temporarily switch

README.md

@@ -4,7 +4,7 @@ emoji: 🚀
 colorFrom: red
 colorTo: red
 sdk: gradio
-sdk_version: 5.7.0
+sdk_version: 5.8.0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -1,74 +1,131 @@
-import sys
-import os
+import torch
+import transformers
+import warnings
 import time
-import argparse
-import subprocess
-
-import bunny.serve.gradio_web_server as gws
-
-subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-e', '.'])
-
-
-def start_controller():
-    controller_command = [
-        sys.executable, '-m', 'bunny.serve.controller',
-        '--host', '0.0.0.0',
-        '--port', '10000'
-    ]
-    return subprocess.Popen(controller_command)
-
-
-def start_worker(port: int, model_path: str, model_type: str):
-    worker_command = [
-        sys.executable, '-m', 'bunny.serve.model_worker',
-        '--host', '0.0.0.0',
-        '--controller', 'http://localhost:10000',
-        '--port', f'{port}',
-        '--worker', f'http://localhost:{port}',
-        '--model-path', model_path,
-        '--model-type', model_type
-    ]
-    return subprocess.Popen(worker_command)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--host", type=str, default="0.0.0.0")
-    parser.add_argument("--port", type=int)
-    parser.add_argument("--controller-url", type=str, default="http://localhost:10000")
-    parser.add_argument("--concurrency-count", type=int, default=5)
-    parser.add_argument("--model-list-mode", type=str, default="reload", choices=["once", "reload"])
-    parser.add_argument("--share", action="store_true")
-    parser.add_argument("--moderate", action="store_true")
-    parser.add_argument("--embed", action="store_true")
-    gws.args = parser.parse_args()
-    gws.models = []
-
-    controller_proc = start_controller()
-
-    worker_procs = []
-
-    worker_procs.append(start_worker(port=40000, model_path='BAAI/Bunny-v1_1-Llama-3-8B-V', model_type='llama3-8b'))
-    worker_procs.append(start_worker(port=40001, model_path='BAAI/Bunny-v1_1-4B', model_type='phi-3'))
-    worker_procs.append(start_worker(port=40002, model_path='BAAI/Bunny-v1_0-3B', model_type='phi-2'))
-
-    time.sleep(60)
-
-    exit_status = 0
-    try:
-        demo = gws.build_demo(embed_mode=gws.args.embed)
-        demo.launch(
-            server_name=gws.args.host,
-            server_port=gws.args.port,
-            share=gws.args.share,
-            debug=True,
-            max_threads=10
-        )
-    except Exception as e:
-        print(e)
-        exit_status = 1
-    finally:
-        for worker_proc in worker_procs:
-            worker_proc.kill()
-        controller_proc.kill()
-        sys.exit(exit_status)
+import spaces
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from PIL import Image
+from threading import Thread
+
+
+transformers.logging.set_verbosity_error()
+transformers.logging.disable_progress_bar()
+warnings.filterwarnings("ignore")
+
+
+device = "cuda"  # or cpu
+torch.set_default_device(device)
+
+model_name = "BAAI/Bunny-v1_1-Llama-3-8B-V"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16, # float32 for cpu
+    device_map="auto",
+    trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name,
+    trust_remote_code=True)
+
+
+@spaces.GPU
+def bot_streaming(message, history):
+    print(message)
+    if message["files"]:
+        # message["files"][-1] is a Dict or just a string
+        if type(message["files"][-1]) == dict:
+            image_file = message["files"][-1]["path"]
+        else:
+            image_file = message["files"][-1]
+    else:
+        image_file = None
+        # if there's no image uploaded for this turn, look for images in the past turns
+        # kept inside tuples, take the last one
+        for hist in history:
+            if type(hist[0]) == tuple:
+                image_file = hist[0][0]
+
+
+    prompt = message["text"]
+    if image_file is None:
+        text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:"
+        input_ids = torch.tensor(tokenizer(text).input_ids, dtype=torch.long).unsqueeze(0).to(device)
+    else:
+        text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\n{prompt} ASSISTANT:"
+        text_chunks = [tokenizer(chunk).input_ids for chunk in text.split("<image>")]
+        input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0).to(device)
+        
+    if image_file is not None:
+        image = Image.open(image_file)
+        image_tensor = model.process_images([image], model.config).to(dtype=model.dtype, device=device)
+    else:
+        image_tensor = None
+
+    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=15)
+
+    thread = Thread(target=model.generate, kwargs=dict(
+            inputs=input_ids,
+            images=image_tensor,
+            do_sample=True,
+            temperature=0.2,
+            top_p=0.7,
+            max_new_tokens=512,
+            streamer=streamer,
+            use_cache=True,
+            repetition_penalty=1.08
+        ))
+    thread.start()
+
+    buffer = ""
+    time.sleep(0.5)
+    for new_text in streamer:
+        if "<|end_of_text|>" in new_text:
+            new_text = new_text.split("<|end_of_text|>")[0]
