Positional Skip-wise Training for Efficient Context Window Extension of LLMs to Extremely Length 
import random
import argparse
import re
from dataclasses import dataclass, field
from typing import Optional, Dict, Sequence
from pathlib import Path
import jsonlines

import torch
import torch.distributed
import transformers
import deepspeed
import evaluate
import datasets
import numpy as np
from torch.utils.data import Dataset
from torch.nn import CrossEntropyLoss
from transformers import LlamaTokenizer, pipeline
from datasets import load_dataset
from evaluate import logging
from tqdm import tqdm

from my_modeling_llama import LlamaForCausalLM
from my_configuration_llama import LlamaConfig
from train_skipos import smart_tokenizer_and_embedding_resize, DEFAULT_BOS_TOKEN, DEFAULT_EOS_TOKEN, DEFAULT_PAD_TOKEN, DEFAULT_UNK_TOKEN

gpu_device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')


def generate_prompt(max_tokens=16384):
    """Generates a text file and inserts an execute line at a random position."""
    # n_garbage_prefix = random.randint(0, n_garbage)
    n_garbage_total = (max_tokens - 32 - 26 - 11) // 25 
    n_garbage_prefix = random.randint(0, n_garbage_total)
    n_garbage_suffix = n_garbage_total - n_garbage_prefix

    task_description = "There is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there." # 32 tokens
    garbage = "The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again." # 25 tokens
    garbage_prefix = garbage * n_garbage_prefix
    garbage_suffix = garbage * n_garbage_suffix
    pass_key = random.randint(1, 50000)
    information_line = f"The pass key is {pass_key}. Remember it. {pass_key} is the pass key." # 26 tokens
    final_question = "What is the pass key? The pass key is" # 11 tokens
    lines = [
        task_description,
        garbage_prefix,
        information_line,
        garbage_suffix,
        final_question
    ]
    return "\n".join(lines), pass_key


def test_model(model, tokenizer, prompt_text, pass_key):
    
    model_input = tokenizer.encode(prompt_text, return_tensors="pt", max_length=100000, truncation=True).to(gpu_device)

    response = model.generate(model_input, num_return_sequences=1, max_new_tokens=10)
    response = tokenizer.batch_decode(response[:, model_input.shape[1]:], skip_special_tokens=True)[0]
    print(response)

    assert f"The pass key is {pass_key}" in prompt_text

    try:
        pass_key = int(re.search(r'\d+', response).group())
    except:
        pass_key = response[:20]

    return pass_key


def main():

    # add parser
    parser = argparse.ArgumentParser()
    parser.add_argument("--min_length", type=int, default=500)
    parser.add_argument("--max_length", type=int, default=1000)
    parser.add_argument("--step", type=int, default=500)
    parser.add_argument("--eval_nums", type=int, default=50)
    parser.add_argument("--batch_size", type=int, default=1)
    parser.add_argument("--model_max_position_embeddings", type=int, default=2048)
    parser.add_argument("--rope_scaling_factor", type=float, default=1.0)
    parser.add_argument("--rope_scaling_type", type=str, default=None)
    parser.add_argument("--input_field", type=str, default="text")
    parser.add_argument("--model_name", type=str, default="llama-7b")
    parser.add_argument("--path_to_ckp", type=str, default="/home/v-daweizhu/teamdrive/model/llama-7b")
    parser.add_argument("--path_to_output_dir", type=str, default="results/passkey")
    args = parser.parse_args()

    # model_name_or_path = "/home/v-daweizhu/teamdrive/skipos/results/2k-32k-v5/checkpoint-500"
    model_name_or_path = args.path_to_ckp

    config = LlamaConfig.from_pretrained(model_name_or_path)
    scaled_max_position_embeddings=int(args.model_max_position_embeddings * args.rope_scaling_factor)

    if config.rope_scaling is None:
        if args.rope_scaling_type is not None:
            config.rope_scaling={"type": args.rope_scaling_type, "factor": args.rope_scaling_factor}
            config.max_position_embeddings=scaled_max_position_embeddings
            

    config.use_cache=False

    model = LlamaForCausalLM.from_pretrained(pretrained_model_name_or_path=model_name_or_path, config=config, torch_dtype=torch.float16)
    model.to(gpu_device)

    tokenizer = LlamaTokenizer.from_pretrained(model_name_or_path, use_fast=True)
    smart_tokenizer_and_embedding_resize(
        special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN),
        tokenizer=tokenizer,
        model=model,
    )
    tokenizer.add_special_tokens(
        {
            "eos_token": DEFAULT_EOS_TOKEN,
            "bos_token": DEFAULT_BOS_TOKEN,
            "unk_token": DEFAULT_UNK_TOKEN,
        }
    )

    result_list = list()
    length_list = [2048,4096,6134,8192,10240,12288,14336,16384,20480,24576,28672,32768]
    # length_list = [16384,20480,24576,28672,32768]
    for context_size in length_list:
        if context_size == scaled_max_position_embeddings:
            context_size -= 100
        # if context_size > scaled_max_position_embeddings:
        #     break
        print(f"context_size: {context_size}")
        correct_cnt = 0
        result_dict = {"scaled_length": scaled_max_position_embeddings, "context_size": context_size}
        iter_nums = 50
        for i in tqdm(range(iter_nums)):
            prompt_text, pass_key = generate_prompt(context_size)
            pred = test_model(model, tokenizer, prompt_text, pass_key)
            result = "Pass!" if pred == pass_key else "Fail!"
            correct_cnt += 1 if pred == pass_key else 0
            case_report = f"pred: {pred}, ans: {pass_key}, result: {result}"
            result_dict[f"case{i}"] = case_report
            print(case_report)
        print(f"correct_rate: {correct_cnt/iter_nums}")
        result_dict["correct_rate"] = correct_cnt/iter_nums
        result_list.append(result_dict)
    
    root_dir = Path(__file__).parent.parent
    path_to_output_fn = (root_dir / args.path_to_output_dir / f"{args.model_name}.jsonl").as_posix()

    with jsonlines.open(path_to_output_fn, "w") as writer:
        writer.write_all(result_list)
    




if __name__ == "__main__":
    main()
src/eval_passkey.py
dwzhu-pku/PoSE
import copy
import random
import argparse
from dataclasses import dataclass, field
from typing import Optional, Dict, Sequence
from pathlib import Path

import torch
import torch.distributed
import transformers
import deepspeed
import evaluate
import datasets
import numpy as np
from torch.utils.data import Dataset
from torch.nn import CrossEntropyLoss
from transformers import LlamaTokenizer, AutoTokenizer, AutoConfig
from datasets import load_dataset
from evaluate import logging
from tqdm import tqdm

from my_modeling_llama import LlamaForCausalLM
from my_modeling_gptj import GPTJForCausalLM
from my_modeling_baichuan import BaichuanForCausalLM
from my_configuration_llama import LlamaConfig
from my_configuration_gptj import GPTJConfig
from my_configuration_baichuan import BaichuanConfig
from tokenization_baichuan import BaichuanTokenizer

from train_pose import smart_tokenizer_and_embedding_resize, DEFAULT_BOS_TOKEN, DEFAULT_EOS_TOKEN, DEFAULT_PAD_TOKEN, DEFAULT_UNK_TOKEN

gpu_device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')



def compute_perplexity(
    encodings, model, tokenizer, add_start_token: bool = True, max_length=None, sliding_window_step=256, truncate=False, aggressive_memory=False
):
    r"""Compute "sliding window" perplexity on a dataset. Validated against the calculations reported in arXiv 2306.15595"""
    device = "cuda" if torch.cuda.is_available() else "cpu"

    if add_start_token:
        # leave room for <BOS> token to be added:
        assert (
            tokenizer.bos_token is not None
        ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
        max_tokenized_len = max_length - 1
    else:
        max_tokenized_len = max_length

    encoded_texts = encodings["input_ids"]
    attn_masks = encodings["attention_mask"]

    if max_length and truncate:
        encoded_texts = [x[0:max_tokenized_len] for x in encoded_texts]
        attn_masks = [x[0:max_tokenized_len] for x in attn_masks]
        sliding_window_step = max_tokenized_len

    pbar = tqdm(total=len(encoded_texts))
    nlls = []
    total_nll = torch.tensor(0,dtype=torch.float64).to(device)
    total_token_cnt = 0
    for encoding_index in range(0, len(encoded_texts)):

        labels = torch.tensor(encoded_texts[encoding_index:encoding_index+1])
        seq_len = labels.size(1)

        prev_end_loc = 0
        for begin_loc in range(0, seq_len, sliding_window_step):

            end_loc = min(begin_loc + max_tokenized_len, seq_len)
            trg_len = end_loc - prev_end_loc
            input_ids = labels[:, begin_loc:end_loc].to(device)

            if add_start_token:
                bos_tokens_tensor = torch.tensor(
                    [[tokenizer.bos_token_id]] * input_ids.size(dim=0)).to(device)
                input_ids = torch.cat(
                    [bos_tokens_tensor, input_ids], dim=1)

            target_ids = input_ids.clone()
            target_ids[:, :-trg_len] = -100

            with torch.no_grad():
                outputs = model(input_ids, labels=target_ids)
                neg_log_likelihood = outputs.loss
                total_nll += neg_log_likelihood * trg_len
                total_token_cnt += trg_len
            
            nlls.append(neg_log_likelihood)

            ppl = float(torch.exp(total_nll / total_token_cnt).float().cpu())
            pbar.set_postfix(ppl=ppl)

            prev_end_loc = end_loc
            if end_loc == seq_len:
                break

        pbar.update(1)

    ppl = float(torch.exp(total_nll / total_token_cnt).float().cpu())
    return {"mean_perplexity": ppl}


def main():

    # add parser
    parser = argparse.ArgumentParser()
    parser.add_argument("--min_input_tokens", type=int, default=500)
    parser.add_argument("--max_input_tokens", type=int, default=1000)
    parser.add_argument("--step", type=int, default=500)
    parser.add_argument("--eval_nums", type=int, default=50)
    parser.add_argument("--batch_size", type=int, default=1)
    parser.add_argument("--sliding_window_step", type=int, default=256)
    parser.add_argument('--window_length_list', type=int, nargs='+', default=[])
    parser.add_argument("--truncate", action="store_true", default=False)
    parser.add_argument("--model_max_position_embeddings", type=int, default=2048)
    parser.add_argument("--rope_scaling_factor", type=float, default=1.0)
    parser.add_argument("--rope_scaling_type", type=str, default=None)
    parser.add_argument("--input_field", type=str, default="text")
    parser.add_argument("--model_name", type=str, default="llama-7b")
    parser.add_argument("--path_to_ckp", type=str, default="/home/v-daweizhu/teamdrive/model/llama-7b")
    parser.add_argument("--dataset_name", type=str, default="scrolls-gov_report")
    parser.add_argument("--path_to_dataset", type=str, default="")
    parser.add_argument("--path_to_output_dir", type=str, default="results/ppls")
    args = parser.parse_args()

    model_name_or_path = args.path_to_ckp

    Config, CausalLM, Tokenizer = None, None, None

    if "llama" in args.model_name:
        Config, CausalLM, Tokenizer = LlamaConfig, LlamaForCausalLM, AutoTokenizer
    elif "gptj" in args.model_name:
        Config, CausalLM, Tokenizer = GPTJConfig, GPTJForCausalLM, AutoTokenizer
    elif "baichuan" in args.model_name:
        Config, CausalLM, Tokenizer = BaichuanConfig, BaichuanForCausalLM, BaichuanTokenizer


    config = Config.from_pretrained(model_name_or_path)
    scaled_max_position_embeddings=int(args.model_max_position_embeddings * args.rope_scaling_factor)

    if config.rope_scaling is None:
        if args.rope_scaling_type is not None:
            config.rope_scaling={"type": args.rope_scaling_type, "factor": args.rope_scaling_factor}
            config.max_position_embeddings=scaled_max_position_embeddings
            if args.rope_scaling_type == "yarn":
                config.rope_scaling["original_max_position_embeddings"] = args.model_max_position_embeddings
            
    config.use_cache=False

    model = CausalLM.from_pretrained(pretrained_model_name_or_path=model_name_or_path, config=config,torch_dtype=torch.float16)
    model.to(gpu_device)

    tokenizer = Tokenizer.from_pretrained(model_name_or_path, use_fast=False if "baichuan" in args.model_name else True)
    
    if not "baichuan" in args.model_name:
        smart_tokenizer_and_embedding_resize(
            special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN),
            tokenizer=tokenizer,
            model=model,
        )
    if "llama" in args.model_name:
        tokenizer.add_special_tokens(
            {
                "eos_token": DEFAULT_EOS_TOKEN,
                "bos_token": DEFAULT_BOS_TOKEN,
                "unk_token": DEFAULT_UNK_TOKEN,
            }
        )

    if "scrolls" in args.dataset_name:
        args.input_field = "input"
    elif "pile" in args.dataset_name:
        args.input_field = "text"
    elif "proof" in args.dataset_name:
        args.input_field = "text"

    input_texts = load_dataset("json", data_files=args.path_to_dataset, split="train")

    def tokenize(example):
        tokenized = tokenizer(
            example[args.input_field],
            add_special_tokens=False,
            padding=True,
            truncation=True,
            max_length=args.max_input_tokens - 1, # leave room for <BOS> token to be added
            return_attention_mask=True,
        )
        example["input_ids"] = tokenized["input_ids"]
        example["attention_mask"] = tokenized["attention_mask"]
        example["tokenized_len"] = len(tokenized["input_ids"])
        return example

    input_texts = input_texts.map(tokenize,num_proc=2)

    if args.min_input_tokens:
        input_texts = input_texts.filter(
            lambda x: x["tokenized_len"] >= args.min_input_tokens - 1)
    if args.eval_nums:
        input_texts = input_texts[:args.eval_nums]

    ppl_list = []
    context_window_size = args.window_length_list
    print(context_window_size)
    # context_window_size = [8192,16384]

    for ctx_size in context_window_size:
        # if args.truncate is True, we calucate the ppl on the whole input text
        # otherwise, we calucate the ppl with sliding window
        ppl = compute_perplexity(encodings=input_texts, model=model, tokenizer=tokenizer, add_start_token=True, max_length=ctx_size, sliding_window_step=args.sliding_window_step, truncate=args.truncate)["mean_perplexity"]

        print(f"model: {args.model_name}; context window size: {ctx_size}; ppl: {ppl}")
        
        ppl_list.append(ppl)

    root_dir = Path(__file__).parent.parent
    path_to_output_fn = (root_dir / args.path_to_output_dir / f"{args.model_name}+{args.dataset_name}").as_posix()
    with open(path_to_output_fn, "w") as f:
        f.write(f"model: {args.model_name}\n")
        f.write(f"length: {', '.join(map(str, context_window_size))}\n")
        f.write(f"ppl: {', '.join(map(str, ppl_list))}\n")


if __name__ == "__main__":
    main()

src/eval_ppl.py
dwzhu-pku/PoSE
# Modification Copyright 2023 Dawei Zhu
# Copyright 2023 Baichuan Inc. All Rights Reserved.

# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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 transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


logger = logging.get_logger(__name__)


class BaichuanConfig(PretrainedConfig):
    model_type = "baichuan"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=125696,
        hidden_size=4096,
        intermediate_size=11008,
        num_hidden_layers=32,
        num_attention_heads=32,
        hidden_act="silu",
        max_position_embeddings=4096,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        tie_word_embeddings=False,
        z_loss_weight=0,
        rope_scaling=None,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        
        self.rope_scaling = rope_scaling
        self._rope_scaling_validation()

        self.z_loss_weight = z_loss_weight
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )

    def _rope_scaling_validation(self):
        """
        Validate the `rope_scaling` configuration.
        """
        if self.rope_scaling is None:
            return

        if not isinstance(self.rope_scaling, dict):
            raise ValueError(
                "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
                f"got {self.rope_scaling}"
            )
        rope_scaling_type = self.rope_scaling.get("type", None)
        rope_scaling_factor = self.rope_scaling.get("factor", None)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic", "vanilla_ntk", "yarn"]:
            raise ValueError(
                f"`rope_scaling`'s name field must be one of ['linear', 'smart_linear', 'dynamic', 'vanilla_ntk', 'yarn'], got {rope_scaling_type}"
            )
        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")

src/my_configuration_baichuan.py
dwzhu-pku/PoSE
# coding=utf-8
# Modification Copyright 2023 Dawei Zhu
# Copyright 2021 The EleutherAI and HuggingFace Teams. 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.
""" GPT-J model configuration"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from transformers import PreTrainedTokenizer, TensorType, is_torch_available
from transformers.configuration_utils import PretrainedConfig
from transformers.onnx import OnnxConfigWithPast, PatchingSpec
from transformers.utils import logging


logger = logging.get_logger(__name__)

GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}


class GPTJConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`GPTJModel`]. It is used to instantiate a GPT-J
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the GPT-J
    [EleutherAI/gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B) architecture. Configuration objects inherit from
    [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`]
    for more information.

    Args:
        vocab_size (`int`, *optional*, defaults to 50400):
            Vocabulary size of the GPT-J model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`GPTJModel`].
        n_positions (`int`, *optional*, defaults to 2048):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        n_embd (`int`, *optional*, defaults to 4096):
            Dimensionality of the embeddings and hidden states.
        n_layer (`int`, *optional*, defaults to 28):
            Number of hidden layers in the Transformer encoder.
        n_head (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        rotary_dim (`int`, *optional*, defaults to 64):
            Number of dimensions in the embedding that Rotary Position Embedding is applied to.
        n_inner (`int`, *optional*, defaults to None):
            Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
        activation_function (`str`, *optional*, defaults to `"gelu_new"`):
            Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
        resid_pdrop (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        embd_pdrop (`int`, *optional*, defaults to 0.1):
            The dropout ratio for the embeddings.
        attn_pdrop (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention.
        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
            The epsilon to use in the layer normalization layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).

    Example:

    ```python
    >>> from transformers import GPTJModel, GPTJConfig

    >>> # Initializing a GPT-J 6B configuration
    >>> configuration = GPTJConfig()

    >>> # Initializing a model from the configuration
    >>> model = GPTJModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "gptj"
    attribute_map = {
        "max_position_embeddings": "n_positions",
        "hidden_size": "n_embd",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }

    def __init__(
        self,
        vocab_size=50400,
        n_positions=2048,
        n_embd=4096,
        n_layer=28,
        n_head=16,
        rotary_dim=64,
        n_inner=None,
        activation_function="gelu_new",
        resid_pdrop=0.0,
        embd_pdrop=0.0,
        attn_pdrop=0.0,
        layer_norm_epsilon=1e-5,
        initializer_range=0.02,
        use_cache=True,
        bos_token_id=50256,
        eos_token_id=50256,
        tie_word_embeddings=False,
        rope_scaling=None,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.n_positions = n_positions
        self.n_embd = n_embd
        self.n_layer = n_layer
        self.n_head = n_head
        self.n_inner = n_inner
        self.rotary_dim = rotary_dim
        self.activation_function = activation_function
        self.resid_pdrop = resid_pdrop
        self.embd_pdrop = embd_pdrop
        self.attn_pdrop = attn_pdrop
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range
        self.use_cache = use_cache

        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id

        self.rope_scaling = rope_scaling

        super().__init__(
            bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
        )


# Copied from transformers.models.gpt2.configuration_gpt2.GPT2OnnxConfig
class GPTJOnnxConfig(OnnxConfigWithPast):
    def __init__(
        self,
        config: PretrainedConfig,
        task: str = "default",
        patching_specs: List[PatchingSpec] = None,
        use_past: bool = False,
    ):
        super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
        if not getattr(self._config, "pad_token_id", None):
            # TODO: how to do that better?
            self._config.pad_token_id = 0

    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
        if self.use_past:
            self.fill_with_past_key_values_(common_inputs, direction="inputs")
            common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"}
        else:
            common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}

        return common_inputs

    @property
    def num_layers(self) -> int:
        return self._config.n_layer

    @property
    def num_attention_heads(self) -> int:
        return self._config.n_head

    def generate_dummy_inputs(
        self,
        tokenizer: PreTrainedTokenizer,
        batch_size: int = -1,
        seq_length: int = -1,
        is_pair: bool = False,
        framework: Optional[TensorType] = None,
    ) -> Mapping[str, Any]:
        common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
        )

        # We need to order the input in the way they appears in the forward()
        ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})

        # Need to add the past_keys
        if self.use_past:
            if not is_torch_available():
                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
            else:
                import torch

                batch, seqlen = common_inputs["input_ids"].shape
                # Not using the same length for past_key_values
                past_key_values_length = seqlen + 2
                past_shape = (
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                ordered_inputs["past_key_values"] = [
                    (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(self.num_layers)
                ]

        ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
        if self.use_past:
            mask_dtype = ordered_inputs["attention_mask"].dtype
            ordered_inputs["attention_mask"] = torch.cat(
                [ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
            )

        return ordered_inputs

    @property
    def default_onnx_opset(self) -> int:
        return 13

src/my_configuration_gptj.py
dwzhu-pku/PoSE
# coding=utf-8
# Modification Copyright 2023 Dawei Zhu
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
""" LLaMA model configuration"""

# from ...configuration_utils import PretrainedConfig
# from ...utils import logging
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


logger = logging.get_logger(__name__)

LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}


class LlamaConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the LLaMA-7B.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 32000):
            Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`LlamaModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 11008):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_key_value_heads (`int`, *optional*):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details checkout [this
            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
            `num_attention_heads`.
        pretraining_tp (`int`, *optional*, defaults to `1`):
            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
            issue](https://github.com/pytorch/pytorch/issues/76232).
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling
            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
            these scaling strategies behave:
            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
            experimental feature, subject to breaking API changes in future versions.

        Example:

    ```python
    >>> from transformers import LlamaModel, LlamaConfig

    >>> # Initializing a LLaMA llama-7b style configuration
    >>> configuration = LlamaConfig()

    >>> # Initializing a model from the llama-7b style configuration
    >>> model = LlamaModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "llama"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=32000,
        hidden_size=4096,
        intermediate_size=11008,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=None,
        hidden_act="silu",
        max_position_embeddings=2048,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        pretraining_tp=1,
        tie_word_embeddings=False,
        rope_scaling=None,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads

        # for backward compatibility
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.pretraining_tp = pretraining_tp
        self.use_cache = use_cache
        self.rope_scaling = rope_scaling
        self._rope_scaling_validation()

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )

    def _rope_scaling_validation(self):
        """
        Validate the `rope_scaling` configuration.
        """
        if self.rope_scaling is None:
            return

        if not isinstance(self.rope_scaling, dict):
            raise ValueError(
                "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
                f"got {self.rope_scaling}"
            )
        rope_scaling_type = self.rope_scaling.get("type", None)
        rope_scaling_factor = self.rope_scaling.get("factor", None)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic", "vanilla_ntk", "yarn"]:
            raise ValueError(
                f"`rope_scaling`'s name field must be one of ['linear', 'dynamic', 'vanilla_ntk', 'yarn'], got {rope_scaling_type}"
            )
        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")

src/my_configuration_llama.py
dwzhu-pku/PoSE
# Modification Copyright 2023 Dawei Zhu
# Copyright 2023 Baichuan Inc. All Rights Reserved.

# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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 my_configuration_baichuan import BaichuanConfig
# from .generation_utils import build_chat_input, TextIterStreamer

import math
from typing import List, Optional, Tuple, Union
from threading import Thread

import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from torch.nn import functional as F
from transformers import PreTrainedModel, PretrainedConfig
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.generation.utils import GenerationConfig
from transformers.utils import logging, ContextManagers

import os
from contextlib import contextmanager
logger = logging.get_logger(__name__)

try:
    from xformers import ops as xops
except ImportError:
    xops = None
    logger.warning(
        "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\npip install xformers."
    )


# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
        input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
    """
    Make causal mask used for bi-directional self-attention.
    """
    bsz, tgt_len = input_ids_shape
    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
    mask_cond = torch.arange(mask.size(-1), device=device)
    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
    mask = mask.to(dtype)

    if past_key_values_length > 0:
        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)

def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
    """
    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
    """
    if len(mask.size()) == 3:
        bsz, src_len, _ = mask.size()
        tgt_len = tgt_len if tgt_len is not None else src_len
        expanded_mask = mask[:,None,:,:].expand(bsz, 1, tgt_len, src_len).to(dtype)
    else:
        bsz, src_len = mask.size()
        tgt_len = tgt_len if tgt_len is not None else src_len
        expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

    inverted_mask = 1.0 - expanded_mask

    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)


class RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        RMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)

        # convert into half-precision if necessary
        if self.weight.dtype in [torch.float16, torch.bfloat16]:
            hidden_states = hidden_states.to(self.weight.dtype)

        return self.weight * hidden_states


class RotaryEmbedding(torch.nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
        super().__init__()

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

        # Build here to make `torch.jit.trace` work.
        self._set_cos_sin_cache(
            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
        )

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
        freqs = torch.outer(t, self.inv_freq)
        emb = torch.cat((freqs, freqs), dim=-1)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32)
        self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32)


    def forward(self, x, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
        if seq_len > self.max_seq_len_cached:
            self.max_seq_len_cached = seq_len
            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

        elif self.cos_cached.device != x.device:
            self.cos_cached = self.cos_cached.to(x.device)
            self.sin_cached = self.sin_cached.to(x.device)  
        return (
            self.cos_cached[:, :, :, ...],
            self.sin_cached[:, :, :, ...],
        )

class LinearScalingRotaryEmbedding(RotaryEmbedding):

    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
        self.scaling_factor = scaling_factor
        super().__init__(dim, max_position_embeddings, base, device)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
        t = t / self.scaling_factor
        freqs = torch.outer(t, self.inv_freq)
        emb = torch.cat((freqs, freqs), dim=-1)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32)
        self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32)


class VanillaNTKScalingRotaryEmbedding(RotaryEmbedding):

    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
        self.scaling_factor = scaling_factor
        super().__init__(dim, max_position_embeddings, base, device)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len

        base = self.base * self.scaling_factor ** (self.dim / (self.dim - 2))
        inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
        freqs = torch.outer(t, self.inv_freq)
        emb = torch.cat((freqs, freqs), dim=-1)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.cos_cached = emb.cos()[None, None, :, :].to(torch.float32)
        self.sin_cached = emb.sin()[None, None, :, :].to(torch.float32)

def _yarn_find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048):
    return (dim * math.log(max_position_embeddings/(num_rotations * 2 * math.pi)))/(2 * math.log(base))

# Find dim range bounds based on rotations
def _yarn_find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048):
    low = math.floor(_yarn_find_correction_dim(
        low_rot, dim, base, max_position_embeddings))
    high = math.ceil(_yarn_find_correction_dim(
        high_rot, dim, base, max_position_embeddings))
    return max(low, 0), min(high, dim-1)  # Clamp values just in case

def _yarn_linear_ramp_mask(min, max, dim):
    if min == max:
        max += 0.001  # Prevent singularity

    linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
    ramp_func = torch.clamp(linear_func, 0, 1)
    return ramp_func

def _yarn_get_mscale(scale=1):
    if scale <= 1:
        return 1.0
    return 0.1 * math.log(scale) + 1.0


class YaRNScaledRotaryEmbedding(torch.nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, scale=1, original_max_position_embeddings=2048, extrapolation_factor=1, attn_factor=1, beta_fast=32, beta_slow=1, finetuned=False, device=None):
        super().__init__()

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        self.scale = scale
        self.original_max_position_embeddings = original_max_position_embeddings
        self.extrapolation_factor = extrapolation_factor
        self.attn_factor = attn_factor
        self.beta_fast = beta_fast
        self.beta_slow = beta_slow

        # self.yarn(device)
        self.revised_yarn(device)

        # Build here to make `torch.jit.trace` work.
        self.max_seq_len_cached = max_position_embeddings

        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.float32)
        freqs = torch.outer(t, self.inv_freq.to(device=t.device).to(t.dtype))
        # t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
        # freqs = torch.einsum("i,j->ij", t, self.inv_freq)

        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        dtype = torch.get_default_dtype()

        self.register_buffer("cos_cached", (emb.cos() * self.mscale)[None, None, :, :].to(dtype), persistent=False)
        self.register_buffer("sin_cached", (emb.sin() * self.mscale)[None, None, :, :].to(dtype), persistent=False)

    def forward(self, x, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
        if seq_len > self.max_seq_len_cached:
            print("*****notice******")
            self.max_seq_len_cached = seq_len

