_metric is None
                or self.state.best_model_checkpoint is None
                or operator(metric_value, self.state.best_metric)
            ):
                self.state.best_metric = metric_value
                self.state.best_model_checkpoint = output_dir

        # Save the Trainer state
        if self.args.should_save:
            self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))

        # Save RNG state in non-distributed training
        rng_states = {
            "python": random.getstate(),
            "numpy": np.random.get_state(),
            "cpu": torch.random.get_rng_state(),
        }
        if torch.cuda.is_available():
            if self.args.local_rank == -1:
                # In non distributed, we save the global CUDA RNG state (will take care of DataParallel)
                rng_states["cuda"] = torch.cuda.random.get_rng_state_all()
            else:
                rng_states["cuda"] = torch.cuda.random.get_rng_state()

        if is_torch_tpu_available():
            rng_states["xla"] = xm.get_rng_state()

        # A process can arrive here before the process 0 has a chance to save the model, in which case output_dir may
        # not yet exist.
        os.makedirs(output_dir, exist_ok=True)

        if self.args.world_size <= 1:
            torch.save(rng_states, os.path.join(output_dir, "rng_state.pth"))
        else:
            torch.save(rng_states, os.path.join(output_dir, f"rng_state_{self.args.process_index}.pth"))

        if self.args.push_to_hub:
            self._push_from_checkpoint(output_dir)

        # Maybe delete some older checkpoints.
        if self.args.should_save:
            self._rotate_checkpoints(use_mtime=True, output_dir=run_dir)

    def _load_optimizer_and_scheduler(self, checkpoint):
        """If optimizer and scheduler states exist, load them."""
        if checkpoint is None:
            return

        if self.deepspeed:
            # deepspeed loads optimizer/lr_scheduler together with the model in deepspeed_init
            return

        checkpoint_file_exists = (
            glob.glob(os.path.join(checkpoint, OPTIMIZER_NAME) + "_*")
            if is_sagemaker_mp_enabled()
            else os.path.isfile(os.path.join(checkpoint, OPTIMIZER_NAME))
        )
        if checkpoint_file_exists and os.path.isfile(os.path.join(checkpoint, SCHEDULER_NAME)):
            # Load in optimizer and scheduler states
            if is_torch_tpu_available():
                # On TPU we have to take some extra precautions to properly load the states on the right device.
                optimizer_state = torch.load(os.path.join(checkpoint, OPTIMIZER_NAME), map_location="cpu")
                with warnings.catch_warnings(record=True) as caught_warnings:
                    lr_scheduler_state = torch.load(os.path.join(checkpoint, SCHEDULER_NAME), map_location="cpu")
                reissue_pt_warnings(caught_warnings)

                xm.send_cpu_data_to_device(optimizer_state, self.args.device)
                xm.send_cpu_data_to_device(lr_scheduler_state, self.args.device)

                self.optimizer.load_state_dict(optimizer_state)
                self.lr_scheduler.load_state_dict(lr_scheduler_state)
            else:
                map_location = "cpu" if is_sagemaker_mp_enabled() else self.args.device
                if is_sagemaker_mp_enabled():
                    if os.path.isfile(os.path.join(checkpoint, "user_content.pt")):
                        # Optimizer checkpoint was saved with smp >= 1.10
                        def opt_load_hook(mod, opt):
                            opt.load_state_dict(smp.load(os.path.join(checkpoint, OPTIMIZER_NAME), partial=True))

                    else:
                        # Optimizer checkpoint was saved with smp < 1.10
                        def opt_load_hook(mod, opt):
                            if IS_SAGEMAKER_MP_POST_1_10:
                                opt.load_state_dict(
                                    smp.load(os.path.join(checkpoint, OPTIMIZER_NAME), partial=True, back_compat=True)
                                )
                            else:
                                opt.load_state_dict(smp.load(os.path.join(checkpoint, OPTIMIZER_NAME), partial=True))

                    self.model_wrapped.register_post_step_hook(opt_load_hook)
                else:
                    self.optimizer.load_state_dict(
                        torch.load(os.path.join(checkpoint, OPTIMIZER_NAME), map_location=map_location)
                    )
                with warnings.catch_warnings(record=True) as caught_warnings:
                    self.lr_scheduler.load_state_dict(torch.load(os.path.join(checkpoint, SCHEDULER_NAME)))
                reissue_pt_warnings(caught_warnings)
                if self.do_grad_scaling and os.path.isfile(os.path.join(checkpoint, SCALER_NAME)):
                    self.scaler.load_state_dict(torch.load(os.path.join(checkpoint, SCALER_NAME)))

    def hyperparameter_search(
        self,
        hp_space: Optional[Callable[["optuna.Trial"], Dict[str, float]]] = None,
        compute_objective: Optional[Callable[[Dict[str, float]], float]] = None,
        n_trials: int = 20,
        direction: str = "minimize",
        backend: Optional[Union["str", HPSearchBackend]] = None,
        hp_name: Optional[Callable[["optuna.Trial"], str]] = None,
        **kwargs,
    ) -> BestRun:
        """
        Launch an hyperparameter search using `optuna` or `Ray Tune` or `SigOpt`. The optimized quantity is determined
        by `compute_objective`, which defaults to a function returning the evaluation loss when no metric is provided,
        the sum of all metrics otherwise.

        <Tip warning={true}>

        To use this method, you need to have provided a `model_init` when initializing your [`Trainer`]: we need to
        reinitialize the model at each new run. This is incompatible with the `optimizers` argument, so you need to
        subclass [`Trainer`] and override the method [`~Trainer.create_optimizer_and_scheduler`] for custom
        optimizer/scheduler.

        </Tip>

        Args:
            hp_space (`Callable[["optuna.Trial"], Dict[str, float]]`, *optional*):
                A function that defines the hyperparameter search space. Will default to
                [`~trainer_utils.default_hp_space_optuna`] or [`~trainer_utils.default_hp_space_ray`] or
                [`~trainer_utils.default_hp_space_sigopt`] depending on your backend.
            compute_objective (`Callable[[Dict[str, float]], float]`, *optional*):
                A function computing the objective to minimize or maximize from the metrics returned by the `evaluate`
                method. Will default to [`~trainer_utils.default_compute_objective`].
            n_trials (`int`, *optional*, defaults to 100):
                The number of trial runs to test.
            direction (`str`, *optional*, defaults to `"minimize"`):
                Whether to optimize greater or lower objects. Can be `"minimize"` or `"maximize"`, you should pick
                `"minimize"` when optimizing the validation loss, `"maximize"` when optimizing one or several metrics.
            backend (`str` or [`~training_utils.HPSearchBackend`], *optional*):
                The backend to use for hyperparameter search. Will default to optuna or Ray Tune or SigOpt, depending
                on which one is installed. If all are installed, will default to optuna.
            hp_name (`Callable[["optuna.Trial"], str]]`, *optional*):
                A function that defines the trial/run name. Will default to None.
            kwargs (`Dict[str, Any]`, *optional*):
                Additional keyword arguments passed along to `optuna.create_study` or `ray.tune.run`. For more
                information see:

                - the documentation of
                  [optuna.create_study](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.create_study.html)
                - the documentation of [tune.run](https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run)
                - the documentation of [sigopt](https://app.sigopt.com/docs/endpoints/experiments/create)

        Returns:
            [`trainer_utils.BestRun`]: All the information about the best run. Experiment summary can be found in
            `run_summary` attribute for Ray backend.
        """
        if backend is None:
            backend = default_hp_search_backend()
            if backend is None:
                raise RuntimeError(
                    "At least one of optuna or ray should be installed. "
                    "To install optuna run `pip install optuna`. "
                    "To install ray run `pip install ray[tune]`. "
                    "To install sigopt run `pip install sigopt`."
                )
        backend = HPSearchBackend(backend)
        if backend == HPSearchBackend.OPTUNA and not is_optuna_available():
            raise RuntimeError("You picked the optuna backend, but it is not installed. Use `pip install optuna`.")
        if backend == HPSearchBackend.RAY and not is_ray_tune_available():
            raise RuntimeError(
                "You picked the Ray Tune backend, but it is not installed. Use `pip install 'ray[tune]'`."
            )
        if backend == HPSearchBackend.SIGOPT and not is_sigopt_available():
            raise RuntimeError("You picked the sigopt backend, but it is not installed. Use `pip install sigopt`.")
        if backend == HPSearchBackend.WANDB and not is_wandb_available():
            raise RuntimeError("You picked the wandb backend, but it is not installed. Use `pip install wandb`.")
        self.hp_search_backend = backend
        if self.model_init is None:
            raise RuntimeError(
                "To use hyperparameter search, you need to pass your model through a model_init function."
            )

        self.hp_space = default_hp_space[backend] if hp_space is None else hp_space
        self.hp_name = hp_name
        self.compute_objective = default_compute_objective if compute_objective is None else compute_objective

        backend_dict = {
            HPSearchBackend.OPTUNA: run_hp_search_optuna,
            HPSearchBackend.RAY: run_hp_search_ray,
            HPSearchBackend.SIGOPT: run_hp_search_sigopt,
            HPSearchBackend.WANDB: run_hp_search_wandb,
        }
        best_run = backend_dict[backend](self, n_trials, direction, **kwargs)

        self.hp_search_backend = None
        return best_run

    def log(self, logs: Dict[str, float]) -> None:
        """
        Log `logs` on the various objects watching training.

        Subclass and override this method to inject custom behavior.

        Args:
            logs (`Dict[str, float]`):
                The values to log.
        """
        if self.state.epoch is not None:
            logs["epoch"] = round(self.state.epoch, 2)

        output = {**logs, **{"step": self.state.global_step}}
        self.state.log_history.append(output)
        self.control = self.callback_handler.on_log(self.args, self.state, self.control, logs)

    def _prepare_input(self, data: Union[torch.Tensor, Any]) -> Union[torch.Tensor, Any]:
        """
        Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors.
        """
        if isinstance(data, Mapping):
            return type(data)({k: self._prepare_input(v) for k, v in data.items()})
        elif isinstance(data, (tuple, list)):
            return type(data)(self._prepare_input(v) for v in data)
        elif isinstance(data, torch.Tensor):
            kwargs = {"device": self.args.device}
            if self.deepspeed and (torch.is_floating_point(data) or torch.is_complex(data)):
                # NLP models inputs are int/uint and those get adjusted to the right dtype of the
                # embedding. Other models such as wav2vec2's inputs are already float and thus
                # may need special handling to match the dtypes of the model
                kwargs.update({"dtype": self.args.hf_deepspeed_config.dtype()})
            return data.to(**kwargs)
        return data

    def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]:
        """
        Prepare `inputs` before feeding them to the model, converting them to tensors if they are not already and
        handling potential state.
        """
        inputs = self._prepare_input(inputs)
        if len(inputs) == 0:
            raise ValueError(
                "The batch received was empty, your model won't be able to train on it. Double-check that your "
                f"training dataset contains keys expected by the model: {','.join(self._signature_columns)}."
            )
        if self.args.past_index >= 0 and self._past is not None:
            inputs["mems"] = self._past

        return inputs

    def compute_loss_context_manager(self):
        """
        A helper wrapper to group together context managers.
        """
        return self.autocast_smart_context_manager()

    def autocast_smart_context_manager(self, cache_enabled: Optional[bool] = True):
        """
        A helper wrapper that creates an appropriate context manager for `autocast` while feeding it the desired
        arguments, depending on the situation.
        """
        if self.use_cuda_amp or self.use_cpu_amp:
            if is_torch_greater_or_equal_than_1_10:
                ctx_manager = (
                    torch.cpu.amp.autocast(cache_enabled=cache_enabled, dtype=self.amp_dtype)
                    if self.use_cpu_amp
                    else torch.cuda.amp.autocast(cache_enabled=cache_enabled, dtype=self.amp_dtype)
                )
            else:
                ctx_manager = torch.cuda.amp.autocast()
        else:
            ctx_manager = contextlib.nullcontext() if sys.version_info >= (3, 7) else contextlib.suppress()

        return ctx_manager

    def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
        """
        Perform a training step on a batch of inputs.

        Subclass and override to inject custom behavior.