+        buffer += new_text
+
+        # generated_text_without_prompt = buffer[len(text_prompt):]
+        generated_text_without_prompt = buffer
+        # print(generated_text_without_prompt)
+        time.sleep(0.06)
+        # print(f"new_text: {generated_text_without_prompt}")
+        yield generated_text_without_prompt
+
+
+title_markdown = ("""
+# 🐰 Bunny: A family of lightweight multimodal models
+
+[📖 [Technical report](https://arxiv.org/abs/2402.11530)] | [🏠 [Code](https://github.com/BAAI-DCAI/Bunny)] | [🤗 [Bunny-v1.1-Llama-3-8B-V](https://huggingface.co/BAAI/Bunny-v1_1-Llama-3-8B-V)] | [🤗 [Bunny-v1.1-4B](https://huggingface.co/BAAI/Bunny-v1_1-4B)] | [🤗 [Bunny-v1.0-3B](https://huggingface.co/BAAI/Bunny-v1_0-3B)]
+
+""")
+
+chatbot = gr.Chatbot(
+    elem_id="chatbot",
+    label="Bunny-v1.1-Llama-3-8B-V",
+    avatar_images=[f"./assets/user.png", f"./assets/icon.jpg"],
+    height=550
+    )
+
+chat_input = gr.MultimodalTextbox(
+    interactive=True,
+    file_types=["image"],
+    placeholder="Enter message or upload file...",
+    show_label=False
+)
+
+with gr.Blocks(fill_height=True) as demo:
+    gr.Markdown(title_markdown)
+
+    gr.ChatInterface(
+        fn=bot_streaming,
+        stop_btn="Stop Generation",
+        multimodal=True,
+        textbox=chat_input,
+        chatbot=chatbot
+    )
+
+    gr.Examples(examples=[{"text": "What is the astronaut holding in his hand?", "files": ["./assets/example_1.png"]},
+            {"text": "Why is the image funny?", "files": ["./assets/example_2.png"]}], inputs=chat_input)
+
+
+demo.queue(api_open=False)
+demo.launch(show_api=False, share=False)

bunny/constants.py

@@ -1,7 +0,0 @@
-# Model Constants
-IGNORE_INDEX = -100
-IMAGE_TOKEN_INDEX = -200
-DEFAULT_IMAGE_TOKEN = "<image>"
-CONTROLLER_HEART_BEAT_EXPIRATION = 30
-LOGDIR = "gradio-logs"
-WORKER_HEART_BEAT_INTERVAL = 15

bunny/conversation.py

@@ -1,239 +0,0 @@
-import dataclasses
-from enum import auto, Enum
-from typing import List
-
-
-class SeparatorStyle(Enum):
-    """Different separator style."""
-    TWO = auto()
-    PLAIN = auto()
-
-
-@dataclasses.dataclass
-class Conversation:
-    """A class that keeps all conversation history."""
-    system: str
-    roles: List[str]
-    messages: List[List[str]]
-    offset: int
-    sep_style: SeparatorStyle
-    sep: str = "###"
-    sep2: str = None
-    version: str = "Unknown"
-
-    skip_next: bool = False
-
-    def get_prompt(self):
-        messages = self.messages
-        if len(messages) > 0 and type(messages[0][1]) is tuple:
-            messages = self.messages.copy()
-            init_role, init_msg = messages[0].copy()
-            init_msg = init_msg[0].replace("<image>", "").strip()
-            if 'mmtag' in self.version:
-                messages[0] = (init_role, init_msg)
-                messages.insert(0, (self.roles[0], "<Image><image></Image>"))
-                messages.insert(1, (self.roles[1], "Received."))
-            else:
-                messages[0] = (init_role, "<image>\n" + init_msg)
-
-        if self.sep_style == SeparatorStyle.TWO:
-            seps = [self.sep, self.sep2]
-            ret = self.system + seps[0]
-            for i, (role, message) in enumerate(messages):
-                if message:
-                    if type(message) is tuple:
-                        message, _, _ = message
-                    ret += role + ": " + message + seps[i % 2]
-                else:
-                    ret += role + ":"
-
-        elif self.sep_style == SeparatorStyle.PLAIN:
-            seps = [self.sep, self.sep2]
-            ret = self.system
-            for i, (role, message) in enumerate(messages):
-                if message:
-                    if type(message) is tuple:
-                        message, _, _ = message
-                    ret += message + seps[i % 2]
-                else:
-                    ret += ""
-        else:
-            raise ValueError(f"Invalid style: {self.sep_style}")
-
-        return ret
-
-    def append_message(self, role, message):
-        self.messages.append([role, message])
-
-    def get_images(self, return_pil=False):
-        images = []
-        for i, (role, msg) in enumerate(self.messages[self.offset:]):
-            if i % 2 == 0:
-                if type(msg) is tuple:
-                    import base64
-                    from io import BytesIO
-                    from PIL import Image
-                    msg, image, image_process_mode = msg
-                    if image_process_mode == "Pad":
-                        def expand2square(pil_img, background_color=(122, 116, 104)):
-                            width, height = pil_img.size
-                            if width == height:
-                                return pil_img
-                            elif width > height:
-                                result = Image.new(pil_img.mode, (width, width), background_color)
-                                result.paste(pil_img, (0, (width - height) // 2))
-                                return result
-                            else:
-                                result = Image.new(pil_img.mode, (height, height), background_color)