            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
            # Different from paper, but it uses a different permutation in order to obtain the same calculation
            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)

            self.register_buffer("cos_cached", (emb.cos() * self.mscale)[None, None, :, :].to(x.dtype), persistent=False)
            self.register_buffer("sin_cached", (emb.sin() * self.mscale)[None, None, :, :].to(x.dtype), persistent=False)
        return (
            self.cos_cached[:, :, :, ...].to(dtype=x.dtype),
            self.sin_cached[:, :, :, ...].to(dtype=x.dtype),
        )

    def yarn(self, device):
        pos_freqs = self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
        inv_freq_extrapolation = 1.0 / pos_freqs
        inv_freq_interpolation = 1.0 / (self.scale * pos_freqs)

        low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base, self.original_max_position_embeddings)
        inv_freq_mask = (1 - _yarn_linear_ramp_mask(low, high, self.dim // 2).float().to(device)) * self.extrapolation_factor # Get n-d rotational scaling corrected for extrapolation
        inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask

        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.mscale = float(_yarn_get_mscale(self.scale) * self.attn_factor) # Get n-d magnitude scaling corrected for interpolation

    def revised_yarn(self, device):
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))

        low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base, self.original_max_position_embeddings)
        inv_freq_mask = (1 - _yarn_linear_ramp_mask(low, high, self.dim // 2).float().to(device)) * self.extrapolation_factor

        inv_freq = inv_freq / ((1-inv_freq_mask)*self.scale + inv_freq_mask)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.mscale = float(_yarn_get_mscale(self.scale) * self.attn_factor)


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2:]
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(q, k, cos_, sin_, position_ids):
    cos = cos_.squeeze(1).squeeze(0)  # [seq_len, dim]
    sin = sin_.squeeze(1).squeeze(0)  # [seq_len, dim]
    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
    q_embed = (q.float() * cos) + (rotate_half(q.float()) * sin)
    k_embed = (k.float() * cos) + (rotate_half(k.float()) * sin)
    return q_embed.to(q.dtype), k_embed.to(k.dtype)


class MLP(nn.Module):
    def __init__(
            self,
            hidden_size: int,
            intermediate_size: int,
            hidden_act: str,
    ):
        super().__init__()
        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
        self.act_fn = ACT2FN[hidden_act]

    def forward(self, x):
        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


class Attention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""
    def __init__(self, config: BaichuanConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.max_position_embeddings = config.max_position_embeddings

        if (self.head_dim * self.num_heads) != self.hidden_size:
            raise ValueError(
                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                f" and `num_heads`: {self.num_heads})."
            )
        self.W_pack = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
        self._init_rope()
        # self.rotary_emb = RotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
        
    def _init_rope(self):
        if self.config.rope_scaling is None:
            self.rotary_emb = RotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
        else:
            scaling_type = self.config.rope_scaling["type"]
            scaling_factor = self.config.rope_scaling["factor"]
            if scaling_type == "linear":
                self.rotary_emb = LinearScalingRotaryEmbedding(
                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
                )
            elif scaling_type == "vanilla_ntk":
                self.rotary_emb = VanillaNTKScalingRotaryEmbedding(
                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
                )
            elif scaling_type == "yarn":
                original_max_position_embeddings = self.config.rope_scaling["original_max_position_embeddings"]
                self.rotary_emb = YaRNScaledRotaryEmbedding(
                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scale=scaling_factor, original_max_position_embeddings=original_max_position_embeddings
                )
            else:
                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def forward(
            self,
            hidden_states: torch.Tensor,
            attention_mask: Optional[torch.Tensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            past_key_value: Optional[Tuple[torch.Tensor]] = None,
            output_attentions: bool = False,
            use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        bsz, q_len, _ = hidden_states.size()

        proj = self.W_pack(hidden_states)
        proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
        query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-2]
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
        # [bsz, nh, t, hd]

        if past_key_value is not None:
            # reuse k, v, self_attention
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)

        past_key_value = (key_states, value_states) if use_cache else None
        if xops is not None and self.training:
            attn_weights = None
            query_states = query_states.transpose(1, 2)
            key_states = key_states.transpose(1, 2)
            value_states = value_states.transpose(1, 2)
            attn_output = xops.memory_efficient_attention(
                query_states, key_states, value_states, attn_bias=xops.LowerTriangularMask()
            )
        else:
            with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
                attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask = attention_mask)
            attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


class DecoderLayer(nn.Module):
    def __init__(self, config: BaichuanConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = Attention(config=config)
        self.mlp = MLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
        )
        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
            self,
            hidden_states: torch.Tensor,
            attention_mask: Optional[torch.Tensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            past_key_value: Optional[Tuple[torch.Tensor]] = None,
            output_attentions: Optional[bool] = False,
            use_cache: Optional[bool] = False,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:

        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


class BaichuanPreTrainedModel(PreTrainedModel):
    config_class = BaichuanConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["DecoderLayer"]
    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, BaichuanModel):
            module.gradient_checkpointing = value


class BaichuanModel(BaichuanPreTrainedModel):
    def __init__(self, config: BaichuanConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        self.gradient_checkpointing = False
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
        # create causal mask
        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        combined_attention_mask = None
        if input_shape[-1] > 1:
            combined_attention_mask = _make_causal_mask(
                input_shape,
                inputs_embeds.dtype,
                device=inputs_embeds.device,
                past_key_values_length=past_key_values_length,
            )

        if attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
                inputs_embeds.device
            )
            combined_attention_mask = (
                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
            )

        return combined_attention_mask

    def forward(
            self,
            input_ids: torch.LongTensor = None,
            attention_mask: Optional[torch.Tensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            past_key_values: Optional[List[torch.FloatTensor]] = None,
            inputs_embeds: Optional[torch.FloatTensor] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

        seq_length_with_past = seq_length
        past_key_values_length = 0

        if past_key_values is not None:
            past_key_values_length = past_key_values[0][0].shape[2]
            seq_length_with_past = seq_length_with_past + past_key_values_length

        if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(
                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
            )
            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
        else:
            position_ids = position_ids.view(-1, seq_length).long()

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)
        # embed positions
        if attention_mask is None:
            attention_mask = torch.ones(
                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
            )
        attention_mask = self._prepare_decoder_attention_mask(
            attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
        )

        hidden_states = inputs_embeds

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = () if use_cache else None

        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            past_key_value = past_key_values[idx] if past_key_values is not None else None

            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, output_attentions, None)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    position_ids,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_value,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


class NormHead(nn.Module):
    def __init__(self, hidden_size, vocab_size, bias=False):
        super().__init__()
        self.weight = nn.Parameter(torch.empty((vocab_size, hidden_size)))
        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        self.first_flag = True

    def forward(self, hidden_states):
        if self.training:
            norm_weight = nn.functional.normalize(self.weight)
            self.first_flag = True
        elif self.first_flag:
            self.first_flag = False
            self.weight = nn.Parameter(nn.functional.normalize(self.weight))
            norm_weight = self.weight
        else:
            norm_weight = self.weight
        return nn.functional.linear(hidden_states, norm_weight)

_init_weights = True
@contextmanager
def no_init_weights(_enable=True):
    global _init_weights
    old_init_weights = _init_weights
    if _enable:
        _init_weights = False
    try:
        yield
    finally:
        _init_weights = old_init_weights

class BaichuanForCausalLM(BaichuanPreTrainedModel):
    def __init__(self, config, *model_args, **model_kwargs):
        super().__init__(config, *model_args, **model_kwargs)
        self.model = BaichuanModel(config)

        self.lm_head = NormHead(config.hidden_size, config.vocab_size, bias=False)
        if hasattr(config, "quantization_config") and isinstance(config.quantization_config, dict) and config.quantization_config.get('load_in_4bit', False):
            try:
                from .quantizer import quantize_offline, init_model_weight_int4
            except ImportError:
                raise ImportError(f"Needs QLinear to run quantize.")
            quantize_offline(self, 4)
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model
    
    @classmethod
    def from_pretrained(
        cls,
        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
        *model_args,
        config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
        cache_dir: Optional[Union[str, os.PathLike]] = None,
        ignore_mismatched_sizes: bool = False,
        force_download: bool = False,
        local_files_only: bool = False,
        token: Optional[Union[str, bool]] = None,
        revision: str = "main",
        use_safetensors: bool = None,
        **kwargs,
    ):
        # Load config if we don't provide a configuration
        if not isinstance(config, PretrainedConfig):
            config_path = config if config is not None else pretrained_model_name_or_path
            config, model_kwargs = cls.config_class.from_pretrained(
                config_path,
                cache_dir=cache_dir,
                return_unused_kwargs=True,
                force_download=force_download,
                resume_download=False,
                proxies=None,
                local_files_only=local_files_only,
                token=token,
                revision=revision,
                subfolder="",
                _from_auto=False,
                _from_pipeline=None,
                **kwargs,
            )
        else:
            model_kwargs = kwargs
        
        if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']:
            try:
                from .quantizer import init_model_weight_int4
                from accelerate import init_empty_weights, dispatch_model, infer_auto_device_map
                from accelerate.utils import CustomDtype
                from accelerate.utils import get_balanced_memory
            except ImportError:
                raise ImportError(f"Needs import model weight init func to run quantize.") 
            # Instantiate model.
            init_contexts = [no_init_weights(_enable=True)]
            init_contexts.append(init_empty_weights())
            with ContextManagers(init_contexts):
                model = cls(config)
            
            model_file = os.path.join(pretrained_model_name_or_path, 'pytorch_model.bin')
            state_dict = torch.load(model_file, map_location="cpu") 
            model.is_quantized = True
            
            device_map = kwargs.pop("device_map", None)
            torch_dtype = kwargs.pop("torch_dtype", None)
            
            if device_map is not None:
                kwargs = {"no_split_module_classes": model._no_split_modules}
                target_dtype = CustomDtype.INT4
                max_memory = get_balanced_memory(
                    model,
                    dtype=target_dtype,
                    low_zero=(device_map == "balanced_low_0"),
                    max_memory=None,
                    **kwargs,
                )
                kwargs["max_memory"] = max_memory
                device_map = infer_auto_device_map(model, dtype=target_dtype, **kwargs)
                
            model = init_model_weight_int4(config, model, state_dict)
            
            # Set model in evaluation mode to deactivate DropOut modules by default
            model.eval()
            # If it is a model with generation capabilities, attempt to load the generation config
            if model.can_generate():
                try:
                    model.generation_config = GenerationConfig.from_pretrained(
                        pretrained_model_name_or_path,
                        cache_dir=cache_dir,
                        force_download=force_download,
                        resume_download=False,
                        proxies=None,
                        local_files_only=local_files_only,
                        token=token,
                        revision=revision,
                        subfolder="",
                        _from_auto=False,
                        _from_pipeline=None,
                        **kwargs,
                    )
                except (OSError, TypeError):
                    logger.info(
                        "Generation config file not found, using a generation config created from the model config."
                    )
                    pass
            
            if device_map is not None:
                dispatch_model(model, device_map=device_map)
            
            return model
        return super(BaichuanForCausalLM, cls).from_pretrained(pretrained_model_name_or_path, *model_args, 
                config=config, cache_dir=cache_dir, ignore_mismatched_sizes=ignore_mismatched_sizes, 
                force_download=force_download, local_files_only=local_files_only, token=token, revision=revision, 
                use_safetensors=use_safetensors, **kwargs)   

    def forward(
            self,
            input_ids: torch.LongTensor = None,
            attention_mask: Optional[torch.Tensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            past_key_values: Optional[List[torch.FloatTensor]] = None,
            inputs_embeds: Optional[torch.FloatTensor] = None,
            labels: Optional[torch.LongTensor] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            softmax_normalizer = shift_logits.max(-1).values ** 2
            z_loss = self.config.z_loss_weight * softmax_normalizer.mean()
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels) + z_loss

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
            self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        if past_key_values:
            input_ids = input_ids[:, -1:]

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -1].unsqueeze(-1)

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )
        return model_inputs

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
        return reordered_past

    def quantize(self, bits: int):
        try:
            from .quantizer import quantize_online
        except ImportError:
            raise ImportError(f"Needs QLinear to run quantize.")
        return quantize_online(self, bits)

    def chat(self, tokenizer, messages: List[dict], stream=False,
             generation_config: Optional[GenerationConfig]=None):
        generation_config = generation_config or self.generation_config
        input_ids = build_chat_input(self, tokenizer, messages, generation_config.max_new_tokens)
        if stream:
            streamer = TextIterStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
            Thread(target=self.generate, kwargs=dict(
                inputs=input_ids, streamer=streamer,
                generation_config=generation_config,
            )).start()
            return streamer
        else:
            outputs = self.generate(input_ids, generation_config=generation_config)
            response = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)
            return response

src/my_modeling_baichuan.py
dwzhu-pku/PoSE
# coding=utf-8
# Modification Copyright 2023 Dawei Zhu
# Copyright 2021 The EleutherAI and HuggingFace Teams. 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.
""" PyTorch GPT-J model."""

import warnings
import math
from typing import Optional, Tuple, Union

import numpy as np
import torch
import torch.fx
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel

from transformers.utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_torch_fx_proxy,
    logging,
)

from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
# from ...utils.model_parallel_utils import assert_device_map, get_device_map

from transformers.models.gptj.configuration_gptj import GPTJConfig


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "hf-internal-testing/tiny-random-gptj"
_REAL_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-j-6B"
_CONFIG_FOR_DOC = "GPTJConfig"


GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "EleutherAI/gpt-j-6B",
    # See all GPT-J models at https://huggingface.co/models?filter=gptj
]


# Inverse dim formula to find dim based on number of rotations
def _yarn_find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048):
    return (dim * math.log(max_position_embeddings/(num_rotations * 2 * math.pi)))/(2 * math.log(base))