        Args:
            model (`nn.Module`):
                The model to train.
            inputs (`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument `labels`. Check your model's documentation for all accepted arguments.

        Return:
            `torch.Tensor`: The tensor with training loss on this batch.
        """
        model.train()
        inputs = self._prepare_inputs(inputs)

        if is_sagemaker_mp_enabled():
            loss_mb = smp_forward_backward(model, inputs, self.args.gradient_accumulation_steps)
            return loss_mb.reduce_mean().detach().to(self.args.device)

        with self.compute_loss_context_manager():
            loss = self.compute_loss(model, inputs)

        if self.args.n_gpu > 1:
            loss = loss.mean()  # mean() to average on multi-gpu parallel training

        if self.args.gradient_accumulation_steps > 1 and not self.deepspeed:
            # deepspeed handles loss scaling by gradient_accumulation_steps in its `backward`
            loss = loss / self.args.gradient_accumulation_steps

        if self.do_grad_scaling:
            self.scaler.scale(loss).backward()
        elif self.use_apex:
            with amp.scale_loss(loss, self.optimizer) as scaled_loss:
                scaled_loss.backward()
        elif self.deepspeed:
            # loss gets scaled under gradient_accumulation_steps in deepspeed
            loss = self.deepspeed.backward(loss)
        else:
            loss.backward()

        return loss.detach()

    def compute_loss(self, model, inputs, return_outputs=False):
        """
        How the loss is computed by Trainer. By default, all models return the loss in the first element.

        Subclass and override for custom behavior.
        """
        if self.label_smoother is not None and "labels" in inputs:
            labels = inputs.pop("labels")
        else:
            labels = None
        outputs = model(**inputs)
        # Save past state if it exists
        # TODO: this needs to be fixed and made cleaner later.
        if self.args.past_index >= 0:
            self._past = outputs[self.args.past_index]

        if labels is not None:
            if unwrap_model(model)._get_name() in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values():
                loss = self.label_smoother(outputs, labels, shift_labels=True)
            else:
                loss = self.label_smoother(outputs, labels)
        else:
            if isinstance(outputs, dict) and "loss" not in outputs:
                raise ValueError(
                    "The model did not return a loss from the inputs, only the following keys: "
                    f"{','.join(outputs.keys())}. For reference, the inputs it received are {','.join(inputs.keys())}."
                )
            # We don't use .loss here since the model may return tuples instead of ModelOutput.
            loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]

        return (loss, outputs) if return_outputs else loss

    def is_local_process_zero(self) -> bool:
        """
        Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
        machines) main process.
        """
        return self.args.local_process_index == 0

    def is_world_process_zero(self) -> bool:
        """
        Whether or not this process is the global main process (when training in a distributed fashion on several
        machines, this is only going to be `True` for one process).
        """
        # Special case for SageMaker ModelParallel since there process_index is dp_process_index, not the global
        # process index.
        if is_sagemaker_mp_enabled():
            return smp.rank() == 0
        else:
            return self.args.process_index == 0

    def save_model(self, output_dir: Optional[str] = None, _internal_call: bool = False):
        """
        Will save the model, so you can reload it using `from_pretrained()`.

        Will only save from the main process.
        """

        if output_dir is None:
            output_dir = self.args.output_dir

        if is_torch_tpu_available():
            self._save_tpu(output_dir)
        elif is_sagemaker_mp_enabled():
            # Calling the state_dict needs to be done on the wrapped model and on all processes.
            os.makedirs(output_dir, exist_ok=True)
            state_dict = self.model_wrapped.state_dict()
            if self.args.should_save:
                self._save(output_dir, state_dict=state_dict)
            if IS_SAGEMAKER_MP_POST_1_10:
                # 'user_content.pt' indicates model state_dict saved with smp >= 1.10
                Path(os.path.join(output_dir, "user_content.pt")).touch()
        elif (
            ShardedDDPOption.ZERO_DP_2 in self.args.sharded_ddp
            or ShardedDDPOption.ZERO_DP_3 in self.args.sharded_ddp
            or self.fsdp is not None
        ):
            state_dict = self.model.state_dict()

            if self.args.should_save:
                self._save(output_dir, state_dict=state_dict)
        elif self.deepspeed:
            # this takes care of everything as long as we aren't under zero3
            if self.args.should_save:
                self._save(output_dir)

            if is_deepspeed_zero3_enabled():
                # It's too complicated to try to override different places where the weights dump gets
                # saved, so since under zero3 the file is bogus, simply delete it. The user should
                # either user deepspeed checkpoint to resume or to recover full weights use
                # zero_to_fp32.py stored in the checkpoint.
                if self.args.should_save:
                    file = os.path.join(output_dir, WEIGHTS_NAME)
                    if os.path.isfile(file):
                        # logger.info(f"deepspeed zero3: removing {file}, see zero_to_fp32.py to recover weights")
                        os.remove(file)

                # now save the real model if stage3_gather_16bit_weights_on_model_save=True
                # if false it will not be saved.
                # This must be called on all ranks
                if not self.deepspeed.save_16bit_model(output_dir, WEIGHTS_NAME):
                    logger.warning(
                        "deepspeed.save_16bit_model didn't save the model, since"
                        " stage3_gather_16bit_weights_on_model_save=false. Saving the full checkpoint instead, use"
                        " zero_to_fp32.py to recover weights"
                    )
                    self.deepspeed.save_checkpoint(output_dir)

        elif self.args.should_save:
            self._save(output_dir)

        # Push to the Hub when `save_model` is called by the user.
        if self.args.push_to_hub and not _internal_call:
            self.push_to_hub(commit_message="Model save")

    def _save_tpu(self, output_dir: Optional[str] = None):
        output_dir = output_dir if output_dir is not None else self.args.output_dir
        logger.info(f"Saving model checkpoint to {output_dir}")

        if xm.is_master_ordinal():
            os.makedirs(output_dir, exist_ok=True)
            torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

        # Save a trained model and configuration using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        xm.rendezvous("saving_checkpoint")
        if not isinstance(self.model, PreTrainedModel):
            if isinstance(unwrap_model(self.model), PreTrainedModel):
                unwrap_model(self.model).save_pretrained(
                    output_dir,
                    is_main_process=self.args.should_save,
                    state_dict=self.model.state_dict(),
                    save_function=xm.save,
                )
            else:
                logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
                state_dict = self.model.state_dict()
                xm.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
        else:
            self.model.save_pretrained(output_dir, is_main_process=self.args.should_save, save_function=xm.save)
        if self.tokenizer is not None and self.args.should_save:
            self.tokenizer.save_pretrained(output_dir)

    def _save(self, output_dir: Optional[str] = None, state_dict=None):
        # If we are executing this function, we are the process zero, so we don't check for that.
        output_dir = output_dir if output_dir is not None else self.args.output_dir
        os.makedirs(output_dir, exist_ok=True)
        logger.info(f"Saving model checkpoint to {output_dir}")
        # Save a trained model and configuration using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        if not isinstance(self.model, PreTrainedModel):
            if isinstance(unwrap_model(self.model), PreTrainedModel):
                if state_dict is None:
                    state_dict = self.model.state_dict()
                unwrap_model(self.model).save_pretrained(output_dir, state_dict=filtered_state_dict)
            else:
                logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
                if state_dict is None:
                    state_dict = self.model.state_dict()
                torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
        else:
            if self.save_prefixencoder:
                print("Saving PrefixEncoder")
                state_dict = self.model.state_dict()
                filtered_state_dict = {}
                for k, v in self.model.named_parameters():
                    if v.requires_grad:
                        filtered_state_dict[k] = state_dict[k]
                self.model.save_pretrained(output_dir, state_dict=filtered_state_dict)
            else:
                print("Saving the whole model")
                self.model.save_pretrained(output_dir, state_dict=state_dict)
        if self.tokenizer is not None:
            self.tokenizer.save_pretrained(output_dir)

        # Good practice: save your training arguments together with the trained model
        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

    def store_flos(self):
        # Storing the number of floating-point operations that went into the model
        if self.args.local_rank != -1:
            self.state.total_flos += (
                distributed_broadcast_scalars([self.current_flos], device=self.args.device).sum().item()
            )
            self.current_flos = 0
        else:
            self.state.total_flos += self.current_flos
            self.current_flos = 0

    def _sorted_checkpoints(
        self, output_dir=None, checkpoint_prefix=PREFIX_CHECKPOINT_DIR, use_mtime=False
    ) -> List[str]:
        ordering_and_checkpoint_path = []

        glob_checkpoints = [str(x) for x in Path(output_dir).glob(f"{checkpoint_prefix}-*") if os.path.isdir(x)]

        for path in glob_checkpoints:
            if use_mtime:
                ordering_and_checkpoint_path.append((os.path.getmtime(path), path))
            else:
                regex_match = re.match(f".*{checkpoint_prefix}-([0-9]+)", path)
                if regex_match is not None and regex_match.groups() is not None:
                    ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))

        checkpoints_sorted = sorted(ordering_and_checkpoint_path)
        checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
        # Make sure we don't delete the best model.
        if self.state.best_model_checkpoint is not None:
            best_model_index = checkpoints_sorted.index(str(Path(self.state.best_model_checkpoint)))
            for i in range(best_model_index, len(checkpoints_sorted) - 2):
                checkpoints_sorted[i], checkpoints_sorted[i + 1] = checkpoints_sorted[i + 1], checkpoints_sorted[i]
        return checkpoints_sorted

    def _rotate_checkpoints(self, use_mtime=False, output_dir=None) -> None:
        if self.args.save_total_limit is None or self.args.save_total_limit <= 0:
            return

        # Check if we should delete older checkpoint(s)
        checkpoints_sorted = self._sorted_checkpoints(use_mtime=use_mtime, output_dir=output_dir)
        if len(checkpoints_sorted) <= self.args.save_total_limit:
            return

        # If save_total_limit=1 with load_best_model_at_end=True, we could end up deleting the last checkpoint, which
        # we don't do to allow resuming.
        save_total_limit = self.args.save_total_limit
        if (
            self.state.best_model_checkpoint is not None
            and self.args.save_total_limit == 1
            and checkpoints_sorted[-1] != self.state.best_model_checkpoint
        ):
            save_total_limit = 2

        number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - save_total_limit)
        checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
        for checkpoint in checkpoints_to_be_deleted:
            logger.info(f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit")
            shutil.rmtree(checkpoint, ignore_errors=True)

    def evaluate(
        self,
        eval_dataset: Optional[Dataset] = None,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "eval",
    ) -> Dict[str, float]:
        """
        Run evaluation and returns metrics.

        The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
        (pass it to the init `compute_metrics` argument).

        You can also subclass and override this method to inject custom behavior.

        Args:
            eval_dataset (`Dataset`, *optional*):
                Pass a dataset if you wish to override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns
                not accepted by the `model.forward()` method are automatically removed. It must implement the `__len__`
                method.
            ignore_keys (`Lst[str]`, *optional*):
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions.
            metric_key_prefix (`str`, *optional*, defaults to `"eval"`):
                An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
                "eval_bleu" if the prefix is "eval" (default)

        Returns:
            A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The
            dictionary also contains the epoch number which comes from the training state.
        """
        # memory metrics - must set up as early as possible
        self._memory_tracker.start()

        eval_dataloader = self.get_eval_dataloader(eval_dataset)
        start_time = time.time()

        eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
        output = eval_loop(
            eval_dataloader,
            description="Evaluation",
            # No point gathering the predictions if there are no metrics, otherwise we defer to
            # self.args.prediction_loss_only
            prediction_loss_only=True if self.compute_metrics is None else None,
            ignore_keys=ignore_keys,
            metric_key_prefix=metric_key_prefix,
        )

        total_batch_size = self.args.eval_batch_size * self.args.world_size
        if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
            start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
        output.metrics.update(
            speed_metrics(
                metric_key_prefix,
                start_time,
                num_samples=output.num_samples,
                num_steps=math.ceil(output.num_samples / total_batch_size),
            )
        )

        self.log(output.metrics)

        if DebugOption.TPU_METRICS_DEBUG in self.args.debug:
            # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
            xm.master_print(met.metrics_report())

        self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, output.metrics)

        self._memory_tracker.stop_and_update_metrics(output.metrics)

        return output.metrics

    def predict(
        self, test_dataset: Dataset, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = "test"
    ) -> PredictionOutput:
        """
        Run prediction and returns predictions and potential metrics.

        Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
        will also return metrics, like in `evaluate()`.

        Args:
            test_dataset (`Dataset`):
                Dataset to run the predictions on. If it is an `datasets.Dataset`, columns not accepted by the
                `model.forward()` method are automatically removed. Has to implement the method `__len__`
            ignore_keys (`Lst[str]`, *optional*):
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions.
            metric_key_prefix (`str`, *optional*, defaults to `"test"`):
                An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
                "test_bleu" if the prefix is "test" (default)

        <Tip>

        If your predictions or labels have different sequence length (for instance because you're doing dynamic padding
        in a token classification task) the predictions will be padded (on the right) to allow for concatenation into
        one array. The padding index is -100.

        </Tip>

        Returns: *NamedTuple* A namedtuple with the following keys:

            - predictions (`np.ndarray`): The predictions on `test_dataset`.
            - label_ids (`np.ndarray`, *optional*): The labels (if the dataset contained some).
            - metrics (`Dict[str, float]`, *optional*): The potential dictionary of metrics (if the dataset contained
              labels).
        """
        # memory metrics - must set up as early as possible
        self._memory_tracker.start()

        test_dataloader = self.get_test_dataloader(test_dataset)
        start_time = time.time()

        eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
        output = eval_loop(
            test_dataloader, description="Prediction", ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix
        )
        total_batch_size = self.args.eval_batch_size * self.args.world_size
        if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
            start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
        output.metrics.update(
            speed_metrics(
                metric_key_prefix,
                start_time,
                num_samples=output.num_samples,
                num_steps=math.ceil(output.num_samples / total_batch_size),
            )
        )

        self.control = self.callback_handler.on_predict(self.args, self.state, self.control, output.metrics)
        self._memory_tracker.stop_and_update_metrics(output.metrics)

        return PredictionOutput(predictions=output.predictions, label_ids=output.label_ids, metrics=output.metrics)

    def evaluation_loop(
        self,
        dataloader: DataLoader,
        description: str,
        prediction_loss_only: Optional[bool] = None,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "eval",
    ) -> EvalLoopOutput:
        """
        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.

        Works both with or without labels.
        """
        args = self.args

        prediction_loss_only = prediction_loss_only if prediction_loss_only is not None else args.prediction_loss_only

        # if eval is called w/o train init deepspeed here
        if args.deepspeed and not self.deepspeed:
            # XXX: eval doesn't have `resume_from_checkpoint` arg but we should be able to do eval
            # from the checkpoint eventually
            deepspeed_engine, _, _ = deepspeed_init(
                self, num_training_steps=0, resume_from_checkpoint=None, inference=True
            )
            self.model = deepspeed_engine.module
            self.model_wrapped = deepspeed_engine
            self.deepspeed = deepspeed_engine

        model = self._wrap_model(self.model, training=False, dataloader=dataloader)

        # if full fp16 or bf16 eval is wanted and this ``evaluation`` or ``predict`` isn't called
        # while ``train`` is running, cast it to the right dtype first and then put on device
        if not self.is_in_train:
            if args.fp16_full_eval:
                model = model.to(dtype=torch.float16, device=args.device)
            elif args.bf16_full_eval:
                model = model.to(dtype=torch.bfloat16, device=args.device)

        batch_size = self.args.eval_batch_size

        logger.info(f"***** Running {description} *****")
        if has_length(dataloader):
            logger.info(f"  Num examples = {self.num_examples(dataloader)}")
        else:
            logger.info("  Num examples: Unknown")
        logger.info(f"  Batch size = {batch_size}")

        model.eval()

        self.callback_handler.eval_dataloader = dataloader
        # Do this before wrapping.
        eval_dataset = getattr(dataloader, "dataset", None)

        if is_torch_tpu_available():
            dataloader = pl.ParallelLoader(dataloader, [args.device]).per_device_loader(args.device)

        if args.past_index >= 0:
            self._past = None

        # Initialize containers
        # losses/preds/labels on GPU/TPU (accumulated for eval_accumulation_steps)
        losses_host = None
        preds_host = None
        labels_host = None
        inputs_host = None

        # losses/preds/labels on CPU (final containers)
        all_losses = None
        all_preds = None
        all_labels = None
        all_inputs = None
        # Will be useful when we have an iterable dataset so don't know its length.

        observed_num_examples = 0
        # Main evaluation loop
        for step, inputs in enumerate(dataloader):
            # Update the observed num examples
            observed_batch_size = find_batch_size(inputs)
            if observed_batch_size is not None:
                observed_num_examples += observed_batch_size
                # For batch samplers, batch_size is not known by the dataloader in advance.
                if batch_size is None:
                    batch_size = observed_batch_size

            # Prediction step
            loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
            inputs_decode = self._prepare_input(inputs["input_ids"]) if args.include_inputs_for_metrics else None

            if is_torch_tpu_available():
                xm.mark_step()

            # Update containers on host
            if loss is not None:
                losses = self._nested_gather(loss.repeat(batch_size))
                losses_host = losses if losses_host is None else torch.cat((losses_host, losses), dim=0)
            if labels is not None:
                labels = self._pad_across_processes(labels)
                labels = self._nested_gather(labels)
                labels_host = labels if labels_host is None else nested_concat(labels_host, labels, padding_index=-100)
            if inputs_decode is not None:
                inputs_decode = self._pad_across_processes(inputs_decode)
                inputs_decode = self._nested_gather(inputs_decode)
                inputs_host = (
                    inputs_decode
                    if inputs_host is None
                    else nested_concat(inputs_host, inputs_decode, padding_index=-100)
                )
            if logits is not None:
                logits = self._pad_across_processes(logits)
                logits = self._nested_gather(logits)
                if self.preprocess_logits_for_metrics is not None:
                    logits = self.preprocess_logits_for_metrics(logits, labels)
                preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index=-100)
            self.control = self.callback_handler.on_prediction_step(args, self.state, self.control)

            # Gather all tensors and put them back on the CPU if we have done enough accumulation steps.
            if args.eval_accumulation_steps is not None and (step + 1) % args.eval_accumulation_steps == 0:
                if losses_host is not None:
                    losses = nested_numpify(losses_host)
                    all_losses = losses if all_losses is None else np.concatenate((all_losses, losses), axis=0)
                if preds_host is not None:
                    logits = nested_numpify(preds_host)
                    all_preds = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
                if inputs_host is not None:
                    inputs_decode = nested_numpify(inputs_host)
                    all_inputs = (
                        inputs_decode
                        if all_inputs is None
                        else nested_concat(all_inputs, inputs_decode, padding_index=-100)
                    )
                if labels_host is not None:
                    labels = nested_numpify(labels_host)
                    all_labels = (
                        labels if all_labels is None else nested_concat(all_labels, labels, padding_index=-100)
                    )

                # Set back to None to begin a new accumulation
                losses_host, preds_host, inputs_host, labels_host = None, None, None, None

        if args.past_index and hasattr(self, "_past"):
            # Clean the state at the end of the evaluation loop
            delattr(self, "_past")

        # Gather all remaining tensors and put them back on the CPU
        if losses_host is not None:
            losses = nested_numpify(losses_host)
            all_losses = losses if all_losses is None else np.concatenate((all_losses, losses), axis=0)
        if preds_host is not None:
            logits = nested_numpify(preds_host)
            all_preds = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
        if inputs_host is not None:
            inputs_decode = nested_numpify(inputs_host)
            all_inputs = (
                inputs_decode if all_inputs is None else nested_concat(all_inputs, inputs_decode, padding_index=-100)
            )
        if labels_host is not None:
            labels = nested_numpify(labels_host)
            all_labels = labels if all_labels is None else nested_concat(all_labels, labels, padding_index=-100)

        # Number of samples
        if has_length(eval_dataset):
            num_samples = len(eval_dataset)
        # The instance check is weird and does not actually check for the type, but whether the dataset has the right
        # methods. Therefore we need to make sure it also has the attribute.
        elif isinstance(eval_dataset, IterableDatasetShard) and getattr(eval_dataset, "num_examples", 0) > 0:
            num_samples = eval_dataset.num_examples
        else:
            if has_length(dataloader):
                num_samples = self.num_examples(dataloader)
            else:  # both len(dataloader.dataset) and len(dataloader) fail
                num_samples = observed_num_examples
        if num_samples == 0 and observed_num_examples > 0:
            num_samples = observed_num_examples

        # Number of losses has been rounded to a multiple of batch_size and in a distributed training, the number of
        # samplers has been rounded to a multiple of batch_size, so we truncate.
        if all_losses is not None:
            all_losses = all_losses[:num_samples]
        if all_preds is not None:
            all_preds = nested_truncate(all_preds, num_samples)
        if all_labels is not None:
            all_labels = nested_truncate(all_labels, num_samples)
        if all_inputs is not None:
            all_inputs = nested_truncate(all_inputs, num_samples)

        # Metrics!
        if self.compute_metrics is not None and all_preds is not None and all_labels is not None:
            if args.include_inputs_for_metrics:
                metrics = self.compute_metrics(
                    EvalPrediction(predictions=all_preds, label_ids=all_labels, inputs=all_inputs)
                )
            else:
                metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels))
        else:
            metrics = {}

        # To be JSON-serializable, we need to remove numpy types or zero-d tensors
        metrics = denumpify_detensorize(metrics)

        if all_losses is not None:
            metrics[f"{metric_key_prefix}_loss"] = all_losses.mean().item()
        if hasattr(self, "jit_compilation_time"):
            metrics[f"{metric_key_prefix}_jit_compilation_time"] = self.jit_compilation_time

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()):
            if not key.startswith(f"{metric_key_prefix}_"):
                metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)

        return EvalLoopOutput(predictions=all_preds, label_ids=all_labels, metrics=metrics, num_samples=num_samples)

    def _nested_gather(self, tensors, name=None):
        """
        Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before
        concatenating them to `gathered`
        """
        if tensors is None:
            return
        if is_torch_tpu_available():
            if name is None:
                name = "nested_gather"
            tensors = nested_xla_mesh_reduce(tensors, name)
        elif is_sagemaker_mp_enabled():
            tensors = smp_gather(tensors)
        elif self.args.local_rank != -1:
            tensors = distributed_concat(tensors)
        return tensors

    # Copied from Accelerate.
    def _pad_across_processes(self, tensor, pad_index=-100):
        """
        Recursively pad the tensors in a nested list/tuple/dictionary of tensors from all devices to the same size so
        they can safely be gathered.
        """
        if isinstance(tensor, (list, tuple)):
            return type(tensor)(self._pad_across_processes(t, pad_index=pad_index) for t in tensor)
        elif isinstance(tensor, dict):
            return type(tensor)({k: self._pad_across_processes(v, pad_index=pad_index) for k, v in tensor.items()})
        elif not isinstance(tensor, torch.Tensor):
            raise TypeError(
                f"Can't pad the values of type {type(tensor)}, only of nested list/tuple/dicts of tensors."
            )

        if len(tensor.shape) < 2:
            return tensor
        # Gather all sizes
        size = torch.tensor(tensor.shape, device=tensor.device)[None]
        sizes = self._nested_gather(size).cpu()

        max_size = max(s[1] for s in sizes)
        # When extracting XLA graphs for compilation, max_size is 0,
        # so use inequality to avoid errors.
        if tensor.shape[1] >= max_size:
            return tensor

        # Then pad to the maximum size
        old_size = tensor.shape
        new_size = list(old_size)
        new_size[1] = max_size
        new_tensor = tensor.new_zeros(tuple(new_size)) + pad_index
        new_tensor[:, : old_size[1]] = tensor
        return new_tensor

    def prediction_step(
        self,
        model: nn.Module,
        inputs: Dict[str, Union[torch.Tensor, Any]],
        prediction_loss_only: bool,
        ignore_keys: Optional[List[str]] = None,
    ) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
        """
        Perform an evaluation step on `model` using `inputs`.