-                                result.paste(pil_img, ((height - width) // 2, 0))
-                                return result
-
-                        image = expand2square(image)
-                    elif image_process_mode in ["Default", "Crop"]:
-                        pass
-                    elif image_process_mode == "Resize":
-                        image = image.resize((336, 336))
-                    else:
-                        raise ValueError(f"Invalid image_process_mode: {image_process_mode}")
-
-                    if return_pil:
-                        images.append(image)
-                    else:
-                        buffered = BytesIO()
-                        image.save(buffered, format="PNG")
-                        img_b64_str = base64.b64encode(buffered.getvalue()).decode()
-                        images.append(img_b64_str)
-        return images
-
-    def to_gradio_chatbot(self):
-        ret = []
-        for i, (role, msg) in enumerate(self.messages[self.offset:]):
-            if i % 2 == 0:
-                if type(msg) is tuple:
-                    import base64
-                    from io import BytesIO
-                    msg, image, image_process_mode = msg
-                    max_hw, min_hw = max(image.size), min(image.size)
-                    aspect_ratio = max_hw / min_hw
-                    max_len, min_len = 800, 400
-                    shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw))
-                    longest_edge = int(shortest_edge * aspect_ratio)
-                    W, H = image.size
-                    if H > W:
-                        H, W = longest_edge, shortest_edge
-                    else:
-                        H, W = shortest_edge, longest_edge
-                    image = image.resize((W, H))
-                    buffered = BytesIO()
-                    image.save(buffered, format="JPEG")
-                    img_b64_str = base64.b64encode(buffered.getvalue()).decode()
-                    img_str = f'<img src="data:image/png;base64,{img_b64_str}" alt="user upload image" />'
-                    msg = img_str + msg.replace('<image>', '').strip()
-                    ret.append([msg, None])
-                else:
-                    ret.append([msg, None])
-            else:
-                ret[-1][-1] = msg
-        return ret
-
-    def copy(self):
-        return Conversation(
-            system=self.system,
-            roles=self.roles,
-            messages=[[x, y] for x, y in self.messages],
-            offset=self.offset,
-            sep_style=self.sep_style,
-            sep=self.sep,
-            sep2=self.sep2,
-            version=self.version)
-
-    def dict(self):
-        if len(self.get_images()) > 0:
-            return {
-                "system": self.system,
-                "roles": self.roles,
-                "messages": [[x, y[0] if type(y) is tuple else y] for x, y in self.messages],
-                "offset": self.offset,
-                "sep": self.sep,
-                "sep2": self.sep2,
-            }
-        return {
-            "system": self.system,
-            "roles": self.roles,
-            "messages": self.messages,
-            "offset": self.offset,
-            "sep": self.sep,
-            "sep2": self.sep2,
-        }
-
-
-conv_bunny = Conversation(
-    system="A chat between a curious user and an artificial intelligence assistant. "
-           "The assistant gives helpful, detailed, and polite answers to the user's questions.",
-    roles=("USER", "ASSISTANT"),
-    version="bunny",
-    messages=(),
-    offset=0,
-    sep_style=SeparatorStyle.TWO,
-    sep=" ",
-    sep2="<|endoftext|>",
-)
-
-conv_phi3 = Conversation(
-    system="A chat between a curious user and an artificial intelligence assistant. "
-           "The assistant gives helpful, detailed, and polite answers to the user's questions.",
-    roles=("USER", "ASSISTANT"),
-    version="phi3",
-    messages=(),
-    offset=0,
-    sep_style=SeparatorStyle.TWO,
-    sep=" ",
-    sep2="<|endoftext|>",
-)
-
-conv_minicpm = Conversation(
-    system="A chat between a curious user and an artificial intelligence assistant. "
-           "The assistant gives helpful, detailed, and polite answers to the user's questions.",
-    roles=("USER", "ASSISTANT"),
-    version="minicpm",
-    messages=(),
-    offset=0,
-    sep_style=SeparatorStyle.TWO,
-    sep=" ",
-    sep2="</s>",
-)
-
-conv_llama = Conversation(
-    system="A chat between a curious user and an artificial intelligence assistant. "
-           "The assistant gives helpful, detailed, and polite answers to the user's questions.",
-    roles=("USER", "ASSISTANT"),
-    version="llama",
-    messages=(),
-    offset=0,
-    sep_style=SeparatorStyle.TWO,
-    sep=" ",
-    sep2="<|end_of_text|>",
-)
-
-conv_plain = Conversation(
-    system="",
-    roles=("", ""),
-    messages=(
-    ),
-    offset=0,
-    sep_style=SeparatorStyle.PLAIN,
-    sep="\n",
-)
-
-default_conversation = conv_bunny
-conv_templates = {
-    "default": conv_bunny,
-    "bunny": conv_bunny,
-    "phi3": conv_phi3,