# Find dim range bounds based on rotations
def _yarn_find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048):
    low = math.floor(_yarn_find_correction_dim(
        low_rot, dim, base, max_position_embeddings))
    high = math.ceil(_yarn_find_correction_dim(
        high_rot, dim, base, max_position_embeddings))
    return max(low, 0), min(high, dim-1)  # Clamp values just in case

def _yarn_linear_ramp_mask(min, max, dim):
    if min == max:
        max += 0.001  # Prevent singularity

    linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
    ramp_func = torch.clamp(linear_func, 0, 1)
    return ramp_func

def _yarn_get_mscale(scale=1):
    if scale <= 1:
        return 1.0
    return 0.1 * math.log(scale) + 1.0


def create_sinusoidal_positions(num_pos: int, dim: int, scaling_factor: int, scaling_type: str) -> torch.Tensor:
    inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim))
    gpu_device = inv_freq.device
    
    # sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.float), inv_freq).float()

    if scaling_type == "none":
        t = np.arange(num_pos*10, dtype=np.float64)
        t = torch.tensor(t, device=inv_freq.device, dtype=torch.float64)
        sinusoid_inp = torch.outer(t, inv_freq.to(device=t.device).to(t.dtype))
        
    elif scaling_type == "linear":
        t = np.arange(num_pos, dtype=np.float64)
        t = t / scaling_factor
        t = torch.tensor(t, device=inv_freq.device, dtype=torch.float64)
        sinusoid_inp = torch.outer(t, inv_freq.to(device=t.device).to(t.dtype))

    elif scaling_type == "vanilla_ntk":
        base = 10000 * scaling_factor ** (dim / (dim - 2))
        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(inv_freq.device) / dim))
        t = np.arange(num_pos, dtype=np.float64)
        t = torch.tensor(t, device=inv_freq.device, dtype=torch.float64)
        sinusoid_inp = torch.outer(t, inv_freq.to(device=t.device).to(t.dtype))

    elif scaling_type == "yarn":
        # calculate inv_freq
        base = 10000
        beta_fast = 32
        beta_slow = 1
        original_max_position_embeddings = 2048
        extrapolation_factor = 1
        attn_factor = 1

        pos_freqs = base ** (torch.arange(0, dim, 2).float().to(gpu_device) / dim)
        inv_freq_extrapolation = 1.0 / pos_freqs
        inv_freq_interpolation = 1.0 / (scaling_factor * pos_freqs)

        low, high = _yarn_find_correction_range(beta_fast, beta_slow, dim, base, original_max_position_embeddings)
        inv_freq_mask = (1 - _yarn_linear_ramp_mask(low, high, dim // 2).float().to(gpu_device)) * extrapolation_factor # Get n-d rotational scaling corrected for extrapolation
        inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask

        # calculate mscale
        mscale = float(_yarn_get_mscale(scaling_factor) * attn_factor)

        # calculate sinusoid_inp
        t = np.arange(num_pos, dtype=np.float64)
        t = torch.tensor(t, device=inv_freq.device, dtype=torch.float64)
        sinusoid_inp = torch.outer(t, inv_freq.to(device=t.device).to(t.dtype))

        return torch.cat((torch.sin(sinusoid_inp)*mscale, torch.cos(sinusoid_inp)*mscale), dim=1)

    return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)


@torch.fx.wrap
def get_embed_positions(embed_positions, position_ids):
    return embed_positions.to(position_ids.device).repeat(position_ids.shape[0], 1, 1)


def rotate_every_two(x: torch.Tensor) -> torch.Tensor:
    x1 = x[:, :, :, ::2]
    x2 = x[:, :, :, 1::2]
    x = torch.stack((-x2, x1), dim=-1)
    return x.flatten(-2)  # in einsum notation: rearrange(x, '... d j -> ... (d j)')


def apply_rotary_pos_emb(tensor: torch.Tensor, sin: torch.Tensor, cos: torch.Tensor) -> torch.Tensor:
    sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3)
    cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3)
    return (tensor * cos) + (rotate_every_two(tensor) * sin)


class GPTJAttention(nn.Module):
    def __init__(self, config):
        super().__init__()

        max_positions = config.max_position_embeddings
        self.register_buffer(
            "bias",
            torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
                1, 1, max_positions, max_positions
            ),
            persistent=False,
        )
        self.register_buffer("masked_bias", torch.tensor(-1e9), persistent=False)

        self.attn_dropout = nn.Dropout(config.attn_pdrop)
        self.resid_dropout = nn.Dropout(config.resid_pdrop)

        self.embed_dim = config.hidden_size
        self.num_attention_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_attention_heads
        if self.head_dim * self.num_attention_heads != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and"
                f" `num_attention_heads`: {self.num_attention_heads})."
            )
        self.scale_attn = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype())

        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
        self.rotary_dim = config.rotary_dim
        pos_embd_dim = self.rotary_dim or self.embed_dim
        self.embed_positions = create_sinusoidal_positions(max_positions, pos_embd_dim, config.rope_scaling["factor"], config.rope_scaling["type"])

    def _split_heads(self, tensor, num_attention_heads, attn_head_size, rotary):
        """
        Splits hidden dim into attn_head_size and num_attention_heads
        """
        new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size)
        tensor = tensor.view(new_shape)
        if rotary:
            return tensor
        if len(tensor.shape) == 5:
            return tensor.permute(0, 1, 3, 2, 4)  # (batch, blocks, head, block_length, head_features)
        elif len(tensor.shape) == 4:
            return tensor.permute(0, 2, 1, 3)  # (batch, head, seq_length, head_features)
        else:
            raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")

    def _merge_heads(self, tensor, num_attention_heads, attn_head_size):
        """
        Merges attn_head_size dim and num_attn_heads dim into hidden dim
        """
        if len(tensor.shape) == 5:
            tensor = tensor.permute(0, 1, 3, 2, 4).contiguous()
        elif len(tensor.shape) == 4:
            tensor = tensor.permute(0, 2, 1, 3).contiguous()
        else:
            raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(tensor.shape)}")
        new_shape = tensor.size()[:-2] + (num_attention_heads * attn_head_size,)
        return tensor.view(new_shape)

    def _attn(
        self,
        query,
        key,
        value,
        attention_mask=None,
        head_mask=None,
    ):
        # compute causal mask from causal mask buffer
        query_length, key_length = query.size(-2), key.size(-2)
        causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]

        # Keep the attention weights computation in fp32 to avoid overflow issues
        query = query.to(torch.float32)
        key = key.to(torch.float32)

        attn_weights = torch.matmul(query, key.transpose(-1, -2))

        mask_value = torch.finfo(attn_weights.dtype).min
        # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
        # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
        mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
        attn_weights = torch.where(causal_mask, attn_weights, mask_value)

        attn_weights = attn_weights / self.scale_attn

        if attention_mask is not None:
            # Apply the attention mask
            attn_weights = attn_weights + attention_mask

        attn_weights = nn.functional.softmax(attn_weights, dim=-1)
        attn_weights = attn_weights.to(value.dtype)
        attn_weights = self.attn_dropout(attn_weights)

        # Mask heads if we want to
        if head_mask is not None:
            attn_weights = attn_weights * head_mask

        attn_output = torch.matmul(attn_weights, value)

        return attn_output, attn_weights

    def _get_embed_positions(self, position_ids):
        embed_positions = self.embed_positions
        if embed_positions.device != position_ids.device:
            embed_positions = embed_positions.to(position_ids.device)
            self.embed_positions = embed_positions
        return embed_positions.repeat(position_ids.shape[0], 1, 1)

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        layer_past: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
    ) -> Union[
        Tuple[torch.Tensor, Tuple[torch.Tensor]],
        Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
    ]:
        query = self.q_proj(hidden_states)
        key = self.k_proj(hidden_states)
        value = self.v_proj(hidden_states)

        query = self._split_heads(query, self.num_attention_heads, self.head_dim, True)
        key = self._split_heads(key, self.num_attention_heads, self.head_dim, True)
        value = self._split_heads(value, self.num_attention_heads, self.head_dim, False)

        if is_torch_fx_proxy(position_ids) or torch.jit.is_tracing():
            # The logic to conditionally copy to GPU could not be traced, so we do this
            # every time in the torch.fx case
            embed_positions = get_embed_positions(self.embed_positions, position_ids)
        else:
            embed_positions = self._get_embed_positions(position_ids)

        repeated_position_ids = position_ids.unsqueeze(-1).repeat(1, 1, embed_positions.shape[-1])
        sincos = torch.gather(embed_positions, 1, repeated_position_ids)
        sin, cos = torch.split(sincos, sincos.shape[-1] // 2, dim=-1)

        if self.rotary_dim is not None:
            k_rot = key[:, :, :, : self.rotary_dim]
            k_pass = key[:, :, :, self.rotary_dim :]

            q_rot = query[:, :, :, : self.rotary_dim]
            q_pass = query[:, :, :, self.rotary_dim :]

            k_rot = apply_rotary_pos_emb(k_rot, sin, cos)
            q_rot = apply_rotary_pos_emb(q_rot, sin, cos)

            key = torch.cat([k_rot, k_pass], dim=-1)
            query = torch.cat([q_rot, q_pass], dim=-1)
        else:
            key = apply_rotary_pos_emb(key, sin, cos)
            query = apply_rotary_pos_emb(query, sin, cos)

        key = key.permute(0, 2, 1, 3)
        query = query.permute(0, 2, 1, 3)

        if layer_past is not None:
            past_key = layer_past[0]
            past_value = layer_past[1]
            key = torch.cat((past_key, key), dim=-2)
            value = torch.cat((past_value, value), dim=-2)

        if use_cache is True:
            present = (key, value)
        else:
            present = None

        # compute self-attention: V x Softmax(QK^T)
        attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)

        attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_dim)
        attn_output = self.out_proj(attn_output)
        attn_output = self.resid_dropout(attn_output)

        outputs = (attn_output, present)
        if output_attentions:
            outputs += (attn_weights,)

        return outputs  # a, present, (attentions)


class GPTJMLP(nn.Module):
    def __init__(self, intermediate_size, config):  # in MLP: intermediate_size= 4 * embed_dim
        super().__init__()
        embed_dim = config.n_embd

        self.fc_in = nn.Linear(embed_dim, intermediate_size)
        self.fc_out = nn.Linear(intermediate_size, embed_dim)

        self.act = ACT2FN[config.activation_function]
        self.dropout = nn.Dropout(config.resid_pdrop)

    def forward(self, hidden_states: Optional[torch.FloatTensor]) -> torch.FloatTensor:
        hidden_states = self.fc_in(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.fc_out(hidden_states)
        hidden_states = self.dropout(hidden_states)
        return hidden_states


class GPTJBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
        self.attn = GPTJAttention(config)
        self.mlp = GPTJMLP(inner_dim, config)

    def forward(
        self,
        hidden_states: Optional[torch.FloatTensor],
        layer_past: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
        residual = hidden_states
        hidden_states = self.ln_1(hidden_states)
        attn_outputs = self.attn(
            hidden_states=hidden_states,
            layer_past=layer_past,
            attention_mask=attention_mask,
            position_ids=position_ids,
            head_mask=head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
        )
        attn_output = attn_outputs[0]  # output_attn: a, present, (attentions)
        outputs = attn_outputs[1:]

        feed_forward_hidden_states = self.mlp(hidden_states)
        hidden_states = attn_output + feed_forward_hidden_states + residual

        if use_cache:
            outputs = (hidden_states,) + outputs
        else:
            outputs = (hidden_states,) + outputs[1:]

        return outputs  # hidden_states, present, (attentions)


class GPTJPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = GPTJConfig
    base_model_prefix = "transformer"
    is_parallelizable = True
    supports_gradient_checkpointing = True
    _no_split_modules = ["GPTJBlock"]
    _skip_keys_device_placement = "past_key_values"

    def __init__(self, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)

    def _init_weights(self, module):
        """Initialize the weights."""
        if isinstance(module, (nn.Linear,)):
            # Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, GPTJModel):
            module.gradient_checkpointing = value


GPTJ_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`GPTJConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

GPTJ_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.

            [What are position IDs?](../glossary#position-ids)
        head_mask (`torch.FloatTensor` of shape `(num_attention_heads,)` or `(n_layer, num_attention_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_dim)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

PARALLELIZE_DOCSTRING = r"""
    This is an experimental feature and is a subject to change at a moment's notice. Uses a device map to distribute
    attention modules of the model across several devices. If no device map is given, it will evenly distribute blocks
    across all devices.

    Args:
        device_map (`Dict[int, list]`, optional, defaults to None):
            A dictionary that maps attention modules to devices. Note that the embedding module and LMHead are always
            automatically mapped to the first device (for esoteric reasons). That means that the first device should
            have fewer attention modules mapped to it than other devices. For reference, the GPT-J models have the
            following number of attention modules:

                - gpt-j-6B: 28

    Example:

    ```python
    # Here is an example of a device map on a machine with 4 GPUs using gpt-j-6B, which has a total of 28 attention modules:
    model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
    device_map = {
        0: [0, 1, 2, 3, 4, 5, 6],
        1: [7, 8, 9, 10, 11, 12, 13],
        2: [14, 15, 16, 17, 18, 19, 20],
        3: [21, 22, 23, 24, 25, 26, 27],
    }
    model.parallelize(device_map)
    ```
"""

DEPARALLELIZE_DOCSTRING = r"""
    Moves the model to CPU from a model parallel state.