        Subclass and override to inject custom behavior.

        Args:
            model (`nn.Module`):
                The model to evaluate.
            inputs (`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument `labels`. Check your model's documentation for all accepted arguments.
            prediction_loss_only (`bool`):
                Whether or not to return the loss only.
            ignore_keys (`Lst[str]`, *optional*):
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions.

        Return:
            Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss,
            logits and labels (each being optional).
        """
        has_labels = False if len(self.label_names) == 0 else all(inputs.get(k) is not None for k in self.label_names)
        # For CLIP-like models capable of returning loss values.
        # If `return_loss` is not specified or being `None` in `inputs`, we check if the default value of `return_loss`
        # is `True` in `model.forward`.
        return_loss = inputs.get("return_loss", None)
        if return_loss is None:
            return_loss = self.can_return_loss
        loss_without_labels = True if len(self.label_names) == 0 and return_loss else False

        inputs = self._prepare_inputs(inputs)
        if ignore_keys is None:
            if hasattr(self.model, "config"):
                ignore_keys = getattr(self.model.config, "keys_to_ignore_at_inference", [])
            else:
                ignore_keys = []

        # labels may be popped when computing the loss (label smoothing for instance) so we grab them first.
        if has_labels or loss_without_labels:
            labels = nested_detach(tuple(inputs.get(name) for name in self.label_names))
            if len(labels) == 1:
                labels = labels[0]
        else:
            labels = None

        with torch.no_grad():
            if is_sagemaker_mp_enabled():
                raw_outputs = smp_forward_only(model, inputs)
                if has_labels or loss_without_labels:
                    if isinstance(raw_outputs, dict):
                        loss_mb = raw_outputs["loss"]
                        logits_mb = tuple(v for k, v in raw_outputs.items() if k not in ignore_keys + ["loss"])
                    else:
                        loss_mb = raw_outputs[0]
                        logits_mb = raw_outputs[1:]

                    loss = loss_mb.reduce_mean().detach().cpu()
                    logits = smp_nested_concat(logits_mb)
                else:
                    loss = None
                    if isinstance(raw_outputs, dict):
                        logits_mb = tuple(v for k, v in raw_outputs.items() if k not in ignore_keys)
                    else:
                        logits_mb = raw_outputs
                    logits = smp_nested_concat(logits_mb)
            else:
                if has_labels or loss_without_labels:
                    with self.compute_loss_context_manager():
                        loss, outputs = self.compute_loss(model, inputs, return_outputs=True)
                    loss = loss.mean().detach()

                    if isinstance(outputs, dict):
                        logits = tuple(v for k, v in outputs.items() if k not in ignore_keys + ["loss"])
                    else:
                        logits = outputs[1:]
                else:
                    loss = None
                    with self.compute_loss_context_manager():
                        outputs = model(**inputs)
                    if isinstance(outputs, dict):
                        logits = tuple(v for k, v in outputs.items() if k not in ignore_keys)
                    else:
                        logits = outputs
                    # TODO: this needs to be fixed and made cleaner later.
                    if self.args.past_index >= 0:
                        self._past = outputs[self.args.past_index - 1]

        if prediction_loss_only:
            return (loss, None, None)

        logits = nested_detach(logits)
        if len(logits) == 1:
            logits = logits[0]

        return (loss, logits, labels)

    def floating_point_ops(self, inputs: Dict[str, Union[torch.Tensor, Any]]):
        """
        For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point
        operations for every backward + forward pass. If using another model, either implement such a method in the
        model or subclass and override this method.

        Args:
            inputs (`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

        Returns:
            `int`: The number of floating-point operations.
        """
        if hasattr(self.model, "floating_point_ops"):
            return self.model.floating_point_ops(inputs)
        else:
            return 0

    def init_git_repo(self, at_init: bool = False):
        """
        Initializes a git repo in `self.args.hub_model_id`.

        Args:
            at_init (`bool`, *optional*, defaults to `False`):
                Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is
                `True` and `at_init` is `True`, the path to the repo (which is `self.args.output_dir`) might be wiped
                out.
        """
        if not self.is_world_process_zero():
            return
        if self.args.hub_model_id is None:
            repo_name = Path(self.args.output_dir).absolute().name
        else:
            repo_name = self.args.hub_model_id
        if "/" not in repo_name:
            repo_name = get_full_repo_name(repo_name, token=self.args.hub_token)

        # Make sure the repo exists.
        create_repo(repo_name, token=self.args.hub_token, private=self.args.hub_private_repo, exist_ok=True)
        try:
            self.repo = Repository(self.args.output_dir, clone_from=repo_name, token=self.args.hub_token)
        except EnvironmentError:
            if self.args.overwrite_output_dir and at_init:
                # Try again after wiping output_dir
                shutil.rmtree(self.args.output_dir)
                self.repo = Repository(self.args.output_dir, clone_from=repo_name, token=self.args.hub_token)
            else:
                raise

        self.repo.git_pull()

        # By default, ignore the checkpoint folders
        if (
            not os.path.exists(os.path.join(self.args.output_dir, ".gitignore"))
            and self.args.hub_strategy != HubStrategy.ALL_CHECKPOINTS
        ):
            with open(os.path.join(self.args.output_dir, ".gitignore"), "w", encoding="utf-8") as writer:
                writer.writelines(["checkpoint-*/"])

        # Add "*.sagemaker" to .gitignore if using SageMaker
        if os.environ.get("SM_TRAINING_ENV"):
            self._add_sm_patterns_to_gitignore()

        self.push_in_progress = None

    def create_model_card(
        self,
        language: Optional[str] = None,
        license: Optional[str] = None,
        tags: Union[str, List[str], None] = None,
        model_name: Optional[str] = None,
        finetuned_from: Optional[str] = None,
        tasks: Union[str, List[str], None] = None,
        dataset_tags: Union[str, List[str], None] = None,
        dataset: Union[str, List[str], None] = None,
        dataset_args: Union[str, List[str], None] = None,
    ):
        """
        Creates a draft of a model card using the information available to the `Trainer`.

        Args:
            language (`str`, *optional*):
                The language of the model (if applicable)
            license (`str`, *optional*):
                The license of the model. Will default to the license of the pretrained model used, if the original
                model given to the `Trainer` comes from a repo on the Hub.
            tags (`str` or `List[str]`, *optional*):
                Some tags to be included in the metadata of the model card.
            model_name (`str`, *optional*):
                The name of the model.
            finetuned_from (`str`, *optional*):
                The name of the model used to fine-tune this one (if applicable). Will default to the name of the repo
                of the original model given to the `Trainer` (if it comes from the Hub).
            tasks (`str` or `List[str]`, *optional*):
                One or several task identifiers, to be included in the metadata of the model card.
            dataset_tags (`str` or `List[str]`, *optional*):
                One or several dataset tags, to be included in the metadata of the model card.
            dataset (`str` or `List[str]`, *optional*):
                One or several dataset identifiers, to be included in the metadata of the model card.
            dataset_args (`str` or `List[str]`, *optional*):
               One or several dataset arguments, to be included in the metadata of the model card.
        """
        if not self.is_world_process_zero():
            return

        training_summary = TrainingSummary.from_trainer(
            self,
            language=language,
            license=license,
            tags=tags,
            model_name=model_name,
            finetuned_from=finetuned_from,
            tasks=tasks,
            dataset_tags=dataset_tags,
            dataset=dataset,
            dataset_args=dataset_args,
        )
        model_card = training_summary.to_model_card()
        with open(os.path.join(self.args.output_dir, "README.md"), "w") as f:
            f.write(model_card)

    def _push_from_checkpoint(self, checkpoint_folder):
        # Only push from one node.
        if not self.is_world_process_zero() or self.args.hub_strategy == HubStrategy.END:
            return
        # If we haven't finished the last push, we don't do this one.
        if self.push_in_progress is not None and not self.push_in_progress.is_done:
            return

        output_dir = self.args.output_dir
        # To avoid a new synchronization of all model weights, we just copy the file from the checkpoint folder
        modeling_files = [CONFIG_NAME, WEIGHTS_NAME]
        for modeling_file in modeling_files:
            if os.path.isfile(os.path.join(checkpoint_folder, modeling_file)):
                shutil.copy(os.path.join(checkpoint_folder, modeling_file), os.path.join(output_dir, modeling_file))
        # Saving the tokenizer is fast and we don't know how many files it may have spawned, so we resave it to be sure.
        if self.tokenizer is not None:
            self.tokenizer.save_pretrained(output_dir)
        # Same for the training arguments
        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

        try:
            if self.args.hub_strategy == HubStrategy.CHECKPOINT:
                # Temporarily move the checkpoint just saved for the push
                tmp_checkpoint = os.path.join(output_dir, "last-checkpoint")
                # We have to remove the "last-checkpoint" dir if it exists, otherwise the checkpoint is moved as a
                # subfolder.
                if os.path.isdir(tmp_checkpoint):
                    shutil.rmtree(tmp_checkpoint)
                shutil.move(checkpoint_folder, tmp_checkpoint)

            if self.args.save_strategy == IntervalStrategy.STEPS:
                commit_message = f"Training in progress, step {self.state.global_step}"
            else:
                commit_message = f"Training in progress, epoch {int(self.state.epoch)}"
            _, self.push_in_progress = self.repo.push_to_hub(
                commit_message=commit_message, blocking=False, auto_lfs_prune=True
            )
        finally:
            if self.args.hub_strategy == HubStrategy.CHECKPOINT:
                # Move back the checkpoint to its place
                shutil.move(tmp_checkpoint, checkpoint_folder)

    def push_to_hub(self, commit_message: Optional[str] = "End of training", blocking: bool = True, **kwargs) -> str:
        """
        Upload *self.model* and *self.tokenizer* to the 🤗 model hub on the repo *self.args.hub_model_id*.

        Parameters:
            commit_message (`str`, *optional*, defaults to `"End of training"`):
                Message to commit while pushing.
            blocking (`bool`, *optional*, defaults to `True`):
                Whether the function should return only when the `git push` has finished.
            kwargs:
                Additional keyword arguments passed along to [`~Trainer.create_model_card`].

        Returns:
            The url of the commit of your model in the given repository if `blocking=False`, a tuple with the url of
            the commit and an object to track the progress of the commit if `blocking=True`
        """
        # If a user calls manually `push_to_hub` with `self.args.push_to_hub = False`, we try to create the repo but
        # it might fail.
        if not hasattr(self, "repo"):
            self.init_git_repo()

        model_name = kwargs.pop("model_name", None)
        if model_name is None and self.args.should_save:
            if self.args.hub_model_id is None:
                model_name = Path(self.args.output_dir).name
            else:
                model_name = self.args.hub_model_id.split("/")[-1]

        # Needs to be executed on all processes for TPU training, but will only save on the processed determined by
        # self.args.should_save.
        self.save_model(_internal_call=True)

        # Only push from one node.
        if not self.is_world_process_zero():
            return

        # Cancel any async push in progress if blocking=True. The commits will all be pushed together.
        if blocking and self.push_in_progress is not None and not self.push_in_progress.is_done:
            self.push_in_progress._process.kill()
            self.push_in_progress = None

        git_head_commit_url = self.repo.push_to_hub(
            commit_message=commit_message, blocking=blocking, auto_lfs_prune=True
        )
        # push separately the model card to be independant from the rest of the model
        if self.args.should_save:
            self.create_model_card(model_name=model_name, **kwargs)
            try:
                self.repo.push_to_hub(
                    commit_message="update model card README.md", blocking=blocking, auto_lfs_prune=True
                )
            except EnvironmentError as exc:
                logger.error(f"Error pushing update to the model card. Please read logs and retry.\n${exc}")

        return git_head_commit_url

    #
    # Deprecated code
    #

    def prediction_loop(
        self,
        dataloader: DataLoader,
        description: str,
        prediction_loss_only: Optional[bool] = None,
        ignore_keys: Optional[List[str]] = None,
        metric_key_prefix: str = "eval",
    ) -> EvalLoopOutput:
        """
        Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.