-    "plain": conv_plain,
-    'minicpm': conv_minicpm,
-    'llama': conv_llama
-}
-
-if __name__ == "__main__":
-    print(default_conversation.get_prompt())

bunny/eval/m4c_evaluator.py

@@ -1,334 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import re
-
-from tqdm import tqdm
-
-
-class EvalAIAnswerProcessor:
-    """
-    Processes an answer similar to Eval AI
-        copied from
-        https://github.com/facebookresearch/mmf/blob/c46b3b3391275b4181567db80943473a89ab98ab/pythia/tasks/processors.py#L897
-    """
-
-    CONTRACTIONS = {
-        "aint": "ain't",
-        "arent": "aren't",
-        "cant": "can't",
-        "couldve": "could've",
-        "couldnt": "couldn't",
-        "couldn'tve": "couldn't've",
-        "couldnt've": "couldn't've",
-        "didnt": "didn't",
-        "doesnt": "doesn't",
-        "dont": "don't",
-        "hadnt": "hadn't",
-        "hadnt've": "hadn't've",
-        "hadn'tve": "hadn't've",
-        "hasnt": "hasn't",
-        "havent": "haven't",
-        "hed": "he'd",
-        "hed've": "he'd've",
-        "he'dve": "he'd've",
-        "hes": "he's",
-        "howd": "how'd",
-        "howll": "how'll",
-        "hows": "how's",
-        "Id've": "I'd've",
-        "I'dve": "I'd've",
-        "Im": "I'm",
-        "Ive": "I've",
-        "isnt": "isn't",
-        "itd": "it'd",
-        "itd've": "it'd've",
-        "it'dve": "it'd've",
-        "itll": "it'll",
-        "let's": "let's",
-        "maam": "ma'am",
-        "mightnt": "mightn't",
-        "mightnt've": "mightn't've",
-        "mightn'tve": "mightn't've",
-        "mightve": "might've",
-        "mustnt": "mustn't",
-        "mustve": "must've",
-        "neednt": "needn't",
-        "notve": "not've",
-        "oclock": "o'clock",
-        "oughtnt": "oughtn't",
-        "ow's'at": "'ow's'at",
-        "'ows'at": "'ow's'at",
-        "'ow'sat": "'ow's'at",
-        "shant": "shan't",
-        "shed've": "she'd've",
-        "she'dve": "she'd've",
-        "she's": "she's",
-        "shouldve": "should've",
-        "shouldnt": "shouldn't",
-        "shouldnt've": "shouldn't've",
-        "shouldn'tve": "shouldn't've",
-        "somebody'd": "somebodyd",
-        "somebodyd've": "somebody'd've",
-        "somebody'dve": "somebody'd've",
-        "somebodyll": "somebody'll",
-        "somebodys": "somebody's",
-        "someoned": "someone'd",
-        "someoned've": "someone'd've",
-        "someone'dve": "someone'd've",
-        "someonell": "someone'll",
-        "someones": "someone's",
-        "somethingd": "something'd",
-        "somethingd've": "something'd've",
-        "something'dve": "something'd've",
-        "somethingll": "something'll",
-        "thats": "that's",
-        "thered": "there'd",
-        "thered've": "there'd've",
-        "there'dve": "there'd've",
-        "therere": "there're",
-        "theres": "there's",
-        "theyd": "they'd",
-        "theyd've": "they'd've",
-        "they'dve": "they'd've",
-        "theyll": "they'll",
-        "theyre": "they're",
-        "theyve": "they've",
-        "twas": "'twas",
-        "wasnt": "wasn't",
-        "wed've": "we'd've",
-        "we'dve": "we'd've",
-        "weve": "we've",
-        "werent": "weren't",
-        "whatll": "what'll",
-        "whatre": "what're",
-        "whats": "what's",
-        "whatve": "what've",
-        "whens": "when's",
-        "whered": "where'd",
-        "wheres": "where's",
-        "whereve": "where've",
-        "whod": "who'd",
-        "whod've": "who'd've",
-        "who'dve": "who'd've",
-        "wholl": "who'll",
-        "whos": "who's",
-        "whove": "who've",
-        "whyll": "why'll",
-        "whyre": "why're",
-        "whys": "why's",
-        "wont": "won't",
-        "wouldve": "would've",
-        "wouldnt": "wouldn't",
-        "wouldnt've": "wouldn't've",
-        "wouldn'tve": "wouldn't've",
-        "yall": "y'all",
-        "yall'll": "y'all'll",
-        "y'allll": "y'all'll",
-        "yall'd've": "y'all'd've",
-        "y'alld've": "y'all'd've",
-        "y'all'dve": "y'all'd've",
-        "youd": "you'd",
-        "youd've": "you'd've",
-        "you'dve": "you'd've",
-        "youll": "you'll",
-        "youre": "you're",
-        "youve": "you've",
-    }
-
-    NUMBER_MAP = {
-        "none": "0",
-        "zero": "0",
-        "one": "1",
-        "two": "2",
-        "three": "3",
-        "four": "4",
-        "five": "5",
-        "six": "6",
-        "seven": "7",
-        "eight": "8",
-        "nine": "9",
-        "ten": "10",
-    }
-    ARTICLES = ["a", "an", "the"]
-    PERIOD_STRIP = re.compile(r"(?!<=\d)(\.)(?!\d)")
-    COMMA_STRIP = re.compile(r"(?<=\d)(\,)+(?=\d)")
-    PUNCTUATIONS = [
-        ";",
-        r"/",
-        "[",
-        "]",
-        '"',
-        "{",
-        "}",
-        "(",
-        ")",
-        "=",
-        "+",
-        "\\",
-        "_",
-        "-",
-        ">",
-        "<",
-        "@",
-        "`",
-        ",",
-        "?",
-        "!",
-    ]
-
-    def __init__(self, *args, **kwargs):
-        pass
-
-    def word_tokenize(self, word):