    Example:

    ```python
    # On a 4 GPU machine with gpt-j-6B:
    model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
    device_map = {
        0: [0, 1, 2, 3, 4, 5, 6],
        1: [7, 8, 9, 10, 11, 12, 13],
        2: [14, 15, 16, 17, 18, 19, 20],
        3: [21, 22, 23, 24, 25, 26, 27],
    }
    model.parallelize(device_map)  # Splits the model across several devices
    model.deparallelize()  # Put the model back on cpu and cleans memory by calling torch.cuda.empty_cache()
    ```
"""


@add_start_docstrings(
    "The bare GPT-J Model transformer outputting raw hidden-states without any specific head on top.",
    GPTJ_START_DOCSTRING,
)
class GPTJModel(GPTJPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        self.embed_dim = config.n_embd
        self.vocab_size = config.vocab_size
        self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
        self.drop = nn.Dropout(config.embd_pdrop)
        self.h = nn.ModuleList([GPTJBlock(config) for _ in range(config.n_layer)])
        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

        # Model parallel
        self.model_parallel = False
        self.device_map = None
        self.gradient_checkpointing = False

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings(PARALLELIZE_DOCSTRING)
    def parallelize(self, device_map=None):
        warnings.warn(
            "`GPTJModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your"
            " model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
            " `device_map` but it needs to be a dictionary module_name to device, so for instance {'h.0': 0, 'h.1': 1,"
            " ...}",
            FutureWarning,
        )
        # Check validity of device_map
        self.device_map = (
            get_device_map(len(self.h), range(torch.cuda.device_count())) if device_map is None else device_map
        )
        assert_device_map(self.device_map, len(self.h))
        self.model_parallel = True
        self.first_device = "cpu" if "cpu" in self.device_map.keys() else "cuda:" + str(min(self.device_map.keys()))
        self.last_device = "cuda:" + str(max(self.device_map.keys()))
        self.wte = self.wte.to(self.first_device)
        # Load onto devices
        for k, v in self.device_map.items():
            for block in v:
                cuda_device = "cuda:" + str(k)
                self.h[block] = self.h[block].to(cuda_device)
        # ln_f to last
        self.ln_f = self.ln_f.to(self.last_device)

    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
    def deparallelize(self):
        warnings.warn(
            "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
            FutureWarning,
        )
        self.model_parallel = False
        self.device_map = None
        self.first_device = "cpu"
        self.last_device = "cpu"
        self.wte = self.wte.to("cpu")
        for index in range(len(self.h)):
            self.h[index] = self.h[index].to("cpu")
        self.ln_f = self.ln_f.to("cpu")
        torch.cuda.empty_cache()

    def get_input_embeddings(self):
        return self.wte

    def set_input_embeddings(self, new_embeddings):
        self.wte = new_embeddings

    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPast,
        config_class=_CONFIG_FOR_DOC,
        real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
            batch_size = input_ids.shape[0]
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
            batch_size = inputs_embeds.shape[0]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if token_type_ids is not None:
            token_type_ids = token_type_ids.view(-1, input_shape[-1])

        if position_ids is not None:
            position_ids = position_ids.view(-1, input_shape[-1]).long()

        if past_key_values is None:
            past_length = 0
            past_key_values = tuple([None] * len(self.h))
        else:
            past_length = past_key_values[0][0].size(-2)

        if position_ids is None:
            position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
            position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])

        # Attention mask.
        if attention_mask is not None:
            if batch_size <= 0:
                raise ValueError("batch_size has to be defined and > 0")
            attention_mask = attention_mask.view(batch_size, -1)
            # We create a 3D attention mask from a 2D tensor mask.
            # Sizes are [batch_size, 1, 1, to_seq_length]
            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
            # this attention mask is more simple than the triangular masking of causal attention
            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
            attention_mask = attention_mask[:, None, None, :]

            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
            # masked positions, this operation will create a tensor which is 0.0 for
            # positions we want to attend and the dtype's smallest value for masked positions.
            # Since we are adding it to the raw scores before the softmax, this is
            # effectively the same as removing these entirely.
            attention_mask = attention_mask.to(dtype=self.dtype)  # fp16 compatibility
            attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x num_attention_heads x N x N
        # head_mask has shape n_layer x batch x num_attention_heads x N x N
        head_mask = self.get_head_mask(head_mask, self.config.n_layer)

        if inputs_embeds is None:
            inputs_embeds = self.wte(input_ids)

        hidden_states = inputs_embeds

        if token_type_ids is not None:
            token_type_embeds = self.wte(token_type_ids)
            hidden_states = hidden_states + token_type_embeds

        hidden_states = self.drop(hidden_states)

        output_shape = input_shape + (hidden_states.size(-1),)

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        presents = () if use_cache else None
        all_self_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
            # Model parallel
            if self.model_parallel:
                torch.cuda.set_device(hidden_states.device)
                # Ensure layer_past is on same device as hidden_states (might not be correct)
                if layer_past is not None:
                    layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past)
                # Ensure that attention_mask is always on the same device as hidden_states
                if attention_mask is not None:
                    attention_mask = attention_mask.to(hidden_states.device)
                if isinstance(head_mask, torch.Tensor):
                    head_mask = head_mask.to(hidden_states.device)
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, use_cache, output_attentions)

                    return custom_forward

                outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    None,
                    attention_mask,
                    position_ids,
                    head_mask[i],
                )
            else:
                outputs = block(
                    hidden_states=hidden_states,
                    layer_past=layer_past,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    head_mask=head_mask[i],
                    use_cache=use_cache,
                    output_attentions=output_attentions,
                )

            hidden_states = outputs[0]
            if use_cache is True:
                presents = presents + (outputs[1],)

            if output_attentions:
                all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)

            # Model Parallel: If it's the last layer for that device, put things on the next device
            if self.model_parallel:
                for k, v in self.device_map.items():
                    if i == v[-1] and "cuda:" + str(k) != self.last_device:
                        hidden_states = hidden_states.to("cuda:" + str(k + 1))

        hidden_states = self.ln_f(hidden_states)

        hidden_states = hidden_states.view(output_shape)
        # Add last hidden state
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=presents,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


@add_start_docstrings(
    """
    The GPT-J Model transformer with a language modeling head on top.
    """,
    GPTJ_START_DOCSTRING,
)
class GPTJForCausalLM(GPTJPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.transformer = GPTJModel(config)
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size)

        # Model parallel
        self.model_parallel = False
        self.device_map = None

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings(PARALLELIZE_DOCSTRING)
    def parallelize(self, device_map=None):
        warnings.warn(
            "`GPTJForCausalLM.parallelize` is deprecated and will be removed in v5 of Transformers, you should load"
            " your model with `device_map='balanced'` in the call to `from_pretrained`. You can also provide your own"
            " `device_map` but it needs to be a dictionary module_name to device, so for instance {'transformer.h.0':"
            " 0, 'transformer.h.1': 1, ...}",
            FutureWarning,
        )
        self.device_map = (
            get_device_map(len(self.transformer.h), range(torch.cuda.device_count()))
            if device_map is None
            else device_map
        )
        assert_device_map(self.device_map, len(self.transformer.h))
        self.transformer.parallelize(self.device_map)
        self.lm_head = self.lm_head.to(self.transformer.first_device)
        self.model_parallel = True

    @add_start_docstrings(DEPARALLELIZE_DOCSTRING)
    def deparallelize(self):
        warnings.warn(
            "Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
            FutureWarning,
        )
        self.transformer.deparallelize()
        self.transformer = self.transformer.to("cpu")
        self.lm_head = self.lm_head.to("cpu")
        self.model_parallel = False
        torch.cuda.empty_cache()

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
        token_type_ids = kwargs.get("token_type_ids", None)
        # only last token for inputs_ids if past is defined in kwargs
        if past_key_values:
            input_ids = input_ids[:, -1].unsqueeze(-1)
            if token_type_ids is not None:
                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)

        attention_mask = kwargs.get("attention_mask", None)
        position_ids = kwargs.get("position_ids", None)

        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -1].unsqueeze(-1)

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "position_ids": position_ids,
                "attention_mask": attention_mask,
                "token_type_ids": token_type_ids,
            }
        )

        return model_inputs

    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=CausalLMOutputWithPast,
        config_class=_CONFIG_FOR_DOC,
        real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]

        # Set device for model parallelism
        # if self.model_parallel:
        #     torch.cuda.set_device(self.transformer.first_device)
        #     hidden_states = hidden_states.to(self.lm_head.weight.device)

        # make sure sampling in fp16 works correctly and
        # compute loss in fp32 to match with mesh-tf version
        # https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
        lm_logits = self.lm_head(hidden_states).to(torch.float32)

        loss = None
        if labels is not None:
            # move labels to correct device to enable model parallelism
            labels = labels.to(lm_logits.device)
            # Shift so that tokens < n predict n
            shift_logits = lm_logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

            loss = loss.to(hidden_states.dtype)

        if not return_dict:
            output = (lm_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=lm_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )

    @staticmethod
    def _reorder_cache(
        past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
    ) -> Tuple[Tuple[torch.Tensor]]:
        """
        This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or
        [`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
        beam_idx at every generation step.
        """
        return tuple(
            tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
            for layer_past in past_key_values
        )


@add_start_docstrings(
    """
    The GPT-J Model transformer with a sequence classification head on top (linear layer).

    [`GPTJForSequenceClassification`] uses the last token in order to do the classification, as other causal models
    (e.g. GPT, GPT-2, GPT-Neo) do.

    Since it does classification on the last token, it requires to know the position of the last token. If a
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
    each row of the batch).
    """,
    GPTJ_START_DOCSTRING,
)
class GPTJForSequenceClassification(GPTJPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.transformer = GPTJModel(config)
        self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)

        # Model parallel
        self.model_parallel = False
        self.device_map = None

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint="ydshieh/tiny-random-gptj-for-sequence-classification",
        output_type=SequenceClassifierOutputWithPast,
        config_class=_CONFIG_FOR_DOC,
        real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.transformer(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)

        if input_ids is not None:
            batch_size = input_ids.shape[0]
        else:
            batch_size = inputs_embeds.shape[0]

        if self.config.pad_token_id is None and batch_size != 1:
            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
        if self.config.pad_token_id is None:
            sequence_lengths = -1
        else:
            if input_ids is not None:
                sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
            else:
                sequence_lengths = -1
                logger.warning(
                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
                    "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
                )

        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]

        loss = None
        if labels is not None:
            labels = labels.to(pooled_logits.device)
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(pooled_logits, labels)
        if not return_dict:
            output = (pooled_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )


@add_start_docstrings(
    """
    The GPT-J Model transformer with a span classification head on top for extractive question-answering tasks like
    SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """,
    GPTJ_START_DOCSTRING,
)
class GPTJForQuestionAnswering(GPTJPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.transformer = GPTJModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

        # Model parallel
        self.model_parallel = False
        self.device_map = None

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=QuestionAnsweringModelOutput,
        config_class=_CONFIG_FOR_DOC,
        real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
    )
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        start_positions: Optional[torch.LongTensor] = None,
        end_positions: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, QuestionAnsweringModelOutput]:
        r"""
        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()

        total_loss = None
        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, split add a dimension
            if len(start_positions.size()) > 1:
                start_positions = start_positions.squeeze(-1).to(start_logits.device)
            if len(end_positions.size()) > 1:
                end_positions = end_positions.squeeze(-1).to(end_logits.device)
            # sometimes the start/end positions are outside our model inputs, we ignore these terms
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)

            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output

        return QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

src/my_modeling_gptj.py
dwzhu-pku/PoSE
# coding=utf-8
# Modification Copyright 2023 Dawei Zhu
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
""" PyTorch LLaMA model."""
import math
from typing import List, Optional, Tuple, Union
from pathlib import Path

import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings

from transformers.models.llama.configuration_llama import LlamaConfig



logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "LlamaConfig"


# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
    """
    Make causal mask used for bi-directional self-attention.
    """
    bsz, tgt_len = input_ids_shape
    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
    mask_cond = torch.arange(mask.size(-1), device=device)
    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
    mask = mask.to(dtype)

    if past_key_values_length > 0:
        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)


# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
    """
    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
    """
    bsz, src_len = mask.size()
    tgt_len = tgt_len if tgt_len is not None else src_len

    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

    inverted_mask = 1.0 - expanded_mask

    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)

class LlamaRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        LlamaRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)


class LlamaRotaryEmbedding(torch.nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
        super().__init__()

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

        # Build here to make `torch.jit.trace` work.
        self._set_cos_sin_cache(
            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
        )

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        # t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
        t = np.arange(self.max_seq_len_cached, dtype=np.float64)
        t = torch.tensor(t, device=self.inv_freq.device, dtype=torch.float64)

        # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        freqs = torch.outer(t, self.inv_freq.to(device=t.device).to(t.dtype))
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)

    def forward(self, x, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        if seq_len > self.max_seq_len_cached:
            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

        return (
            self.cos_cached[:, :, :, ...].to(dtype=x.dtype),
            self.sin_cached[:, :, :, ...].to(dtype=x.dtype),
        )


class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
    """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
        self.scaling_factor = scaling_factor
        super().__init__(dim, max_position_embeddings, base, device)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len

        t = np.arange(self.max_seq_len_cached, dtype=np.float64)
        t = t / self.scaling_factor
        t = torch.tensor(t, device=self.inv_freq.device, dtype=torch.float64)

        freqs = torch.outer(t, self.inv_freq.to(device=t.device).to(t.dtype))
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)

class LlamaVanillaNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):

    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
        self.scaling_factor = scaling_factor
        super().__init__(dim, max_position_embeddings, base, device)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len

        base = self.base * self.scaling_factor ** (self.dim / (self.dim - 2))
        inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

        t = np.arange(self.max_seq_len_cached, dtype=np.float64)
        t = torch.tensor(t, device=self.inv_freq.device, dtype=torch.float64)

        # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        freqs = torch.outer(t, self.inv_freq.to(device=t.device).to(t.dtype))
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)

class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
    """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""

    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
        self.scaling_factor = scaling_factor
        super().__init__(dim, max_position_embeddings, base, device)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len

        if seq_len > self.max_position_embeddings:
            base = self.base * (
                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
            ) ** (self.dim / (self.dim - 2))
            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
            self.register_buffer("inv_freq", inv_freq, persistent=False)

        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)

        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)


# Inverse dim formula to find dim based on number of rotations
def _yarn_find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048):
    return (dim * math.log(max_position_embeddings/(num_rotations * 2 * math.pi)))/(2 * math.log(base))

# Find dim range bounds based on rotations
def _yarn_find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048):
    low = math.floor(_yarn_find_correction_dim(
        low_rot, dim, base, max_position_embeddings))
    high = math.ceil(_yarn_find_correction_dim(
        high_rot, dim, base, max_position_embeddings))
    return max(low, 0), min(high, dim-1)  # Clamp values just in case

def _yarn_linear_ramp_mask(min, max, dim):
    if min == max:
        max += 0.001  # Prevent singularity

    linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
    ramp_func = torch.clamp(linear_func, 0, 1)
    return ramp_func

def _yarn_get_mscale(scale=1):
    if scale <= 1:
        return 1.0
    return 0.1 * math.log(scale) + 1.0


class LlamaYaRNScaledRotaryEmbedding(torch.nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, scale=1, original_max_position_embeddings=2048, extrapolation_factor=1, attn_factor=1, beta_fast=32, beta_slow=1, finetuned=False, device=None):
        super().__init__()