        Works both with or without labels.
        """
        args = self.args

        if not has_length(dataloader):
            raise ValueError("dataloader must implement a working __len__")

        prediction_loss_only = prediction_loss_only if prediction_loss_only is not None else args.prediction_loss_only

        # if eval is called w/o train init deepspeed here
        if args.deepspeed and not self.deepspeed:
            # XXX: eval doesn't have `resume_from_checkpoint` arg but we should be able to do eval
            # from the checkpoint eventually
            deepspeed_engine, _, _ = deepspeed_init(self, num_training_steps=0, resume_from_checkpoint=None)
            self.model = deepspeed_engine.module
            self.model_wrapped = deepspeed_engine
            self.deepspeed = deepspeed_engine
            # XXX: we don't need optim/sched for inference, but this needs to be sorted out, since
            # for example the Z3-optimizer is a must for zero3 to work even for inference - what we
            # don't need is the deepspeed basic optimizer which is self.optimizer.optimizer
            deepspeed_engine.optimizer.optimizer = None
            deepspeed_engine.lr_scheduler = None

        model = self._wrap_model(self.model, training=False, dataloader=dataloader)

        # if full fp16 or bf16 eval is wanted and this ``evaluation`` or ``predict`` isn't called
        # while ``train`` is running, cast it to the right dtype first and then put on device
        if not self.is_in_train:
            if args.fp16_full_eval:
                model = model.to(dtype=torch.float16, device=args.device)
            elif args.bf16_full_eval:
                model = model.to(dtype=torch.bfloat16, device=args.device)

        batch_size = dataloader.batch_size
        num_examples = self.num_examples(dataloader)
        logger.info(f"***** Running {description} *****")
        logger.info(f"  Num examples = {num_examples}")
        logger.info(f"  Batch size = {batch_size}")
        losses_host: torch.Tensor = None
        preds_host: Union[torch.Tensor, List[torch.Tensor]] = None
        labels_host: Union[torch.Tensor, List[torch.Tensor]] = None
        inputs_host: Union[torch.Tensor, List[torch.Tensor]] = None

        world_size = max(1, args.world_size)

        eval_losses_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=batch_size)
        if not prediction_loss_only:
            # The actual number of eval_sample can be greater than num_examples in distributed settings (when we pass
            # a batch size to the sampler)
            make_multiple_of = None
            if hasattr(dataloader, "sampler") and isinstance(dataloader.sampler, SequentialDistributedSampler):
                make_multiple_of = dataloader.sampler.batch_size
            preds_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=make_multiple_of)
            labels_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=make_multiple_of)
            inputs_gatherer = DistributedTensorGatherer(world_size, num_examples, make_multiple_of=make_multiple_of)

        model.eval()

        if is_torch_tpu_available():
            dataloader = pl.ParallelLoader(dataloader, [args.device]).per_device_loader(args.device)

        if args.past_index >= 0:
            self._past = None

        self.callback_handler.eval_dataloader = dataloader

        for step, inputs in enumerate(dataloader):
            loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
            inputs_decode = self._prepare_input(inputs["input_ids"]) if args.include_inputs_for_metrics else None

            if loss is not None:
                losses = loss.repeat(batch_size)
                losses_host = losses if losses_host is None else torch.cat((losses_host, losses), dim=0)
            if logits is not None:
                preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index=-100)
            if labels is not None:
                labels_host = labels if labels_host is None else nested_concat(labels_host, labels, padding_index=-100)
            if inputs_decode is not None:
                inputs_host = (
                    inputs_decode
                    if inputs_host is None
                    else nested_concat(inputs_host, inputs_decode, padding_index=-100)
                )
            self.control = self.callback_handler.on_prediction_step(args, self.state, self.control)

            # Gather all tensors and put them back on the CPU if we have done enough accumulation steps.
            if args.eval_accumulation_steps is not None and (step + 1) % args.eval_accumulation_steps == 0:
                eval_losses_gatherer.add_arrays(self._gather_and_numpify(losses_host, "eval_losses"))
                if not prediction_loss_only:
                    preds_gatherer.add_arrays(self._gather_and_numpify(preds_host, "eval_preds"))
                    labels_gatherer.add_arrays(self._gather_and_numpify(labels_host, "eval_label_ids"))
                    inputs_gatherer.add_arrays(self._gather_and_numpify(inputs_host, "eval_inputs_ids"))

                # Set back to None to begin a new accumulation
                losses_host, preds_host, labels_host, inputs_host = None, None, None, None

        if args.past_index and hasattr(self, "_past"):
            # Clean the state at the end of the evaluation loop
            delattr(self, "_past")

        # Gather all remaining tensors and put them back on the CPU
        eval_losses_gatherer.add_arrays(self._gather_and_numpify(losses_host, "eval_losses"))
        if not prediction_loss_only:
            preds_gatherer.add_arrays(self._gather_and_numpify(preds_host, "eval_preds"))
            labels_gatherer.add_arrays(self._gather_and_numpify(labels_host, "eval_label_ids"))
            inputs_gatherer.add_arrays(self._gather_and_numpify(inputs_host, "eval_inputs_ids"))

        eval_loss = eval_losses_gatherer.finalize()
        preds = preds_gatherer.finalize() if not prediction_loss_only else None
        label_ids = labels_gatherer.finalize() if not prediction_loss_only else None
        inputs_ids = inputs_gatherer.finalize() if not prediction_loss_only else None

        if self.compute_metrics is not None and preds is not None and label_ids is not None:
            if args.include_inputs_for_metrics:
                metrics = self.compute_metrics(
                    EvalPrediction(predictions=preds, label_ids=label_ids, inputs=inputs_ids)
                )
            else:
                metrics = self.compute_metrics(EvalPrediction(predictions=preds, label_ids=label_ids))
        else:
            metrics = {}

        # To be JSON-serializable, we need to remove numpy types or zero-d tensors
        metrics = denumpify_detensorize(metrics)

        if eval_loss is not None:
            metrics[f"{metric_key_prefix}_loss"] = eval_loss.mean().item()

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()):
            if not key.startswith(f"{metric_key_prefix}_"):
                metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key)

        return EvalLoopOutput(predictions=preds, label_ids=label_ids, metrics=metrics, num_samples=num_examples)

    def _gather_and_numpify(self, tensors, name):
        """
        Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before
        concatenating them to `gathered`
        """
        if tensors is None:
            return
        if is_torch_tpu_available():
            tensors = nested_xla_mesh_reduce(tensors, name)
        elif is_sagemaker_mp_enabled():
            tensors = smp_gather(tensors)
        elif self.args.local_rank != -1:
            tensors = distributed_concat(tensors)

        return nested_numpify(tensors)

    def _add_sm_patterns_to_gitignore(self) -> None:
        """Add SageMaker Checkpointing patterns to .gitignore file."""
        # Make sure we only do this on the main process
        if not self.is_world_process_zero():
            return

        patterns = ["*.sagemaker-uploading", "*.sagemaker-uploaded"]

        # Get current .gitignore content
        if os.path.exists(os.path.join(self.repo.local_dir, ".gitignore")):
            with open(os.path.join(self.repo.local_dir, ".gitignore"), "r") as f:
                current_content = f.read()
        else:
            current_content = ""

        # Add the patterns to .gitignore
        content = current_content
        for pattern in patterns:
            if pattern not in content:
                if content.endswith("\n"):
                    content += pattern
                else:
                    content += f"\n{pattern}"

        # Write the .gitignore file if it has changed
        if content != current_content:
            with open(os.path.join(self.repo.local_dir, ".gitignore"), "w") as f:
                logger.debug(f"Writing .gitignore file. Content: {content}")
                f.write(content)

        self.repo.git_add(".gitignore")

        # avoid race condition with git status
        time.sleep(0.5)

        if not self.repo.is_repo_clean():
            self.repo.git_commit("Add *.sagemaker patterns to .gitignore.")
            self.repo.git_push()



File: ptuning/main.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace 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 sequence to sequence.
"""
# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.

import logging
import os
import sys
import json

import numpy as np
from datasets import load_dataset
import jieba 
from rouge_chinese import Rouge
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
import torch

import transformers
from transformers import (
    AutoConfig,
    AutoModel,
    AutoTokenizer,
    DataCollatorForSeq2Seq,
    HfArgumentParser,
    Seq2SeqTrainingArguments,
    set_seed,
)
from trainer_seq2seq import Seq2SeqTrainer

from arguments import ModelArguments, DataTrainingArguments

logger = logging.getLogger(__name__)

def main():

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # 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.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # 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: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Load dataset
    data_files = {}
    if data_args.train_file is not None:
        data_files["train"] = data_args.train_file
        extension = data_args.train_file.split(".")[-1]
    if data_args.validation_file is not None:
        data_files["validation"] = data_args.validation_file
        extension = data_args.validation_file.split(".")[-1]
    if data_args.test_file is not None:
        data_files["test"] = data_args.test_file
        extension = data_args.test_file.split(".")[-1]

    raw_datasets = load_dataset(
        extension,
        data_files=data_files,
        cache_dir=model_args.cache_dir,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    # Load pretrained model and tokenizer
    config = AutoConfig.from_pretrained(model_args.model_name_or_path, trust_remote_code=True)
    config.pre_seq_len = model_args.pre_seq_len
    config.prefix_projection = model_args.prefix_projection

    tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, trust_remote_code=True)

    if model_args.ptuning_checkpoint is not None:
        # Evaluation
        # Loading extra state dict of prefix encoder
        model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)
        prefix_state_dict = torch.load(os.path.join(model_args.ptuning_checkpoint, "pytorch_model.bin"))
        new_prefix_state_dict = {}
        for k, v in prefix_state_dict.items():
            if k.startswith("transformer.prefix_encoder."):
                new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
        model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
    else:
        model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)

    if model_args.quantization_bit is not None:
        print(f"Quantized to {model_args.quantization_bit} bit")
        model = model.quantize(model_args.quantization_bit)
    if model_args.pre_seq_len is not None:
        # P-tuning v2
        model = model.half()
        model.transformer.prefix_encoder.float()
    else:
        # Finetune
        model = model.float()

    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""

    # Preprocessing the datasets.
    # We need to tokenize inputs and targets.
    if training_args.do_train:
        column_names = raw_datasets["train"].column_names
    elif training_args.do_eval:
        column_names = raw_datasets["validation"].column_names
    elif training_args.do_predict:
        column_names = raw_datasets["test"].column_names
    else:
        logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
        return

    # Get the column names for input/target.
    prompt_column = data_args.prompt_column
    response_column = data_args.response_column
    history_column = data_args.history_column
    