-        word = word.lower()
-        word = word.replace(",", "").replace("?", "").replace("'s", " 's")
-        return word.strip()
-
-    def process_punctuation(self, in_text):
-        out_text = in_text
-        for p in self.PUNCTUATIONS:
-            if (p + " " in in_text or " " + p in in_text) or (
-                    re.search(self.COMMA_STRIP, in_text) is not None
-            ):
-                out_text = out_text.replace(p, "")
-            else:
-                out_text = out_text.replace(p, " ")
-        out_text = self.PERIOD_STRIP.sub("", out_text, re.UNICODE)
-        return out_text
-
-    def process_digit_article(self, in_text):
-        out_text = []
-        temp_text = in_text.lower().split()
-        for word in temp_text:
-            word = self.NUMBER_MAP.setdefault(word, word)
-            if word not in self.ARTICLES:
-                out_text.append(word)
-            else:
-                pass
-        for word_id, word in enumerate(out_text):
-            if word in self.CONTRACTIONS:
-                out_text[word_id] = self.CONTRACTIONS[word]
-        out_text = " ".join(out_text)
-        return out_text
-
-    def __call__(self, item):
-        item = self.word_tokenize(item)
-        item = item.replace("\n", " ").replace("\t", " ").strip()
-        item = self.process_punctuation(item)
-        item = self.process_digit_article(item)
-        return item
-
-
-class TextVQAAccuracyEvaluator:
-    def __init__(self):
-        self.answer_processor = EvalAIAnswerProcessor()
-
-    def _compute_answer_scores(self, raw_answers):
-        """
-        compute the accuracy (soft score) of human answers
-        """
-        answers = [self.answer_processor(a) for a in raw_answers]
-        assert len(answers) == 10
-        gt_answers = list(enumerate(answers))
-        unique_answers = set(answers)
-        unique_answer_scores = {}
-
-        for unique_answer in unique_answers:
-            accs = []
-            for gt_answer in gt_answers:
-                other_answers = [item for item in gt_answers if item != gt_answer]
-                matching_answers = [
-                    item for item in other_answers if item[1] == unique_answer
-                ]
-                acc = min(1, float(len(matching_answers)) / 3)
-                accs.append(acc)
-            unique_answer_scores[unique_answer] = sum(accs) / len(accs)
-
-        return unique_answer_scores
-
-    def eval_pred_list(self, pred_list):
-        pred_scores = []
-        for entry in tqdm(pred_list):
-            pred_answer = self.answer_processor(entry["pred_answer"])
-            unique_answer_scores = self._compute_answer_scores(entry["gt_answers"])
-            score = unique_answer_scores.get(pred_answer, 0.0)
-            pred_scores.append(score)
-
-        accuracy = sum(pred_scores) / len(pred_scores)
-        return accuracy
-
-
-class STVQAAccuracyEvaluator:
-    def __init__(self):
-        self.answer_processor = EvalAIAnswerProcessor()
-
-    def eval_pred_list(self, pred_list):
-        pred_scores = []
-        for entry in pred_list:
-            pred_answer = self.answer_processor(entry["pred_answer"])
-            gts = [self.answer_processor(a) for a in entry["gt_answers"]]
-            score = 1.0 if pred_answer in gts else 0.0
-            pred_scores.append(score)
-
-        accuracy = sum(pred_scores) / len(pred_scores)
-        return accuracy
-
-
-class STVQAANLSEvaluator:
-    def __init__(self):
-        import editdistance  # install with `pip install editdistance`
-
-        self.get_edit_distance = editdistance.eval
-
-    def get_anls(self, s1, s2):
-        s1 = s1.lower().strip()
-        s2 = s2.lower().strip()
-        iou = 1 - self.get_edit_distance(s1, s2) / max(len(s1), len(s2))
-        anls = iou if iou >= 0.5 else 0.0
-        return anls
-
-    def eval_pred_list(self, pred_list):
-        pred_scores = []
-        for entry in pred_list:
-            anls = max(
-                self.get_anls(entry["pred_answer"], gt) for gt in entry["gt_answers"]
-            )
-            pred_scores.append(anls)
-
-        accuracy = sum(pred_scores) / len(pred_scores)
-        return accuracy
-
-
-class TextCapsBleu4Evaluator:
-    def __init__(self):
-        # The following script requires Java 1.8.0 and pycocotools installed.
-        # The pycocoevalcap can be installed with pip as
-        # pip install git+https://github.com/ronghanghu/coco-caption.git@python23
-        # Original pycocoevalcap code is at https://github.com/tylin/coco-caption
-        # but has no python3 support yet.
-        try:
-            from pycocoevalcap.bleu.bleu import Bleu
-            from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
-        except ModuleNotFoundError:
-            print(
-                "Please install pycocoevalcap module using "
-                "pip install git+https://github.com/ronghanghu/coco-caption.git@python23"  # noqa
-            )
-            raise
-
-        self.tokenizer = PTBTokenizer()
-        self.scorer = Bleu(4)
-
-    def eval_pred_list(self, pred_list):
-        # Create reference and hypotheses captions.