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        self.scale = scale
        self.original_max_position_embeddings = original_max_position_embeddings
        self.extrapolation_factor = extrapolation_factor
        self.attn_factor = attn_factor
        self.beta_fast = beta_fast
        self.beta_slow = beta_slow

        # self.yarn(device)
        self.revised_yarn(device)

        # Build here to make `torch.jit.trace` work.
        self.max_seq_len_cached = max_position_embeddings

        t = np.arange(self.max_seq_len_cached, dtype=np.float64)
        t = torch.tensor(t, device=self.inv_freq.device, dtype=torch.float64)
        freqs = torch.outer(t, self.inv_freq.to(device=t.device).to(t.dtype))
        # t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
        # freqs = torch.einsum("i,j->ij", t, self.inv_freq)

        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        dtype = torch.get_default_dtype()

        self.register_buffer("cos_cached", (emb.cos() * self.mscale)[None, None, :, :].to(dtype), persistent=False)
        self.register_buffer("sin_cached", (emb.sin() * self.mscale)[None, None, :, :].to(dtype), persistent=False)

    def forward(self, x, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
        if seq_len > self.max_seq_len_cached:
            print("*****notice******")
            self.max_seq_len_cached = seq_len

            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
            # Different from paper, but it uses a different permutation in order to obtain the same calculation
            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)

            self.register_buffer("cos_cached", (emb.cos() * self.mscale)[None, None, :, :].to(x.dtype), persistent=False)
            self.register_buffer("sin_cached", (emb.sin() * self.mscale)[None, None, :, :].to(x.dtype), persistent=False)
        return (
            self.cos_cached[:, :, :, ...].to(dtype=x.dtype),
            self.sin_cached[:, :, :, ...].to(dtype=x.dtype),
        )

    def yarn(self, device):
        pos_freqs = self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
        inv_freq_extrapolation = 1.0 / pos_freqs
        inv_freq_interpolation = 1.0 / (self.scale * pos_freqs)

        low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base, self.original_max_position_embeddings)
        inv_freq_mask = (1 - _yarn_linear_ramp_mask(low, high, self.dim // 2).float().to(device)) * self.extrapolation_factor # Get n-d rotational scaling corrected for extrapolation
        inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask

        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.mscale = float(_yarn_get_mscale(self.scale) * self.attn_factor) # Get n-d magnitude scaling corrected for interpolation

    def revised_yarn(self, device):
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))

        low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base, self.original_max_position_embeddings)
        inv_freq_mask = (1 - _yarn_linear_ramp_mask(low, high, self.dim // 2).float().to(device)) * self.extrapolation_factor

        inv_freq = inv_freq / ((1-inv_freq_mask)*self.scale + inv_freq_mask)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.mscale = float(_yarn_get_mscale(self.scale) * self.attn_factor)

        


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


class LlamaMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        if self.config.pretraining_tp > 1:
            slice = self.intermediate_size // self.config.pretraining_tp
            gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
            up_proj_slices = self.up_proj.weight.split(slice, dim=0)
            down_proj_slices = self.down_proj.weight.split(slice, dim=1)

            gate_proj = torch.cat(
                [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
            )
            up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)

            intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
            down_proj = [
                F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
            ]
            down_proj = sum(down_proj)
        else:
            down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))

        return down_proj


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


class LlamaAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: LlamaConfig):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        self.max_position_embeddings = config.max_position_embeddings

        if (self.head_dim * self.num_heads) != self.hidden_size:
            raise ValueError(
                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                f" and `num_heads`: {self.num_heads})."
            )
        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
        self._init_rope()

    def _init_rope(self):
        if self.config.rope_scaling is None:
            self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
        else:
            scaling_type = self.config.rope_scaling["type"]
            scaling_factor = self.config.rope_scaling["factor"]
            if scaling_type == "linear":
                self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
                )
            elif scaling_type == "dynamic":
                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
                )
            elif scaling_type == "vanilla_ntk":
                self.rotary_emb = LlamaVanillaNTKScalingRotaryEmbedding(
                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
                )
            elif scaling_type == "yarn":
                original_max_position_embeddings = self.config.rope_scaling["original_max_position_embeddings"]
                self.rotary_emb = LlamaYaRNScaledRotaryEmbedding(
                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scale=scaling_factor, original_max_position_embeddings=original_max_position_embeddings
                )
            else:
                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        bsz, q_len, _ = hidden_states.size()

        if self.config.pretraining_tp > 1:
            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
            query_slices = self.q_proj.weight.split(
                (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
            )
            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)

            query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
            query_states = torch.cat(query_states, dim=-1)

            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
            key_states = torch.cat(key_states, dim=-1)

            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
            value_states = torch.cat(value_states, dim=-1)

        else:
            query_states = self.q_proj(hidden_states)
            key_states = self.k_proj(hidden_states)
            value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-2]
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
        have_past_key_value = past_key_value is not None
        if past_key_value is not None:
            # reuse k, v, self_attention
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)

        past_key_value = (key_states, value_states) if use_cache else None

        # repeat k/v heads if n_kv_heads < n_heads
        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        
        use_xformer = True

        if not use_xformer or have_past_key_value:
            attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

            if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
                    f" {attn_weights.size()}"
                )

            if attention_mask is not None:
                if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                    raise ValueError(
                        f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                    )
                attn_weights = attn_weights + attention_mask

            # upcast attention to fp32
            attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
            attn_output = torch.matmul(attn_weights, value_states)

            if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
                raise ValueError(
                    f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                    f" {attn_output.size()}"
                )

            attn_output = attn_output.transpose(1, 2).contiguous()
            attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
        else:
            import xformers.ops as xops
            attn_weights = None
            #attn_bias = attention_mask.expand(-1, self.num_heads, -1, -1)
            attn_bias=xops.LowerTriangularMask()
            attn_output = xops.memory_efficient_attention(
                query_states.transpose(1,2), key_states.transpose(1,2), value_states.transpose(1,2),
                attn_bias=attn_bias,
                ).reshape(bsz, q_len, self.hidden_size)


        if self.config.pretraining_tp > 1:
            attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
            o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
            attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
        else:
            attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


class LlamaDecoderLayer(nn.Module):
    def __init__(self, config: LlamaConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = LlamaAttention(config=config)
        self.mlp = LlamaMLP(config)
        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
        """

        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


LLAMA_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`LlamaConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
    LLAMA_START_DOCSTRING,
)
class LlamaPreTrainedModel(PreTrainedModel):
    config_class = LlamaConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["LlamaDecoderLayer"]
    _skip_keys_device_placement = "past_key_values"

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, LlamaModel):
            module.gradient_checkpointing = value


LLAMA_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.

            [What are position IDs?](../glossary#position-ids)
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
    LLAMA_START_DOCSTRING,
)
class LlamaModel(LlamaPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]

    Args:
        config: LlamaConfig
    """

    def __init__(self, config: LlamaConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        self.gradient_checkpointing = False
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
        # create causal mask
        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        combined_attention_mask = None
        if input_shape[-1] > 1:
            combined_attention_mask = _make_causal_mask(
                input_shape,
                inputs_embeds.dtype,
                device=inputs_embeds.device,
                past_key_values_length=past_key_values_length,
            )

        if attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
                inputs_embeds.device
            )
            combined_attention_mask = (
                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
            )

        return combined_attention_mask

    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

        seq_length_with_past = seq_length
        past_key_values_length = 0

        if past_key_values is not None:
            past_key_values_length = past_key_values[0][0].shape[2]
            seq_length_with_past = seq_length_with_past + past_key_values_length

        if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(
                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
            )
            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
        else:
            position_ids = position_ids.view(-1, seq_length).long()

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)
        # embed positions
        # if attention_mask is None:
        #     attention_mask = torch.ones(
        #         (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
        #     )

        if attention_mask is not None:
            attention_mask = self._prepare_decoder_attention_mask(
            attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
        )

        hidden_states = inputs_embeds

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = () if use_cache else None

        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            past_key_value = past_key_values[idx] if past_key_values is not None else None

            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, output_attentions, None)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    position_ids,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_value,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


class LlamaForCausalLM(LlamaPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = LlamaModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""
        Args:
            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, LlamaForCausalLM

        >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

        >>> prompt = "Hey, are you conscious? Can you talk to me?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
        ```"""

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        if self.config.pretraining_tp > 1:
            lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
            logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
            logits = torch.cat(logits, dim=-1)
        else:
            logits = self.lm_head(hidden_states)
        logits = logits.float()

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        if past_key_values:
            input_ids = input_ids[:, -1:]

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -1].unsqueeze(-1)

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )
        return model_inputs

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
            )
        return reordered_past


@add_start_docstrings(
    """
    The LLaMa Model transformer with a sequence classification head on top (linear layer).

    [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
    (e.g. GPT-2) do.

    Since it does classification on the last token, it requires to know the position of the last token. If a
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
    each row of the batch).
    """,
    LLAMA_START_DOCSTRING,
)
class LlamaForSequenceClassification(LlamaPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = LlamaModel(config)
        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)

        if input_ids is not None:
            batch_size = input_ids.shape[0]
        else:
            batch_size = inputs_embeds.shape[0]

        if self.config.pad_token_id is None and batch_size != 1:
            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
        if self.config.pad_token_id is None:
            sequence_lengths = -1
        else:
            if input_ids is not None:
                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to(
                    logits.device
                )
            else:
                sequence_lengths = -1

        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]

        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(pooled_logits, labels)
        if not return_dict:
            output = (pooled_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )
src/my_modeling_llama.py
dwzhu-pku/PoSE
# Copyright 2023 Baichuan Inc. All Rights Reserved.

# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
from transformers.utils import logging


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {},
    "tokenizer_file": {},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}


class BaichuanTokenizer(PreTrainedTokenizer):
    """
    Construct a Baichuan tokenizer. Based on byte-level Byte-Pair-Encoding.

    Args:
        vocab_file (`str`):
            Path to the vocabulary file.
    """

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file,
        unk_token="<unk>",
        bos_token="<s>",
        eos_token="</s>",
        pad_token=None,
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        add_bos_token=True,
        add_eos_token=False,
        clean_up_tokenization_spaces=False,
        **kwargs,
    ):
        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            pad_token=pad_token,
            add_bos_token=add_bos_token,
            add_eos_token=add_eos_token,
            sp_model_kwargs=self.sp_model_kwargs,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            **kwargs,
        )
        self.vocab_file = vocab_file
        self.add_bos_token = add_bos_token
        self.add_eos_token = add_eos_token
        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(vocab_file)

    def __getstate__(self):
        state = self.__dict__.copy()
        state["sp_model"] = None
        return state

    def __setstate__(self, d):
        self.__dict__ = d
        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(self.vocab_file)

    @property
    def vocab_size(self):
        """Returns vocab size"""
        return self.sp_model.get_piece_size()

    def get_vocab(self):
        """Returns vocab as a dict"""
        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
        vocab.update(self.added_tokens_encoder)
        return vocab

    def _tokenize(self, text):
        """Returns a tokenized string."""
        return self.sp_model.encode(text, out_type=str)

    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        return self.sp_model.piece_to_id(token)

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        token = self.sp_model.IdToPiece(index)
        return token

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        current_sub_tokens = []
        out_string = ""
        prev_is_special = False
        for i, token in enumerate(tokens):
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                if not prev_is_special and i != 0:
                    out_string += " "
                out_string += self.sp_model.decode(current_sub_tokens) + token
                prev_is_special = True
                current_sub_tokens = []
            else:
                current_sub_tokens.append(token)
                prev_is_special = False
        out_string += self.sp_model.decode(current_sub_tokens)
        return out_string

    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
        """
        Save the vocabulary and special tokens file to a directory.

        Args:
            save_directory (`str`):
                The directory in which to save the vocabulary.

        Returns:
            `Tuple(str)`: Paths to the files saved.
        """
        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
            return
        out_vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
            copyfile(self.vocab_file, out_vocab_file)
        elif not os.path.isfile(self.vocab_file):
            with open(out_vocab_file, "wb") as fi:
                content_spiece_model = self.sp_model.serialized_model_proto()
                fi.write(content_spiece_model)

        return (out_vocab_file,)

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
        eos_token_id = [self.eos_token_id] if self.add_eos_token else []

        output = bos_token_id + token_ids_0 + eos_token_id

        if token_ids_1 is not None:
            output = output + bos_token_id + token_ids_1 + eos_token_id

        return output

    def get_special_tokens_mask(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
    ) -> List[int]:
        """
        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
        special tokens using the tokenizer `prepare_for_model` method.

        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.
            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not the token list is already formatted with special tokens for the model.

        Returns:
            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
            )

        bos_token_id = [1] if self.add_bos_token else []
        eos_token_id = [1] if self.add_eos_token else []

        if token_ids_1 is None:
            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
        return (
            bos_token_id
            + ([0] * len(token_ids_0))
            + eos_token_id
            + bos_token_id
            + ([0] * len(token_ids_1))
            + eos_token_id
        )

    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
        sequence pair mask has the following format:

        ```
        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
        | first sequence    | second sequence |
        ```

        if token_ids_1 is None, only returns the first portion of the mask (0s).

        Args:
            token_ids_0 (`List[int]`):
                List of ids.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.