    # Temporarily set max_target_length for training.
    max_target_length = data_args.max_target_length

    def preprocess_function_eval(examples):
        inputs, targets = [], []
        for i in range(len(examples[prompt_column])):
            if examples[prompt_column][i] and examples[response_column][i]:
                query = examples[prompt_column][i]
                if history_column is None or len(examples[history_column][i]) == 0:
                    prompt = query
                else:
                    prompt = ""
                    history = examples[history_column][i]
                    for turn_idx, (old_query, response) in enumerate(history):
                        prompt += "[Round {}]\n问：{}\n答：{}\n".format(turn_idx, old_query, response)
                    prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)
                inputs.append(prompt)
                targets.append(examples[response_column][i])

        inputs = [prefix + inp for inp in inputs]
        model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, truncation=True, padding=True)
        labels = tokenizer(text_target=targets, max_length=max_target_length, truncation=True)

        if data_args.ignore_pad_token_for_loss:
            labels["input_ids"] = [
                [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
            ]
        model_inputs["labels"] = labels["input_ids"]

        return model_inputs

    def preprocess_function_train(examples):
        max_seq_length = data_args.max_source_length + data_args.max_target_length

        model_inputs = {
            "input_ids": [],
            "labels": [],
        }
        for i in range(len(examples[prompt_column])):
            if examples[prompt_column][i] and examples[response_column][i]:
                query, answer = examples[prompt_column][i], examples[response_column][i]

                if history_column is None:
                    prompt = query
                else:
                    prompt = ""
                    history = examples[history_column][i]
                    for turn_idx, (old_query, response) in enumerate(history):
                        prompt += "[Round {}]\n问：{}\n答：{}\n".format(turn_idx, old_query, response)
                    prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)

                prompt = prefix + prompt
                a_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
                b_ids = tokenizer.encode(text=answer, add_special_tokens=False)

                if len(a_ids) > data_args.max_source_length - 1:
                    a_ids = a_ids[: data_args.max_source_length - 1]

                if len(b_ids) > data_args.max_target_length - 2:
                    b_ids = b_ids[: data_args.max_target_length - 2]

                input_ids = tokenizer.build_inputs_with_special_tokens(a_ids, b_ids)

                context_length = input_ids.index(tokenizer.bos_token_id)
                mask_position = context_length - 1
                labels = [-100] * context_length + input_ids[mask_position+1:]
                
                pad_len = max_seq_length - len(input_ids)
                input_ids = input_ids + [tokenizer.pad_token_id] * pad_len
                labels = labels + [tokenizer.pad_token_id] * pad_len
                if data_args.ignore_pad_token_for_loss:
                    labels = [(l if l != tokenizer.pad_token_id else -100) for l in labels]

                model_inputs["input_ids"].append(input_ids)
                model_inputs["labels"].append(labels)

        return model_inputs
    
    def print_dataset_example(example):
        print("input_ids",example["input_ids"])
        print("inputs", tokenizer.decode(example["input_ids"]))
        print("label_ids", example["labels"])
        print("labels", tokenizer.decode(example["labels"]))

    if training_args.do_train:
        if "train" not in raw_datasets:
            raise ValueError("--do_train requires a train dataset")
        train_dataset = raw_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))
        with training_args.main_process_first(desc="train dataset map pre-processing"):
            train_dataset = train_dataset.map(
                preprocess_function_train,
                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 train dataset",
            )
        print_dataset_example(train_dataset[0])

    if training_args.do_eval:
        max_target_length = data_args.val_max_target_length
        if "validation" not in raw_datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = raw_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))
        with training_args.main_process_first(desc="validation dataset map pre-processing"):
            eval_dataset = eval_dataset.map(
                preprocess_function_eval,
                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 validation dataset",
            )
        print_dataset_example(eval_dataset[0])

    if training_args.do_predict:
        max_target_length = data_args.val_max_target_length
        if "test" not in raw_datasets:
            raise ValueError("--do_predict requires a test dataset")
        predict_dataset = raw_datasets["test"]
        if data_args.max_predict_samples is not None:
            max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
            predict_dataset = predict_dataset.select(range(max_predict_samples))
        with training_args.main_process_first(desc="prediction dataset map pre-processing"):
            predict_dataset = predict_dataset.map(
                preprocess_function_eval,
                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 prediction dataset",
            )
        print_dataset_example(predict_dataset[0])

    # Data collator
    label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
    data_collator = DataCollatorForSeq2Seq(
        tokenizer,
        model=model,
        label_pad_token_id=label_pad_token_id,
        pad_to_multiple_of=None,
        padding=False
    )

    # Metric
    def compute_metrics(eval_preds):
        preds, labels = eval_preds
        if isinstance(preds, tuple):
            preds = preds[0]
        decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
        if data_args.ignore_pad_token_for_loss:
            # Replace -100 in the labels as we can't decode them.
            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

        score_dict = {
            "rouge-1": [],
            "rouge-2": [],
            "rouge-l": [],
            "bleu-4": []
        }
        for pred, label in zip(decoded_preds, decoded_labels):
            hypothesis = list(jieba.cut(pred))
            reference = list(jieba.cut(label))
            rouge = Rouge()
            scores = rouge.get_scores(' '.join(hypothesis) , ' '.join(reference))
            result = scores[0]
            
            for k, v in result.items():
                score_dict[k].append(round(v["f"] * 100, 4))
            bleu_score = sentence_bleu([list(label)], list(pred), smoothing_function=SmoothingFunction().method3)
            score_dict["bleu-4"].append(round(bleu_score * 100, 4))

        for k, v in score_dict.items():
            score_dict[k] = float(np.mean(v))
        return score_dict

    # Override the decoding parameters of Seq2SeqTrainer
    training_args.generation_max_length = (
        training_args.generation_max_length
        if training_args.generation_max_length is not None
        else data_args.val_max_target_length
    )
    training_args.generation_num_beams = (
        data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
    )
    # Initialize our Trainer
    trainer = Seq2SeqTrainer(
        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,
        data_collator=data_collator,
        compute_metrics=compute_metrics if training_args.predict_with_generate else None,
        save_prefixencoder=model_args.pre_seq_len is not 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
        model.gradient_checkpointing_enable()
        model.enable_input_require_grads()
        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()

    # Evaluation
    results = {}
    max_seq_length = data_args.max_source_length + data_args.max_target_length + 1
    if training_args.do_eval:
        logger.info("*** Evaluate ***")
        metrics = trainer.evaluate(metric_key_prefix="eval", do_sample=True, top_p=0.7, max_length=max_seq_length, temperature=0.95)
        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))

        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    if training_args.do_predict:
        logger.info("*** Predict ***")
        predict_results = trainer.predict(predict_dataset, metric_key_prefix="predict", max_length=max_seq_length, do_sample=True, top_p=0.7, temperature=0.95)
        metrics = predict_results.metrics
        max_predict_samples = (
            data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
        )
        metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))

        trainer.log_metrics("predict", metrics)
        trainer.save_metrics("predict", metrics)

        if trainer.is_world_process_zero():
            if training_args.predict_with_generate:
                predictions = tokenizer.batch_decode(
                    predict_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
                )
                predictions = [pred.strip() for pred in predictions]
                labels = tokenizer.batch_decode(
                    predict_results.label_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
                )
                labels = [label.strip() for label in labels]
                output_prediction_file = os.path.join(training_args.output_dir, "generated_predictions.txt")
                with open(output_prediction_file, "w", encoding="utf-8") as writer:
                    for p, l in zip(predictions, labels):
                        res = json.dumps({"labels": l, "predict": p}, ensure_ascii=False)
                        writer.write(f"{res}\n")
    return results


def _mp_fn(index):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()



File: ptuning/web_demo.py

import os, sys

import gradio as gr
import mdtex2html

import torch
import transformers
from transformers import (
    AutoConfig,
    AutoModel,
    AutoTokenizer,
    AutoTokenizer,
    DataCollatorForSeq2Seq,
    HfArgumentParser,
    Seq2SeqTrainingArguments,
    set_seed,
)

from arguments import ModelArguments, DataTrainingArguments


model = None
tokenizer = None

"""Override Chatbot.postprocess"""


def postprocess(self, y):
    if y is None:
        return []
    for i, (message, response) in enumerate(y):
        y[i] = (
            None if message is None else mdtex2html.convert((message)),
            None if response is None else mdtex2html.convert(response),
        )
    return y


gr.Chatbot.postprocess = postprocess


def parse_text(text):
    """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
    lines = text.split("\n")
    lines = [line for line in lines if line != ""]
    count = 0
    for i, line in enumerate(lines):
        if "```" in line:
            count += 1
            items = line.split('`')
            if count % 2 == 1:
                lines[i] = f'<pre><code class="language-{items[-1]}">'
            else:
                lines[i] = f'<br></code></pre>'
        else:
            if i > 0:
                if count % 2 == 1:
                    line = line.replace("`", "\`")
                    line = line.replace("<", "&lt;")
                    line = line.replace(">", "&gt;")
                    line = line.replace(" ", "&nbsp;")
                    line = line.replace("*", "&ast;")
                    line = line.replace("_", "&lowbar;")
                    line = line.replace("-", "&#45;")
                    line = line.replace(".", "&#46;")
                    line = line.replace("!", "&#33;")
                    line = line.replace("(", "&#40;")
                    line = line.replace(")", "&#41;")
                    line = line.replace("$", "&#36;")
                lines[i] = "<br>"+line
    text = "".join(lines)
    return text


def predict(input, chatbot, max_length, top_p, temperature, history):
    chatbot.append((parse_text(input), ""))
    for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,
                                               temperature=temperature):
        chatbot[-1] = (parse_text(input), parse_text(response))       

        yield chatbot, history


def reset_user_input():
    return gr.update(value='')


def reset_state():
    return [], []


with gr.Blocks() as demo:
    gr.HTML("""<h1 align="center">ChatGLM</h1>""")

    chatbot = gr.Chatbot()
    with gr.Row():
        with gr.Column(scale=4):
            with gr.Column(scale=12):
                user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(
                    container=False)
            with gr.Column(min_width=32, scale=1):
                submitBtn = gr.Button("Submit", variant="primary")
        with gr.Column(scale=1):
            emptyBtn = gr.Button("Clear History")
            max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
            top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
            temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)

    history = gr.State([])

    submitBtn.click(predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history],
                    show_progress=True)
    submitBtn.click(reset_user_input, [], [user_input])

    emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)



def main():
    global model, tokenizer

    parser = HfArgumentParser((
        ModelArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # 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 = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))[0]
    else:
        model_args = parser.parse_args_into_dataclasses()[0]

    tokenizer = AutoTokenizer.from_pretrained(
        model_args.model_name_or_path, trust_remote_code=True)
    config = AutoConfig.from_pretrained(
        model_args.model_name_or_path, trust_remote_code=True)

    config.pre_seq_len = model_args.pre_seq_len
    config.prefix_projection = model_args.prefix_projection

    if model_args.ptuning_checkpoint is not None:
        print(f"Loading prefix_encoder weight from {model_args.ptuning_checkpoint}")
        model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)
        prefix_state_dict = torch.load(os.path.join(model_args.ptuning_checkpoint, "pytorch_model.bin"))
        new_prefix_state_dict = {}
        for k, v in prefix_state_dict.items():
            if k.startswith("transformer.prefix_encoder."):
                new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
        model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
    else:
        model = AutoModel.from_pretrained(model_args.model_name_or_path, config=config, trust_remote_code=True)

    if model_args.quantization_bit is not None:
        print(f"Quantized to {model_args.quantization_bit} bit")
        model = model.quantize(model_args.quantization_bit)

    if model_args.pre_seq_len is not None:
        # P-tuning v2
        model = model.half().cuda()
        model.transformer.prefix_encoder.float().cuda()
    
    model = model.eval()
    demo.queue().launch(share=False, inbrowser=True)



if __name__ == "__main__":
    main()


# ChatGLM-6B

<p align="center">
   🌐 <a href="https://chatglm.cn/blog" target="_blank">Blog</a> • 🤗 <a href="https://huggingface.co/THUDM/chatglm-6b" target="_blank">HF Repo</a> • 🐦 <a href="https://twitter.com/thukeg" target="_blank">Twitter</a> • 📄<a href="https://arxiv.org/pdf/2406.12793" target="_blank"> Report </a> <br>
</p>
<p align="center">
    👋 加入我们的  <a href="https://discord.gg/fK2dz4bg" target="_blank">Discord</a> 和 <a href="resources/WECHAT.md" target="_blank">WeChat</a>
</p>
<p align="center">
📍在 <a href="https://open.bigmodel.cn/?utm_campaign=open&_channel_track_key=OWTVNma9">智谱AI开放平台</a> 体验和使用更大规模的 GLM 商业模型。
</p>

*Read this in [English](README_en.md).*

## GLM-4 开源模型和API

我们已经发布最新的 **GLM-4** 大语言对话模型，该模型在多个指标上有了新的突破，您可以在以下两个渠道体验我们的最新模型。

+ [GLM-4 开源模型](https://github.com/THUDM/GLM-4) 我们已经开源了 GLM-4-9B 系列模型，在各项指标的ce是上有明显提升，欢迎尝试。
+ [智谱清言](https://chatglm.cn/main/detail?fr=ecology_x) 体验最新版 GLM-4，包括 **GLMs，All tools**等功能。
+ [API平台](https://open.bigmodel.cn/?utm_campaign=open&_channel_track_key=OWTVNma9) 新一代 API 平台已经上线，您可以直接在
  API
  平台上体验 `GLM-4-0520`、`GLM-4-air`、`GLM-4-airx`、`GLM-4-flash`、`GLM-4`、`GLM-3-Turbo`、`CharacterGLM-3`，`CogView-3`
  等新模型。
  其中`GLM-4`、`GLM-3-Turbo`两个模型支持了 `System Prompt`、`Function Call`、 `Retrieval`、`Web_Search`等新功能，欢迎体验。