-        gts = {}
-        res = {}
-        for idx, entry in enumerate(pred_list):
-            gts[idx] = [{"caption": a} for a in entry["gt_answers"]]
-            res[idx] = [{"caption": entry["pred_answer"]}]
-
-        gts = self.tokenizer.tokenize(gts)
-        res = self.tokenizer.tokenize(res)
-        score, _ = self.scorer.compute_score(gts, res)
-
-        bleu4 = score[3]  # score is (Bleu-1, Bleu-2, Bleu-3, Bleu-4)
-        return bleu4

bunny/eval/model_vqa.py

@@ -1,111 +0,0 @@
-import argparse
-import torch
-import os
-import json
-from tqdm import tqdm
-import shortuuid
-
-from bunny.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
-from bunny.conversation import conv_templates, SeparatorStyle
-from bunny.model.builder import load_pretrained_model
-from bunny.util.utils import disable_torch_init
-from bunny.util.mm_utils import tokenizer_image_token, get_model_name_from_path, process_images
-
-from PIL import Image
-import math
-
-
-def split_list(lst, n):
-    """Split a list into n (roughly) equal-sized chunks"""
-    chunk_size = math.ceil(len(lst) / n)  # integer division
-    return [lst[i:i + chunk_size] for i in range(0, len(lst), chunk_size)]
-
-
-def get_chunk(lst, n, k):
-    chunks = split_list(lst, n)
-    return chunks[k]
-
-
-def eval_model(args):
-    # Model
-    disable_torch_init()
-    model_path = os.path.expanduser(args.model_path)
-    model_name = get_model_name_from_path(model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name,
-                                                                           args.model_type)
-
-    questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
-    questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
-    answers_file = os.path.expanduser(args.answers_file)
-    os.makedirs(os.path.dirname(answers_file), exist_ok=True)
-    ans_file = open(answers_file, "w")
-    for line in tqdm(questions):
-        idx = line["question_id"]
-        image_file = line["image"]
-        qs = line["text"]
-        cur_prompt = qs
-
-        qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
-
-        conv = conv_templates[args.conv_mode].copy()
-        conv.append_message(conv.roles[0], qs)
-        conv.append_message(conv.roles[1], None)
-        prompt = conv.get_prompt()
-
-        input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
-
-        image = Image.open(os.path.join(args.image_folder, image_file))
-        image_tensor = process_images([image], image_processor, model.config)[0]
-
-        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
-
-        with torch.inference_mode():
-            output_ids = model.generate(
-                input_ids,
-                images=image_tensor.unsqueeze(0).to(dtype=model.dtype, device='cuda', non_blocking=True),
-                do_sample=True if args.temperature > 0 else False,
-                temperature=args.temperature,
-                top_p=args.top_p,
-                num_beams=args.num_beams,
-                # no_repeat_ngram_size=3,
-                max_new_tokens=1024,
-                use_cache=True)
-
-        input_token_len = input_ids.shape[1]
-        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-        if n_diff_input_output > 0:
-            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
-        outputs = outputs.strip()
-        if outputs.endswith(stop_str):
-            outputs = outputs[:-len(stop_str)]
-        outputs = outputs.strip()
-
-        ans_id = shortuuid.uuid()
-        ans_file.write(json.dumps({"question_id": idx,
-                                   "prompt": cur_prompt,
-                                   "text": outputs,
-                                   "answer_id": ans_id,
-                                   "model_id": model_name,
-                                   "metadata": {}}) + "\n")
-        ans_file.flush()
-    ans_file.close()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model-path", type=str, default=None)
-    parser.add_argument("--model-base", type=str, default=None)
-    parser.add_argument("--model-type", type=str, default=None)
-    parser.add_argument("--image-folder", type=str, default=None)
-    parser.add_argument("--question-file", type=str, default=None)
-    parser.add_argument("--answers-file", type=str, default=None)
-    parser.add_argument("--conv-mode", type=str, default=None)
-    parser.add_argument("--num-chunks", type=int, default=1)
-    parser.add_argument("--chunk-idx", type=int, default=0)
-    parser.add_argument("--temperature", type=float, default=0.2)
-    parser.add_argument("--top_p", type=float, default=None)
-    parser.add_argument("--num_beams", type=int, default=1)
-    args = parser.parse_args()
-
-    eval_model(args)

bunny/eval/model_vqa_cmmmu.py

@@ -1,234 +0,0 @@
-import random
-import numpy as np
-import os
-import json
-import yaml
-import torch
-
-from tqdm import tqdm
-from datasets import load_dataset, concatenate_datasets
-from argparse import ArgumentParser
-
-from bunny.model.builder import load_pretrained_model
-from bunny.util.mm_utils import get_model_name_from_path, tokenizer_image_token, process_images
-from bunny.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
-from bunny.conversation import conv_templates
-
-CAT_CN2EN = {'艺术与设计': 'art_and_design',
-             '商业': 'business',
-             '健康与医学': 'health_and_medicine',
-             '人文社会科学': 'humanities_and_social_sciences',
-             '科学': 'science',
-             '技术与工程': 'technology_and_engineering'}
-
-
-def call_bunny_engine_df(args, sample, model, tokenizer=None, processor=None):
-    def deal_with_prompt(input_text):
-        qs = input_text
-        qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
-        return qs
-
-    prompt = sample['final_input_prompt']