        Returns:
            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
        """
        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
        eos_token_id = [self.eos_token_id] if self.add_eos_token else []

        output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)

        if token_ids_1 is not None:
            output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)

        return output

src/tokenization_baichuan.py
dwzhu-pku/PoSE
#    Modification Copyright 2023 Dawei Zhu
#    Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
#
#    Licensed under the Apache License, Version 2.0 (the "License");
#    you may not use this file except in compliance with the License.
#    You may obtain a copy of the License at
#
#        http://www.apache.org/licenses/LICENSE-2.0
#
#    Unless required by applicable law or agreed to in writing, software
#    distributed under the License is distributed on an "AS IS" BASIS,
#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#    See the License for the specific language governing permissions and
#    limitations under the License.

import copy
import logging
import random
from dataclasses import dataclass, field
from typing import Optional, Dict, Sequence

import torch
import torch.distributed
import transformers
import deepspeed
from torch.utils.data import Dataset
from transformers import Trainer, GPT2ForQuestionAnswering, AutoConfig, AutoTokenizer
from datasets import load_dataset

from my_modeling_baichuan import BaichuanForCausalLM
from my_configuration_baichuan import BaichuanConfig
from tokenization_baichuan import BaichuanTokenizer

#import utils

IGNORE_INDEX = -100
DEFAULT_PAD_TOKEN = "[PAD]"
DEFAULT_EOS_TOKEN = "</s>"
DEFAULT_BOS_TOKEN = "<s>"
DEFAULT_UNK_TOKEN = "</s>"
PROMPT_DICT = {
    "prompt_input": (
        "Below is an instruction that describes a task, paired with an input that provides further context. "
        "Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
    ),
    "prompt_no_input": (
        "Below is an instruction that describes a task. "
        "Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Response:"
    ),
}


@dataclass
class ModelArguments:
    model_name_or_path: Optional[str] = field(default="facebook/opt-125m")


@dataclass
class DataArguments:
    train_data_path: str = field(default=None, metadata={"help": "Path to the training data."})
    valid_data_path: str = field(default=None, metadata={"help": "Path to the training data."})
    test_data_path: str = field(default=None, metadata={"help": "Path to the test data."})


@dataclass
class TrainingArguments(transformers.TrainingArguments):
    cache_dir: Optional[str] = field(default=None)
    optim: str = field(default="adamw_torch")
    model_max_position_embeddings: int = field(
        default=4096,
        metadata={"help": "Maximum position embeddings."},
    )
    inference_length: int = field(
        default=2048,
        metadata={"help": "Maximum position embeddings."},
    )
    rope_scaling_type: Optional[str] = field(default=None)
    rope_scaling_factor: float = field(default=1.0)


def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str):
    """Collects the state dict and dump to disk."""
    state_dict = trainer.model.state_dict()
    if trainer.args.should_save:
        cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()}
        del state_dict
        trainer._save(output_dir, state_dict=cpu_state_dict)  # noqa


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

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

    if num_new_tokens > 0:
        input_embeddings = model.get_input_embeddings().weight.data
        output_embeddings = model.get_output_embeddings().weight.data

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

        input_embeddings[-num_new_tokens:] = input_embeddings_avg
        output_embeddings[-num_new_tokens:] = output_embeddings_avg




@dataclass
class DataCollatorForSupervisedDataset(object):
    """Collate examples for supervised fine-tuning."""

    tokenizer: transformers.PreTrainedTokenizer

    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
        input_ids, labels, position_ids = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels", "position_ids"))
        input_ids = [torch.tensor(x) for x in input_ids]
        input_ids = torch.nn.utils.rnn.pad_sequence(
            input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
        )
        labels = [torch.tensor(x) for x in labels]
        labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
        position_ids = [torch.tensor(x) for x in position_ids]
        position_ids = torch.nn.utils.rnn.pad_sequence(position_ids, batch_first=True, padding_value=0)
        return dict(
            input_ids=input_ids,
            labels=labels,
            position_ids=position_ids,
            attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
        )


def train_preprocess_function_pose(examples, tokenizer, scaled_max_position_embeddings, model_max_position_embeddings):

    inputs = examples["text"]
    raw_model_inputs = tokenizer(inputs, padding=False, truncation=True, max_length=model_max_position_embeddings*5)

    input_ids = []
    position_ids = []

    for ids in raw_model_inputs["input_ids"]:

        len_chunk = min(len(ids), model_max_position_embeddings)
        len_input = len(ids)
        lt1 = 0
        rt1 = random.randint(1, (len_chunk+1)//2)
        rt2 = random.randint(lt1+len_chunk, len_input)
        lt2 = rt2 - (len_chunk - (rt1-lt1))
        chunked_ids = ids[lt1:rt1] + ids[lt2:rt2]
        input_ids.append(chunked_ids)

        pos_ids = torch.arange(len(chunked_ids), dtype=torch.long)
        len_pos_ids = len(pos_ids)
        # lt = random.randint(0, scaled_max_position_embeddings-len_pos_ids)
        lt = 0
        rt = random.randint(lt, scaled_max_position_embeddings-len_pos_ids)

        pos_ids[:rt1-lt1] += lt
        pos_ids[rt1-lt1:] += rt
        position_ids.append(pos_ids)
    
    model_inputs = {"input_ids": input_ids, "position_ids": position_ids, "labels": input_ids}

    return model_inputs

def test_preprocess_function(examples, tokenizer, inference_length):

    inputs = examples["text"]
    model_inputs = tokenizer(inputs, padding=False, truncation=True, max_length=inference_length)
    position_ids = [torch.arange(len(ids), dtype=torch.long) for ids in model_inputs["input_ids"]]
    model_inputs["position_ids"] = position_ids
    model_inputs["labels"] = model_inputs["input_ids"]

    return model_inputs

              
def train():
    parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    config = BaichuanConfig.from_pretrained(model_args.model_name_or_path, cache_dir=training_args.cache_dir)
    scaled_max_position_embeddings=int(training_args.model_max_position_embeddings * training_args.rope_scaling_factor)
    config.max_position_embeddings=scaled_max_position_embeddings

    if training_args.rope_scaling_type is not None:
        config.rope_scaling={"type": training_args.rope_scaling_type, "factor": training_args.rope_scaling_factor}
        if training_args.rope_scaling_type == "yarn":
            config.rope_scaling["original_max_position_embeddings"] = training_args.model_max_position_embeddings
        
    model = BaichuanForCausalLM.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=training_args.cache_dir,
        config=config,
    )

    tokenizer = BaichuanTokenizer.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=training_args.cache_dir,
        padding_side="right",
        use_fast=False,
    )
    if tokenizer.pad_token is None:
        smart_tokenizer_and_embedding_resize(
            special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN),
            tokenizer=tokenizer,
            model=model,
        )

    raw_train_datasets = load_dataset('json', data_files=data_args.train_data_path, split="train", cache_dir=training_args.cache_dir)
    raw_valid_datasets = load_dataset('json', data_files=data_args.valid_data_path, split="train", cache_dir=training_args.cache_dir)
    raw_test_datasets = load_dataset('json', data_files=data_args.test_data_path, split="train", cache_dir=training_args.cache_dir)
    if training_args.local_rank > 0: 
        torch.distributed.barrier()

    train_dataset = raw_train_datasets.map(
        train_preprocess_function_pose,
        batched=True,
        batch_size=3000, 
        num_proc=32, 
        remove_columns=raw_train_datasets.column_names,
        load_from_cache_file=True, # not args.overwrite_cache
        desc="Running tokenizer on train dataset",
        fn_kwargs={"tokenizer": tokenizer, "scaled_max_position_embeddings": scaled_max_position_embeddings, "model_max_position_embeddings": training_args.model_max_position_embeddings}
    )


    valid_dataset = raw_valid_datasets.map(
        test_preprocess_function,
        batched=True,
        batch_size=3000,
        num_proc=1,
        remove_columns=raw_valid_datasets.column_names,
        load_from_cache_file=True, # not args.overwrite_cache
        desc="Running tokenizer on valid dataset",
        fn_kwargs={"tokenizer": tokenizer, "inference_length": training_args.inference_length}
    )

    test_dataset = raw_test_datasets.map(
        test_preprocess_function,
        batched=True,
        batch_size=3000,
        num_proc=1,
        remove_columns=raw_test_datasets.column_names,
        load_from_cache_file=True, # not args.overwrite_cache
        desc="Running tokenizer on test dataset",
        fn_kwargs={"tokenizer": tokenizer, "inference_length": training_args.inference_length}
    )

    if training_args.local_rank == 0:
        torch.distributed.barrier()
    
    if training_args.local_rank == 0:
        print(len(train_dataset))
        for index in random.sample(range(len(train_dataset)), 3):
            print(f"Sample {index} of the training set: {train_dataset[index]}.")
    
    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
    # data_module = dict(eval_dataset=valid_dataset, data_collator=data_collator)
    data_module = dict(train_dataset=train_dataset, eval_dataset=valid_dataset, data_collator=data_collator)

    #Tell Trainer not to attempt DataParallel
    model.is_parallelizable = True
    model.model_parallel = True

    trainer = Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)
    model.config.use_cache = False

    if training_args.do_train:
        logging.info("*** Start Training ***")
        trainer.train()
        trainer.save_state()
        safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir)

    if training_args.do_eval:
        logging.info("*** Evaluate on valid set***")
        metrics = trainer.evaluate(eval_dataset=valid_dataset)
        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    if training_args.do_predict:
        logging.info("*** Evaluate on test set***")
        metrics = trainer.evaluate(eval_dataset=test_dataset)
        trainer.log_metrics("predict", metrics)
        trainer.save_metrics("predict", metrics)




if __name__ == "__main__":
    train()

src/train_baichuan.py
dwzhu-pku/PoSE
#    Modification Copyright 2023 Dawei Zhu
#    Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
#
#    Licensed under the Apache License, Version 2.0 (the "License");
#    you may not use this file except in compliance with the License.
#    You may obtain a copy of the License at
#
#        http://www.apache.org/licenses/LICENSE-2.0
#
#    Unless required by applicable law or agreed to in writing, software
#    distributed under the License is distributed on an "AS IS" BASIS,
#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#    See the License for the specific language governing permissions and
#    limitations under the License.

import copy
import logging
import random
from dataclasses import dataclass, field
from typing import Optional, Dict, Sequence

import torch
import torch.distributed
import transformers
import deepspeed
from torch.utils.data import Dataset
from transformers import Trainer, GPT2ForQuestionAnswering, AutoConfig, AutoTokenizer
from datasets import load_dataset

from my_modeling_gptj import GPTJForCausalLM

#import utils

IGNORE_INDEX = -100
DEFAULT_PAD_TOKEN = "[PAD]"
DEFAULT_EOS_TOKEN = "</s>"
DEFAULT_BOS_TOKEN = "<s>"
DEFAULT_UNK_TOKEN = "</s>"
PROMPT_DICT = {
    "prompt_input": (
        "Below is an instruction that describes a task, paired with an input that provides further context. "
        "Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
    ),
    "prompt_no_input": (
        "Below is an instruction that describes a task. "
        "Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Response:"
    ),
}


@dataclass
class ModelArguments:
    model_name_or_path: Optional[str] = field(default="facebook/opt-125m")


@dataclass
class DataArguments:
    train_data_path: str = field(default=None, metadata={"help": "Path to the training data."})
    valid_data_path: str = field(default=None, metadata={"help": "Path to the training data."})
    test_data_path: str = field(default=None, metadata={"help": "Path to the test data."})


@dataclass
class TrainingArguments(transformers.TrainingArguments):
    cache_dir: Optional[str] = field(default=None)
    optim: str = field(default="adamw_torch")
    model_max_position_embeddings: int = field(
        default=2048,
        metadata={"help": "Maximum position embeddings."},
    )
    inference_length: int = field(
        default=2048,
        metadata={"help": "Maximum position embeddings."},
    )
    rope_scaling_type: Optional[str] = field(default=None)
    rope_scaling_factor: float = field(default=1.0)


def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str):
    """Collects the state dict and dump to disk."""
    state_dict = trainer.model.state_dict()
    if trainer.args.should_save:
        cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()}
        del state_dict
        trainer._save(output_dir, state_dict=cpu_state_dict)  # noqa


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

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

    if num_new_tokens > 0:
        input_embeddings = model.get_input_embeddings().weight.data
        output_embeddings = model.get_output_embeddings().weight.data

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

        input_embeddings[-num_new_tokens:] = input_embeddings_avg
        output_embeddings[-num_new_tokens:] = output_embeddings_avg




@dataclass
class DataCollatorForSupervisedDataset(object):
    """Collate examples for supervised fine-tuning."""

    tokenizer: transformers.PreTrainedTokenizer

    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
        input_ids, labels, position_ids = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels", "position_ids"))
        input_ids = [torch.tensor(x) for x in input_ids]
        input_ids = torch.nn.utils.rnn.pad_sequence(
            input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
        )
        labels = [torch.tensor(x) for x in labels]
        labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
        position_ids = [torch.tensor(x) for x in position_ids]
        position_ids = torch.nn.utils.rnn.pad_sequence(position_ids, batch_first=True, padding_value=0)
        return dict(
            input_ids=input_ids,
            labels=labels,
            position_ids=position_ids,
            attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
        )


def train_preprocess_function_pose(examples, tokenizer, scaled_max_position_embeddings, model_max_position_embeddings):

    inputs = examples["text"]
    raw_model_inputs = tokenizer(inputs, padding=False, truncation=True, max_length=model_max_position_embeddings*5)

    input_ids = []
    position_ids = []

    for ids in raw_model_inputs["input_ids"]:

        len_chunk = min(len(ids), model_max_position_embeddings)
        len_input = len(ids)
        lt1 = 0
        rt1 = random.randint(1, (len_chunk+1)//2)
        rt2 = random.randint(lt1+len_chunk, len_input)
        lt2 = rt2 - (len_chunk - (rt1-lt1))
        chunked_ids = ids[lt1:rt1] + ids[lt2:rt2]
        input_ids.append(chunked_ids)

        pos_ids = torch.arange(len(chunked_ids), dtype=torch.long)
        len_pos_ids = len(pos_ids)
        # lt = random.randint(0, scaled_max_position_embeddings-len_pos_ids)
        lt = 0
        rt = random.randint(lt, scaled_max_position_embeddings-len_pos_ids)

        pos_ids[:rt1-lt1] += lt
        pos_ids[rt1-lt1:] += rt
        position_ids.append(pos_ids)
    
    model_inputs = {"input_ids": input_ids, "position_ids": position_ids, "labels": input_ids}

    return model_inputs

def test_preprocess_function(examples, tokenizer, inference_length):

    inputs = examples["text"]
    model_inputs = tokenizer(inputs, padding=False, truncation=True, max_length=inference_length)
    position_ids = [torch.arange(len(ids), dtype=torch.long) for ids in model_inputs["input_ids"]]
    model_inputs["position_ids"] = position_ids
    model_inputs["labels"] = model_inputs["input_ids"]

    return model_inputs

              
def train():
    parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=training_args.cache_dir)
    scaled_max_position_embeddings=int(training_args.model_max_position_embeddings * training_args.rope_scaling_factor)
    config.max_position_embeddings=scaled_max_position_embeddings

    if training_args.rope_scaling_type is not None:
        config.rope_scaling={"type": training_args.rope_scaling_type, "factor": training_args.rope_scaling_factor}
        
    model = GPTJForCausalLM.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=training_args.cache_dir,
        config=config,
    )