+ [GLM-4 API 开源教程](https://github.com/MetaGLM/glm-cookbook/) GLM-4 API教程和基础应用，欢迎尝试。
  API相关问题可以在本开源教程疑问，或者使用 [GLM-4 API AI助手](https://open.bigmodel.cn/shareapp/v1/?share_code=sQwt5qyqYVaNh1O_87p8O)
  来获得常见问题的帮助。

-----
## 介绍

ChatGLM-6B 是一个开源的、支持中英双语的对话语言模型，基于 [General Language Model (GLM)](https://github.com/THUDM/GLM) 架构，具有 62 亿参数。结合模型量化技术，用户可以在消费级的显卡上进行本地部署（INT4 量化级别下最低只需 6GB 显存）。
ChatGLM-6B 使用了和 ChatGPT 相似的技术，针对中文问答和对话进行了优化。经过约 1T 标识符的中英双语训练，辅以监督微调、反馈自助、人类反馈强化学习等技术的加持，62 亿参数的 ChatGLM-6B 已经能生成相当符合人类偏好的回答，更多信息请参考我们的[博客](https://chatglm.cn/blog)。欢迎通过 [chatglm.cn](https://chatglm.cn) 体验更大规模的 ChatGLM 模型。

为了方便下游开发者针对自己的应用场景定制模型，我们同时实现了基于 [P-Tuning v2](https://github.com/THUDM/P-tuning-v2) 的高效参数微调方法 [(使用指南)](ptuning/README.md) ，INT4 量化级别下最低只需 7GB 显存即可启动微调。

ChatGLM-6B 权重对学术研究**完全开放**，在填写[问卷](https://open.bigmodel.cn/mla/form)进行登记后**亦允许免费商业使用**。

-----

ChatGLM-6B 开源模型旨在与开源社区一起推动大模型技术发展，恳请开发者和大家遵守[开源协议](MODEL_LICENSE)，勿将开源模型和代码及基于开源项目产生的衍生物用于任何可能给国家和社会带来危害的用途以及用于任何未经过安全评估和备案的服务。**目前，本项目团队未基于 ChatGLM-6B 开发任何应用，包括网页端、安卓、苹果 iOS 及 Windows App 等应用。**

尽管模型在训练的各个阶段都尽力确保数据的合规性和准确性，但由于 ChatGLM-6B 模型规模较小，且模型受概率随机性因素影响，无法保证输出内容的准确性，且模型易被误导（详见[局限性](README.md#局限性)）。**本项目不承担开源模型和代码导致的数据安全、舆情风险或发生任何模型被误导、滥用、传播、不当利用而产生的风险和责任。**

## 更新信息
**[2023/07/25]** 发布 [CodeGeeX2](https://github.com/THUDM/CodeGeeX2) ，基于 ChatGLM2-6B 的代码生成模型，代码能力全面提升，更多特性包括：

* **更强大的代码能力**：CodeGeeX2-6B 进一步经过了 600B 代码数据预训练，相比 CodeGeeX 一代模型，在代码能力上全面提升，[HumanEval-X](https://huggingface.co/datasets/THUDM/humaneval-x) 评测集的六种编程语言均大幅提升 (Python +57%, C++ +71%, Java +54%, JavaScript +83%, Go +56%, Rust +321\%)，在Python上达到 35.9\% 的 Pass@1 一次通过率，超越规模更大的 StarCoder-15B。
* **更优秀的模型特性**：继承 ChatGLM2-6B 模型特性，CodeGeeX2-6B 更好支持中英文输入，支持最大 8192 序列长度，推理速度较一代 大幅提升，量化后仅需6GB显存即可运行，支持轻量级本地化部署。
* **更全面的AI编程助手**：CodeGeeX插件（[VS Code](https://marketplace.visualstudio.com/items?itemName=aminer.codegeex), [Jetbrains](https://plugins.jetbrains.com/plugin/20587-codegeex)）后端升级，支持超过100种编程语言，新增上下文补全、跨文件补全等实用功能。结合 Ask CodeGeeX 交互式AI编程助手，支持中英文对话解决各种编程问题，包括且不限于代码解释、代码翻译、代码纠错、文档生成等，帮助程序员更高效开发。

**[2023/06/25]** 发布 [ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B)，ChatGLM-6B 的升级版本，在保留了了初代模型对话流畅、部署门槛较低等众多优秀特性的基础之上，ChatGLM**2**-6B 引入了如下新特性：

1. **更强大的性能**：基于 ChatGLM 初代模型的开发经验，我们全面升级了 ChatGLM2-6B 的基座模型。ChatGLM2-6B 使用了 [GLM](https://github.com/THUDM/GLM) 的混合目标函数，经过了 1.4T 中英标识符的预训练与人类偏好对齐训练，[评测结果](#评测结果)显示，相比于初代模型，ChatGLM2-6B 在 MMLU（+23%）、CEval（+33%）、GSM8K（+571%） 、BBH（+60%）等数据集上的性能取得了大幅度的提升，在同尺寸开源模型中具有较强的竞争力。
2. **更长的上下文**：基于 [FlashAttention](https://github.com/HazyResearch/flash-attention) 技术，我们将基座模型的上下文长度（Context Length）由 ChatGLM-6B 的 2K 扩展到了 32K，并在对话阶段使用 8K 的上下文长度训练，允许更多轮次的对话。但当前版本的 ChatGLM2-6B 对单轮超长文档的理解能力有限，我们会在后续迭代升级中着重进行优化。
3. **更高效的推理**：基于 [Multi-Query Attention](http://arxiv.org/abs/1911.02150) 技术，ChatGLM2-6B 有更高效的推理速度和更低的显存占用：在官方的模型实现下，推理速度相比初代提升了 42%，INT4 量化下，6G 显存支持的对话长度由 1K 提升到了 8K。

更多信息参见 [ChatGLM2-6B](https://github.com/THUDM/ChatGLM2-6B)。

**[2023/06/14]** 发布 [WebGLM](https://github.com/THUDM/WebGLM)，一项被接受于KDD 2023的研究工作，支持利用网络信息生成带有准确引用的长回答。

![](resources/webglm.jpg)

**[2023/05/17]** 发布 [VisualGLM-6B](https://github.com/THUDM/VisualGLM-6B)，一个支持图像理解的多模态对话语言模型。

![](resources/visualglm.png)

可以通过本仓库中的 [cli_demo_vision.py](cli_demo_vision.py) 和 [web_demo_vision.py](web_demo_vision.py) 来运行命令行和网页 Demo。注意 VisualGLM-6B 需要额外安装 [SwissArmyTransformer](https://github.com/THUDM/SwissArmyTransformer/) 和 torchvision。更多信息参见 [VisualGLM-6B](https://github.com/THUDM/VisualGLM-6B)。

**[2023/05/15]** 更新 v1.1 版本 checkpoint，训练数据增加英文指令微调数据以平衡中英文数据比例，解决英文回答中夹杂中文词语的现象。

<details><summary><b>以下是更新前后的英文问题对比：</b></summary>

* 问题：Describe a time when you had to make a difficult decision.
  - v1.0:
  ![](resources/english-q1-old.png)
  - v1.1:
  ![](resources/english-q1-new.png)
* 问题：Describe the function of a computer motherboard
  - v1.0:
  ![](resources/english-q2-old.png)
  - v1.1:
  ![](resources/english-q2-new.png)
* 问题：Develop a plan to reduce electricity usage in a home.
  - v1.0:
  ![](resources/english-q3-old.png)
  - v1.1:
  ![](resources/english-q3-new.png)
* 问题：未来的NFT，可能真实定义一种现实的资产，它会是一处房产，一辆汽车，一片土地等等，这样的数字凭证可能比真实的东西更有价值，你可以随时交易和使用，在虚拟和现实中无缝的让拥有的资产继续创造价值，未来会是万物归我所用，但不归我所有的时代。翻译成专业的英语
  - v1.0:
  ![](resources/english-q4-old.png)
  - v1.1:
  ![](resources/english-q4-new.png)
</details>

更多更新信息参见 [UPDATE.md](UPDATE.md)

## 友情链接
对 ChatGLM 进行加速的开源项目：
* [lyraChatGLM](https://huggingface.co/TMElyralab/lyraChatGLM): 对 ChatGLM-6B 进行推理加速，最高可以实现 9000+ tokens/s 的推理速度
* [ChatGLM-MNN](https://github.com/wangzhaode/ChatGLM-MNN): 一个基于 MNN 的 ChatGLM-6B C++ 推理实现，支持根据显存大小自动分配计算任务给 GPU 和 CPU
* [JittorLLMs](https://github.com/Jittor/JittorLLMs)：最低3G显存或者没有显卡都可运行 ChatGLM-6B FP16， 支持Linux、windows、Mac部署
* [InferLLM](https://github.com/MegEngine/InferLLM)：轻量级 C++ 推理，可以实现本地 x86，Arm 处理器上实时聊天，手机上也同样可以实时运行，运行内存只需要 4G

基于或使用了 ChatGLM-6B 的开源项目：
* [langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM)：基于 langchain 的 ChatGLM 应用，实现基于可扩展知识库的问答
* [闻达](https://github.com/l15y/wenda)：大型语言模型调用平台，基于 ChatGLM-6B 实现了类 ChatPDF 功能
* [glm-bot](https://github.com/initialencounter/glm-bot)：将ChatGLM接入Koishi可在各大聊天平台上调用ChatGLM
* [Chuanhu Chat](https://github.com/GaiZhenbiao/ChuanhuChatGPT): 为各个大语言模型和在线模型API提供美观易用、功能丰富、快速部署的用户界面，支持ChatGLM-6B。

支持 ChatGLM-6B 和相关应用在线训练的示例项目：
* [ChatGLM-6B 的部署与微调教程](https://www.heywhale.com/mw/project/6436d82948f7da1fee2be59e)
* [ChatGLM-6B 结合 langchain 实现本地知识库 QA Bot](https://www.heywhale.com/mw/project/643977aa446c45f4592a1e59)

第三方评测：
* [Measuring Massive Multitask Chinese Understanding](https://arxiv.org/abs/2304.12986)

更多开源项目参见 [PROJECT.md](PROJECT.md)

## 使用方式

### 硬件需求

| **量化等级**   | **最低 GPU 显存**（推理） | **最低 GPU 显存**（高效参数微调） |
| -------------- | ------------------------- | --------------------------------- |
| FP16（无量化） | 13 GB                     | 14 GB                             |
| INT8           | 8 GB                     | 9 GB                             |
| INT4           | 6 GB                      | 7 GB                              |
### 环境安装

使用 pip 安装依赖：`pip install -r requirements.txt`，其中 `transformers` 库版本推荐为 `4.27.1`，但理论上不低于 `4.23.1` 即可。

此外，如果需要在 cpu 上运行量化后的模型，还需要安装 `gcc` 与 `openmp`。多数 Linux 发行版默认已安装。对于 Windows ，可在安装 [TDM-GCC](https://jmeubank.github.io/tdm-gcc/) 时勾选 `openmp`。 Windows 测试环境 `gcc` 版本为 `TDM-GCC 10.3.0`， Linux 为 `gcc 11.3.0`。在 MacOS 上请参考 [Q1](FAQ.md#q1)。