-    prompt = deal_with_prompt(prompt)
-
-    conv = conv_templates[args.conv_mode].copy()
-    conv.append_message(conv.roles[0], prompt)
-    conv.append_message(conv.roles[1], None)
-    prompt = conv.get_prompt()
-    input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
-
-    image = sample['image_1']
-    if sample['image_2'] is not None:  # multiple images actually
-        if sample['type'] == '选择':
-            all_choices = sample['all_choices']
-            response = random.choice(all_choices)
-        else:
-            response = 'INVALID GENERATION FOR MULTIPLE IMAGE INPUTS'
-    elif image is not None:
-        output_ids = model.generate(
-            input_ids,
-            images=image.unsqueeze(0).to(dtype=model.dtype, device='cuda', non_blocking=True),
-            do_sample=False,
-            temperature=0,
-            top_p=None,
-            # num_beams=5,
-            max_new_tokens=128,
-            use_cache=True)
-
-        input_token_len = input_ids.shape[1]
-        # n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-        # if n_diff_input_output > 0:
-        #     print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-        response = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
-
-    return response
-
-
-def load_yaml(file_path):
-    with open(file_path, 'r') as stream:
-        try:
-            yaml_dict = yaml.safe_load(stream)
-        except yaml.YAMLError as exc:
-            print(exc)
-
-    return yaml_dict
-
-
-# DATA PROCESSING
-def construct_prompt(sample, config):
-    question = sample['question']
-    options = []
-    for i in range(1, 5):
-        if sample[f'option{i}'] is None:
-            break
-        options.append(sample[f'option{i}'])
-
-    example = ""
-    if sample['type'] == '选择':
-        start_chr = 'A'
-        prediction_range = []
-        for option in options:
-            prediction_range.append(start_chr)
-            example += f"({start_chr}) {option}\n"
-            start_chr = chr(ord(start_chr) + 1)
-        empty_prompt_sample_structure = config['multi_choice_example_format']
-        empty_prompt = empty_prompt_sample_structure.format(question, example)
-        res_dict = {}
-        res_dict['correct_choice'] = sample['answer']
-        res_dict['all_choices'] = prediction_range
-        res_dict['empty_prompt'] = empty_prompt
-        if config['task_instructions']:
-            res_dict['final_input_prompt'] = config['task_instructions'][0].strip() + '\n\n' + empty_prompt
-        else:
-            res_dict['final_input_prompt'] = empty_prompt
-
-        res_dict['gt_content'] = sample['answer']
-    elif sample['type'] == '判断':
-        empty_prompt_sample_structure = config['T/F_example_format']
-        empty_prompt = empty_prompt_sample_structure.format(question, example)
-        res_dict = {}
-        res_dict['empty_prompt'] = empty_prompt
-        if config['task_instructions']:
-            res_dict['final_input_prompt'] = config['task_instructions'][1].strip() + '\n\n' + empty_prompt
-        else:
-            res_dict['final_input_prompt'] = empty_prompt
-        res_dict['gt_content'] = sample['answer']
-    else:
-        empty_prompt_sample_structure = config['short_ans_example_format']
-        empty_prompt = empty_prompt_sample_structure.format(question)
-        res_dict = {}
-        res_dict['empty_prompt'] = empty_prompt
-        if config['task_instructions']:
-            res_dict['final_input_prompt'] = config['task_instructions'][2].strip() + '\n\n' + empty_prompt
-        else:
-            res_dict['final_input_prompt'] = empty_prompt
-        res_dict['gt_content'] = sample['answer']
-
-    res_dict.update(sample)
-    return res_dict
-
-
-def run_model(args, samples, model, call_model_engine_fn=None, tokenizer=None, processor=None):
-    out_samples = []
-    with torch.no_grad():
-        for sample in tqdm(samples):
-            if args.small_gpu_usage:
-                sample['image_1'] = sample['image_1'].cuda()
-            response = call_model_engine_fn(args, sample, model, tokenizer, processor)
-            if args.small_gpu_usage:
-                sample['image_1'] = sample['image_1'].cpu()
-
-            out_sample = dict()
-            out_sample['id'] = sample['id']
-            out_sample['type'] = sample['type']
-            out_sample['response'] = response
-            out_samples.append(out_sample)
-    return out_samples
-
-
-def set_seed(seed_value):
-    """
-    Set the seed for PyTorch (both CPU and CUDA), Python, and NumPy for reproducible results.
-
-    :param seed_value: An integer value to be used as the seed.
-    """
-    torch.manual_seed(seed_value)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(seed_value)
-        torch.cuda.manual_seed_all(seed_value)  # For multi-GPU setups
-    random.seed(seed_value)
-    np.random.seed(seed_value)
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = False
-
-
-def main():
-    parser = ArgumentParser()
-    parser.add_argument('--model-path', type=str, default=None)
-    parser.add_argument('--model-base', type=str, default=None)
-    parser.add_argument("--model-type", type=str, default=None)
-    parser.add_argument("--conv-mode", type=str, default=None)
-    parser.add_argument('--data-path', type=str, default=None)
-    parser.add_argument('--config-path', type=str, default=None)
-    parser.add_argument('--output-path', type=str, default=None)
-    parser.add_argument('--split', type=str, default='validation')
-    parser.add_argument('--seed', type=int, default=42)
-    parser.add_argument("--small-gpu-usage", action="store_true")
-
-    args = parser.parse_args()
-    device = torch.device("cuda") if torch.cuda.is_available() else "cpu"
-    set_seed(args.seed)
-
-    print('bunny_initializing...')