    tokenizer = AutoTokenizer.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=training_args.cache_dir,
        padding_side="right",
        use_fast=True,
    )
    if tokenizer.pad_token is None:
        smart_tokenizer_and_embedding_resize(
            special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN),
            tokenizer=tokenizer,
            model=model,
        )

    raw_train_datasets = load_dataset('json', data_files=data_args.train_data_path, split="train", cache_dir=training_args.cache_dir)
    raw_valid_datasets = load_dataset('json', data_files=data_args.valid_data_path, split="train", cache_dir=training_args.cache_dir)
    raw_test_datasets = load_dataset('json', data_files=data_args.test_data_path, split="train", cache_dir=training_args.cache_dir)
    if training_args.local_rank > 0: 
        torch.distributed.barrier()

    train_dataset = raw_train_datasets.map(
        train_preprocess_function_pose,
        batched=True,
        batch_size=3000,
        num_proc=32,
        remove_columns=raw_train_datasets.column_names,
        load_from_cache_file=True, # not args.overwrite_cache
        desc="Running tokenizer on train dataset",
        fn_kwargs={"tokenizer": tokenizer, "scaled_max_position_embeddings": scaled_max_position_embeddings, "model_max_position_embeddings": training_args.model_max_position_embeddings}
    )


    valid_dataset = raw_valid_datasets.map(
        test_preprocess_function,
        batched=True,
        batch_size=3000,
        num_proc=1,
        remove_columns=raw_valid_datasets.column_names,
        load_from_cache_file=True, # not args.overwrite_cache
        desc="Running tokenizer on valid dataset",
        fn_kwargs={"tokenizer": tokenizer, "inference_length": training_args.inference_length}
    )

    test_dataset = raw_test_datasets.map(
        test_preprocess_function,
        batched=True,
        batch_size=3000,
        num_proc=1,
        remove_columns=raw_test_datasets.column_names,
        load_from_cache_file=True, # not args.overwrite_cache
        desc="Running tokenizer on test dataset",
        fn_kwargs={"tokenizer": tokenizer, "inference_length": training_args.inference_length}
    )

    if training_args.local_rank == 0:
        torch.distributed.barrier()
    
    if training_args.local_rank == 0:
        print(len(train_dataset))
        for index in random.sample(range(len(train_dataset)), 3):
            print(f"Sample {index} of the training set: {train_dataset[index]}.")
    
    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
    # data_module = dict(eval_dataset=valid_dataset, data_collator=data_collator)
    data_module = dict(train_dataset=train_dataset, eval_dataset=valid_dataset, data_collator=data_collator)

    #Tell Trainer not to attempt DataParallel
    model.is_parallelizable = True
    model.model_parallel = True

    trainer = Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)
    model.config.use_cache = False

    if training_args.do_train:
        logging.info("*** Start Training ***")
        trainer.train()
        trainer.save_state()
        safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir)

    if training_args.do_eval:
        logging.info("*** Evaluate on valid set***")
        metrics = trainer.evaluate(eval_dataset=valid_dataset)
        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    if training_args.do_predict:
        logging.info("*** Evaluate on test set***")
        metrics = trainer.evaluate(eval_dataset=test_dataset)
        trainer.log_metrics("predict", metrics)
        trainer.save_metrics("predict", metrics)




if __name__ == "__main__":
    train()

src/train_gptj.py
dwzhu-pku/PoSE
#    Modification Copyright 2023 Dawei Zhu
#    Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
#
#    Licensed under the Apache License, Version 2.0 (the "License");
#    you may not use this file except in compliance with the License.
#    You may obtain a copy of the License at
#
#        http://www.apache.org/licenses/LICENSE-2.0
#
#    Unless required by applicable law or agreed to in writing, software
#    distributed under the License is distributed on an "AS IS" BASIS,
#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#    See the License for the specific language governing permissions and
#    limitations under the License.

import copy
import logging
import random
from dataclasses import dataclass, field
from typing import Optional, Dict, Sequence

import torch
import torch.distributed
import transformers
import deepspeed
from torch.utils.data import Dataset
from transformers import Trainer, GPT2ForQuestionAnswering, AutoConfig
from datasets import load_dataset

from my_modeling_llama import LlamaForCausalLM
#import utils

IGNORE_INDEX = -100
DEFAULT_PAD_TOKEN = "[PAD]"
DEFAULT_EOS_TOKEN = "</s>"
DEFAULT_BOS_TOKEN = "<s>"
DEFAULT_UNK_TOKEN = "</s>"
PROMPT_DICT = {
    "prompt_input": (
        "Below is an instruction that describes a task, paired with an input that provides further context. "
        "Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
    ),
    "prompt_no_input": (
        "Below is an instruction that describes a task. "
        "Write a response that appropriately completes the request.\n\n"
        "### Instruction:\n{instruction}\n\n### Response:"
    ),
}


@dataclass
class ModelArguments:
    model_name_or_path: Optional[str] = field(default="facebook/opt-125m")


@dataclass
class DataArguments:
    train_data_path: str = field(default=None, metadata={"help": "Path to the training data."})
    valid_data_path: str = field(default=None, metadata={"help": "Path to the training data."})
    test_data_path: str = field(default=None, metadata={"help": "Path to the test data."})


@dataclass
class TrainingArguments(transformers.TrainingArguments):
    cache_dir: Optional[str] = field(default=None)
    optim: str = field(default="adamw_torch")
    model_max_position_embeddings: int = field(
        default=2048,
        metadata={"help": "Maximum position embeddings."},
    )
    inference_length: int = field(
        default=2048,
        metadata={"help": "Maximum position embeddings."},
    )
    rope_scaling_type: Optional[str] = field(default=None)
    rope_scaling_factor: float = field(default=1.0)


def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str):
    """Collects the state dict and dump to disk."""
    state_dict = trainer.model.state_dict()
    if trainer.args.should_save:
        cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()}
        del state_dict
        trainer._save(output_dir, state_dict=cpu_state_dict)  # noqa


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

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

    if num_new_tokens > 0:
        input_embeddings = model.get_input_embeddings().weight.data
        output_embeddings = model.get_output_embeddings().weight.data

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

        input_embeddings[-num_new_tokens:] = input_embeddings_avg
        output_embeddings[-num_new_tokens:] = output_embeddings_avg




@dataclass
class DataCollatorForSupervisedDataset(object):
    """Collate examples for supervised fine-tuning."""

    tokenizer: transformers.PreTrainedTokenizer

    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
        input_ids, labels, position_ids = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels", "position_ids"))
        input_ids = [torch.tensor(x) for x in input_ids]
        input_ids = torch.nn.utils.rnn.pad_sequence(
            input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
        )
        labels = [torch.tensor(x) for x in labels]
        labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
        position_ids = [torch.tensor(x) for x in position_ids]
        position_ids = torch.nn.utils.rnn.pad_sequence(position_ids, batch_first=True, padding_value=0)
        return dict(
            input_ids=input_ids,
            labels=labels,
            position_ids=position_ids,
            attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
        )

def train_preprocess_function_randomized(examples, tokenizer, scaled_max_position_embeddings, model_max_position_embeddings):

    inputs = examples["text"]
    model_inputs = tokenizer(inputs, padding=False, truncation=True, max_length=model_max_position_embeddings)
    position_ids = [torch.arange(len(ids), dtype=torch.long) for ids in model_inputs["input_ids"]]

    for pos_ids in position_ids:
        len_pos_ids = len(pos_ids)

        tot_pos_list = list(range(scaled_max_position_embeddings))
        new_pos_list = random.sample(tot_pos_list, len_pos_ids)
        new_pos_list.sort()
        pos_ids[:] = torch.tensor(new_pos_list, dtype=torch.long)

    model_inputs["position_ids"] = position_ids
    model_inputs["labels"] = model_inputs["input_ids"]

    return model_inputs

def train_preprocess_function_pose(examples, tokenizer, scaled_max_position_embeddings, model_max_position_embeddings):

    inputs = examples["text"]
    raw_model_inputs = tokenizer(inputs, padding=False, truncation=True, max_length=model_max_position_embeddings*5)

    input_ids = []
    position_ids = []

    for ids in raw_model_inputs["input_ids"]:

        len_chunk = min(len(ids), model_max_position_embeddings)
        len_input = len(ids)
        lt1 = 0
        rt1 = random.randint(1, (len_chunk+1)//2)
        rt2 = random.randint(lt1+len_chunk, len_input)
        lt2 = rt2 - (len_chunk - (rt1-lt1))
        chunked_ids = ids[lt1:rt1] + ids[lt2:rt2]
        input_ids.append(chunked_ids)

        pos_ids = torch.arange(len(chunked_ids), dtype=torch.long)
        len_pos_ids = len(pos_ids)
        # lt = random.randint(0, scaled_max_position_embeddings-len_pos_ids)
        lt = 0 # this revision makes the coverage possiblity more uniform for large relative positions
        rt = random.randint(lt, scaled_max_position_embeddings-len_pos_ids)

        pos_ids[:rt1-lt1] += lt
        pos_ids[rt1-lt1:] += rt
        position_ids.append(pos_ids)
    
    model_inputs = {"input_ids": input_ids, "position_ids": position_ids, "labels": input_ids}

    return model_inputs

def train_preprocess_function_pi(examples, tokenizer, scaled_max_position_embeddings, model_max_position_embeddings):

    inputs = examples["text"]
    model_inputs = tokenizer(inputs, padding=False, truncation=True, max_length=scaled_max_position_embeddings)
    position_ids = [torch.arange(len(ids), dtype=torch.long) for ids in model_inputs["input_ids"]]
    model_inputs["position_ids"] = position_ids
    model_inputs["labels"] = model_inputs["input_ids"]

    return model_inputs

def test_preprocess_function(examples, tokenizer, inference_length):

    inputs = examples["text"]
    model_inputs = tokenizer(inputs, padding=False, truncation=True, max_length=inference_length)
    position_ids = [torch.arange(len(ids), dtype=torch.long) for ids in model_inputs["input_ids"]]
    model_inputs["position_ids"] = position_ids
    model_inputs["labels"] = model_inputs["input_ids"]

    return model_inputs

              
def train():
    parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=training_args.cache_dir)
    scaled_max_position_embeddings=int(training_args.model_max_position_embeddings * training_args.rope_scaling_factor)
    config.max_position_embeddings=scaled_max_position_embeddings

    if training_args.rope_scaling_type is not None:
        config.rope_scaling={"type": training_args.rope_scaling_type, "factor": training_args.rope_scaling_factor}
        if training_args.rope_scaling_type == "yarn":
            config.rope_scaling["original_max_position_embeddings"] = training_args.model_max_position_embeddings
        
    model = LlamaForCausalLM.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=training_args.cache_dir,
        config=config,
    )

    tokenizer = transformers.LlamaTokenizer.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=training_args.cache_dir,
        padding_side="right",
        use_fast=True,
    )
    if tokenizer.pad_token is None:
        smart_tokenizer_and_embedding_resize(
            special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN),
            tokenizer=tokenizer,
            model=model,
        )
    if "llama" in model_args.model_name_or_path:
        tokenizer.add_special_tokens(
            {
                "eos_token": DEFAULT_EOS_TOKEN,
                "bos_token": DEFAULT_BOS_TOKEN,
                "unk_token": DEFAULT_UNK_TOKEN,
            }
        )

    raw_train_datasets = load_dataset('json', data_files=data_args.train_data_path, split="train", cache_dir=training_args.cache_dir)
    raw_valid_datasets = load_dataset('json', data_files=data_args.valid_data_path, split="train", cache_dir=training_args.cache_dir)
    raw_test_datasets = load_dataset('json', data_files=data_args.test_data_path, split="train", cache_dir=training_args.cache_dir)
    if training_args.local_rank > 0: 
        torch.distributed.barrier()

    train_dataset = raw_train_datasets.map(
        train_preprocess_function_pose,
        batched=True,
        batch_size=3000,
        num_proc=32,
        remove_columns=raw_train_datasets.column_names,
        load_from_cache_file=True, # not args.overwrite_cache
        desc="Running tokenizer on train dataset",
        fn_kwargs={"tokenizer": tokenizer, "scaled_max_position_embeddings": scaled_max_position_embeddings, "model_max_position_embeddings": training_args.model_max_position_embeddings}
    )


    valid_dataset = raw_valid_datasets.map(
        test_preprocess_function,
        batched=True,
        batch_size=3000,
        num_proc=1,
        remove_columns=raw_valid_datasets.column_names,
        load_from_cache_file=True, # not args.overwrite_cache
        desc="Running tokenizer on valid dataset",
        fn_kwargs={"tokenizer": tokenizer, "inference_length": training_args.inference_length}
    )

    test_dataset = raw_test_datasets.map(
        test_preprocess_function,
        batched=True,
        batch_size=3000,
        num_proc=1,
        remove_columns=raw_test_datasets.column_names,
        load_from_cache_file=True, # not args.overwrite_cache
        desc="Running tokenizer on test dataset",
        fn_kwargs={"tokenizer": tokenizer, "inference_length": training_args.inference_length}
    )

    if training_args.local_rank == 0:
        torch.distributed.barrier()
    
    if training_args.local_rank == 0:
        print(len(train_dataset))
        for index in random.sample(range(len(train_dataset)), 3):
            print(f"Sample {index} of the training set: {train_dataset[index]}.")
    
    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
    # data_module = dict(eval_dataset=valid_dataset, data_collator=data_collator)
    data_module = dict(train_dataset=train_dataset, eval_dataset=valid_dataset, data_collator=data_collator)

    #Tell Trainer not to attempt DataParallel
    model.is_parallelizable = True
    model.model_parallel = True

    trainer = Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)
    model.config.use_cache = False

    if training_args.do_train:
        logging.info("*** Start Training ***")
        trainer.train()
        trainer.save_state()
        safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir)

    if training_args.do_eval:
        logging.info("*** Evaluate on valid set***")
        metrics = trainer.evaluate(eval_dataset=valid_dataset)
        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    if training_args.do_predict:
        logging.info("*** Evaluate on test set***")
        metrics = trainer.evaluate(eval_dataset=test_dataset)
        trainer.log_metrics("predict", metrics)
        trainer.save_metrics("predict", metrics)




if __name__ == "__main__":
    train()

src/train_pose.py
dwzhu-pku/PoSE