### 代码调用 

可以通过如下代码调用 ChatGLM-6B 模型来生成对话：

```python
>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
>>> model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
>>> model = model.eval()
>>> response, history = model.chat(tokenizer, "你好", history=[])
>>> print(response)
你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。
>>> response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history)
>>> print(response)
晚上睡不着可能会让你感到焦虑或不舒服,但以下是一些可以帮助你入睡的方法:

1. 制定规律的睡眠时间表:保持规律的睡眠时间表可以帮助你建立健康的睡眠习惯,使你更容易入睡。尽量在每天的相同时间上床,并在同一时间起床。
2. 创造一个舒适的睡眠环境:确保睡眠环境舒适,安静,黑暗且温度适宜。可以使用舒适的床上用品,并保持房间通风。
3. 放松身心:在睡前做些放松的活动,例如泡个热水澡,听些轻柔的音乐,阅读一些有趣的书籍等,有助于缓解紧张和焦虑,使你更容易入睡。
4. 避免饮用含有咖啡因的饮料:咖啡因是一种刺激性物质,会影响你的睡眠质量。尽量避免在睡前饮用含有咖啡因的饮料,例如咖啡,茶和可乐。
5. 避免在床上做与睡眠无关的事情:在床上做些与睡眠无关的事情,例如看电影,玩游戏或工作等,可能会干扰你的睡眠。
6. 尝试呼吸技巧:深呼吸是一种放松技巧,可以帮助你缓解紧张和焦虑,使你更容易入睡。试着慢慢吸气,保持几秒钟,然后缓慢呼气。

如果这些方法无法帮助你入睡,你可以考虑咨询医生或睡眠专家,寻求进一步的建议。
```
模型的实现仍然处在变动中。如果希望固定使用的模型实现以保证兼容性，可以在 `from_pretrained` 的调用中增加 `revision="v1.1.0"` 参数。`v1.1.0` 是当前最新的版本号，完整的版本列表参见 [Change Log](https://huggingface.co/THUDM/chatglm-6b#change-log)。

### 从本地加载模型
以上代码会由 `transformers` 自动下载模型实现和参数。完整的模型实现可以在 [Hugging Face Hub](https://huggingface.co/THUDM/chatglm-6b)。如果你的网络环境较差，下载模型参数可能会花费较长时间甚至失败。此时可以先将模型下载到本地，然后从本地加载。

从 Hugging Face Hub 下载模型需要先[安装Git LFS](https://docs.github.com/zh/repositories/working-with-files/managing-large-files/installing-git-large-file-storage)，然后运行
```Shell
git clone https://huggingface.co/THUDM/chatglm-6b
```

如果你从 Hugging Face Hub 上下载 checkpoint 的速度较慢，可以只下载模型实现
```Shell
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/THUDM/chatglm-6b
```
然后从[这里](https://cloud.tsinghua.edu.cn/d/fb9f16d6dc8f482596c2/)手动下载模型参数文件，并将下载的文件替换到本地的 `chatglm-6b` 目录下。

将模型下载到本地之后，将以上代码中的 `THUDM/chatglm-6b` 替换为你本地的 `chatglm-6b` 文件夹的路径，即可从本地加载模型。

**Optional** 模型的实现仍然处在变动中。如果希望固定使用的模型实现以保证兼容性，可以执行
```Shell
git checkout v1.1.0
```

## Demo & API

我们提供了一个基于 [Gradio](https://gradio.app) 的网页版 Demo 和一个命令行 Demo。使用时首先需要下载本仓库：

```shell
git clone https://github.com/THUDM/ChatGLM-6B
cd ChatGLM-6B
```

### 网页版 Demo

![web-demo](resources/web-demo.gif)

首先安装 Gradio：`pip install gradio`，然后运行仓库中的 [web_demo.py](web_demo.py)： 

```shell
python web_demo.py
```

程序会运行一个 Web Server，并输出地址。在浏览器中打开输出的地址即可使用。最新版 Demo 实现了打字机效果，速度体验大大提升。注意，由于国内 Gradio 的网络访问较为缓慢，启用 `demo.queue().launch(share=True, inbrowser=True)` 时所有网络会经过 Gradio 服务器转发，导致打字机体验大幅下降，现在默认启动方式已经改为 `share=False`，如有需要公网访问的需求，可以重新修改为 `share=True` 启动。

感谢 [@AdamBear](https://github.com/AdamBear) 实现了基于 Streamlit 的网页版 Demo，运行方式见[#117](https://github.com/THUDM/ChatGLM-6B/pull/117).

### 命令行 Demo

![cli-demo](resources/cli-demo.png)

运行仓库中 [cli_demo.py](cli_demo.py)：

```shell
python cli_demo.py
```

程序会在命令行中进行交互式的对话，在命令行中输入指示并回车即可生成回复，输入 `clear` 可以清空对话历史，输入 `stop` 终止程序。

### API部署
首先需要安装额外的依赖 `pip install fastapi uvicorn`，然后运行仓库中的 [api.py](api.py)：
```shell
python api.py
```
默认部署在本地的 8000 端口，通过 POST 方法进行调用
```shell
curl -X POST "http://127.0.0.1:8000" \
     -H 'Content-Type: application/json' \
     -d '{"prompt": "你好", "history": []}'
```
得到的返回值为
```shell
{
  "response":"你好👋！我是人工智能助手 ChatGLM-6B，很高兴见到你，欢迎问我任何问题。",
  "history":[["你好","你好👋！我是人工智能助手 ChatGLM-6B，很高兴见到你，欢迎问我任何问题。"]],
  "status":200,
  "time":"2023-03-23 21:38:40"
}
```

## 低成本部署
### 模型量化
默认情况下，模型以 FP16 精度加载，运行上述代码需要大概 13GB 显存。如果你的 GPU 显存有限，可以尝试以量化方式加载模型，使用方法如下：

```python
# 按需修改，目前只支持 4/8 bit 量化
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).quantize(8).half().cuda()
```

进行 2 至 3 轮对话后，8-bit 量化下 GPU 显存占用约为 10GB，4-bit 量化下仅需 6GB 占用。随着对话轮数的增多，对应消耗显存也随之增长，由于采用了相对位置编码，理论上 ChatGLM-6B 支持无限长的 context-length，但总长度超过 2048（训练长度）后性能会逐渐下降。

模型量化会带来一定的性能损失，经过测试，ChatGLM-6B 在 4-bit 量化下仍然能够进行自然流畅的生成。使用 [GPT-Q](https://arxiv.org/abs/2210.17323) 等量化方案可以进一步压缩量化精度/提升相同量化精度下的模型性能，欢迎大家提出对应的 Pull Request。

量化过程需要在内存中首先加载 FP16 格式的模型，消耗大概 13GB 的内存。如果你的内存不足的话，可以直接加载量化后的模型，INT4 量化后的模型仅需大概 5.2GB 的内存：
```python
# INT8 量化的模型将"THUDM/chatglm-6b-int4"改为"THUDM/chatglm-6b-int8"
model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True).half().cuda()
```
量化模型的参数文件也可以从[这里](https://cloud.tsinghua.edu.cn/d/674208019e314311ab5c/)手动下载。

### CPU 部署
如果你没有 GPU 硬件的话，也可以在 CPU 上进行推理，但是推理速度会更慢。使用方法如下（需要大概 32GB 内存）
```python
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
```

如果你的内存不足，可以直接加载量化后的模型：
```python
# INT8 量化的模型将"THUDM/chatglm-6b-int4"改为"THUDM/chatglm-6b-int8"
model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4",trust_remote_code=True).float()
```

如果遇到了报错 `Could not find module 'nvcuda.dll'` 或者 `RuntimeError: Unknown platform: darwin` (MacOS) ，请[从本地加载模型](README.md#从本地加载模型)

### Mac 部署
对于搭载了 Apple Silicon 或者 AMD GPU 的Mac，可以使用 MPS 后端来在 GPU 上运行 ChatGLM-6B。需要参考 Apple 的 [官方说明](https://developer.apple.com/metal/pytorch) 安装 PyTorch-Nightly（正确的版本号应该是2.1.0.dev2023xxxx，而不是2.0.0）。

目前在 MacOS 上只支持[从本地加载模型](README.md#从本地加载模型)。将代码中的模型加载改为从本地加载，并使用 mps 后端：
```python
model = AutoModel.from_pretrained("your local path", trust_remote_code=True).half().to('mps')
```

加载半精度的 ChatGLM-6B 模型需要大概 13GB 内存。内存较小的机器（比如 16GB 内存的 MacBook Pro），在空余内存不足的情况下会使用硬盘上的虚拟内存，导致推理速度严重变慢。此时可以使用量化后的模型如 chatglm-6b-int4。因为 GPU 上量化的 kernel 是使用 CUDA 编写的，因此无法在 MacOS 上使用，只能使用 CPU 进行推理。
```python
# INT8 量化的模型将"THUDM/chatglm-6b-int4"改为"THUDM/chatglm-6b-int8"
model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4",trust_remote_code=True).float()
```
为了充分使用 CPU 并行，还需要[单独安装 OpenMP](FAQ.md#q1)。

### 多卡部署
如果你有多张 GPU，但是每张 GPU 的显存大小都不足以容纳完整的模型，那么可以将模型切分在多张GPU上。首先安装 accelerate: `pip install accelerate`，然后通过如下方法加载模型：
```python
from utils import load_model_on_gpus
model = load_model_on_gpus("THUDM/chatglm-6b", num_gpus=2)
```
即可将模型部署到两张 GPU 上进行推理。你可以将 `num_gpus` 改为你希望使用的 GPU 数。默认是均匀切分的，你也可以传入 `device_map` 参数来自己指定。 

## 高效参数微调
基于 [P-tuning v2](https://github.com/THUDM/P-tuning-v2) 的高效参数微调。具体使用方法详见 [ptuning/README.md](ptuning/README.md)。

## ChatGLM-6B 示例

以下是一些使用 `web_demo.py` 得到的示例截图。更多 ChatGLM-6B 的可能，等待你来探索发现！

<details><summary><b>自我认知</b></summary>

![](examples/self-introduction.png)

</details>

<details><summary><b>提纲写作</b></summary>

![](examples/blog-outline.png)

</details>

<details><summary><b>文案写作</b></summary>

![](examples/ad-writing-2.png)

![](examples/comments-writing.png)

</details>

<details><summary><b>邮件写作助手</b></summary>

![](examples/email-writing-1.png)

![](examples/email-writing-2.png)

</details>

<details><summary><b>信息抽取</b></summary>

![](examples/information-extraction.png)

</details>

<details><summary><b>角色扮演</b></summary>

![](examples/role-play.png)

</details>

<details><summary><b>评论比较</b></summary>

![](examples/sport.png)

</details>

<details><summary><b>旅游向导</b></summary>

![](examples/tour-guide.png)

</details>

## 局限性

由于 ChatGLM-6B 的小规模，其能力仍然有许多局限性。以下是我们目前发现的一些问题：

- 模型容量较小：6B 的小容量，决定了其相对较弱的模型记忆和语言能力。在面对许多事实性知识任务时，ChatGLM-6B 可能会生成不正确的信息；它也不擅长逻辑类问题（如数学、编程）的解答。
    <details><summary><b>点击查看例子</b></summary>
    
    ![](limitations/factual_error.png)
    
    ![](limitations/math_error.png)
    
    </details>
  
- 产生有害说明或有偏见的内容：ChatGLM-6B 只是一个初步与人类意图对齐的语言模型，可能会生成有害、有偏见的内容。（内容可能具有冒犯性，此处不展示）

- 英文能力不足：ChatGLM-6B 训练时使用的指示/回答大部分都是中文的，仅有极小一部分英文内容。因此，如果输入英文指示，回复的质量远不如中文，甚至与中文指示下的内容矛盾，并且出现中英夹杂的情况。

- 易被误导，对话能力较弱：ChatGLM-6B 对话能力还比较弱，而且 “自我认知” 存在问题，并很容易被误导并产生错误的言论。例如当前版本的模型在被误导的情况下，会在自我认知上发生偏差。
    <details><summary><b>点击查看例子</b></summary>

    ![](limitations/self-confusion_google.jpg)
    
    ![](limitations/self-confusion_openai.jpg)
    
    ![](limitations/self-confusion_tencent.jpg)
    
    </details>

## 协议

本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，ChatGLM-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。ChatGLM-6B 权重对学术研究**完全开放**，在填写[问卷](https://open.bigmodel.cn/mla/form)进行登记后**亦允许免费商业使用**。

## 引用

如果你觉得我们的工作有帮助的话，请考虑引用下列论文

```
@misc{glm2024chatglm,
      title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools}, 
      author={Team GLM and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang},
      year={2024},
      eprint={2406.12793},
      archivePrefix={arXiv},
      primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
}
```