-    processor = None
-    call_model_engine = call_bunny_engine_df
-
-    # load config and process to one value
-    args.config = load_yaml(args.config_path)
-    for key, value in args.config.items():
-        if key == 'task_instructions':
-            args.config[key] = value
-        elif key != 'eval_params' and type(value) == list:
-            assert len(value) == 1, 'key {} has more than one value'.format(key)
-            args.config[key] = value[0]
-
-    # run for each subject
-    sub_dataset_list = []
-    for subject in CAT_CN2EN.values():
-        sub_dataset = load_dataset(args.data_path, subject, split=args.split)
-        sub_dataset_list.append(sub_dataset)
-
-    # merge all dataset
-    dataset = concatenate_datasets(sub_dataset_list)
-
-    # load model
-    model_path = os.path.expanduser(args.model_path)
-    model_name = get_model_name_from_path(model_path)
-    tokenizer, model, vis_processors, context_len = load_pretrained_model(model_path, args.model_base, model_name,
-                                                                          args.model_type)
-
-    samples = []
-    print('Processing CMMMU dataset...')
-    for sample in tqdm(dataset):
-
-        sample = construct_prompt(sample, args.config)
-        if sample['image_1']:
-            sample['image_1'] = process_images([sample['image_1'].convert('RGB')], vis_processors, model.config)[0]
-            if not args.small_gpu_usage:
-                sample['image_1'] = sample['image_1'].to(device)
-
-        samples.append(sample)
-
-    print('Start to evaluate...')
-    # run ex
-    out_samples = run_model(args, samples, model, call_model_engine, tokenizer, processor)
-
-    os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
-
-    with open(args.output_path, 'w') as f:
-        for out_sample in out_samples:
-            f.write(json.dumps(out_sample) + '\n')
-
-
-if __name__ == '__main__':
-    main()

bunny/eval/model_vqa_loader.py

@@ -1,143 +0,0 @@
-import argparse
-import torch
-import os
-import json
-from tqdm import tqdm
-import shortuuid
-
-from bunny.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
-from bunny.conversation import conv_templates
-from bunny.model.builder import load_pretrained_model
-from bunny.util.utils import disable_torch_init
-from bunny.util.mm_utils import tokenizer_image_token, process_images, get_model_name_from_path
-from torch.utils.data import Dataset, DataLoader
-
-from PIL import Image
-import math
-
-
-def split_list(lst, n):
-    """Split a list into n (roughly) equal-sized chunks"""
-    chunk_size = math.ceil(len(lst) / n)  # integer division
-    return [lst[i:i + chunk_size] for i in range(0, len(lst), chunk_size)]
-
-
-def get_chunk(lst, n, k):
-    chunks = split_list(lst, n)
-    return chunks[k]
-
-
-# Custom dataset class
-class CustomDataset(Dataset):
-    def __init__(self, questions, image_folder, tokenizer, image_processor, model_config):
-        self.questions = questions
-        self.image_folder = image_folder
-        self.tokenizer = tokenizer
-        self.image_processor = image_processor
-        self.model_config = model_config
-
-    def __getitem__(self, index):
-        line = self.questions[index]
-        image_file = line["image"]
-        qs = line["text"]
-
-        qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
-
-        conv = conv_templates[args.conv_mode].copy()
-        conv.append_message(conv.roles[0], qs)
-        conv.append_message(conv.roles[1], None)
-        prompt = conv.get_prompt()
-
-        image = Image.open(os.path.join(self.image_folder, image_file)).convert('RGB')
-        image_tensor = process_images([image], self.image_processor, self.model_config)[0]
-
-        input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt')
-
-        return input_ids, image_tensor
-
-    def __len__(self):
-        return len(self.questions)
-
-
-# DataLoader
-def create_data_loader(questions, image_folder, tokenizer, image_processor, model_config, batch_size=1, num_workers=4):
-    assert batch_size == 1, "batch_size must be 1"
-    dataset = CustomDataset(questions, image_folder, tokenizer, image_processor, model_config)
-    data_loader = DataLoader(dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False)
-    return data_loader
-
-
-def eval_model(args):
-    # Model
-    disable_torch_init()
-    model_path = os.path.expanduser(args.model_path)
-    model_name = get_model_name_from_path(model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name,
-                                                                           args.model_type)
-
-    questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
-    questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
-    answers_file = os.path.expanduser(args.answers_file)
-    os.makedirs(os.path.dirname(answers_file), exist_ok=True)
-    ans_file = open(answers_file, "w")
-
-    if 'plain' in model_name and 'finetune' not in model_name.lower() and 'mmtag' not in args.conv_mode:
-        args.conv_mode = args.conv_mode + '_mmtag'
-        print(
-            f'It seems that this is a plain model, but it is not using a mmtag prompt, auto switching to {args.conv_mode}.')
-
-    data_loader = create_data_loader(questions, args.image_folder, tokenizer, image_processor, model.config)
-
-    for (input_ids, image_tensor), line in tqdm(zip(data_loader, questions), total=len(questions)):
-        idx = line["question_id"]
-        cur_prompt = line["text"]
-
-        input_ids = input_ids.to(device='cuda', non_blocking=True)
-
-        with torch.inference_mode():
-            output_ids = model.generate(
-                input_ids,
-                images=image_tensor.to(dtype=model.dtype, device='cuda', non_blocking=True),
-                do_sample=True if args.temperature > 0 else False,
-                temperature=args.temperature,
-                top_p=args.top_p,
-                num_beams=args.num_beams,
-                max_new_tokens=args.max_new_tokens,
-                use_cache=True)
-
-        input_token_len = input_ids.shape[1]
-        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-        if n_diff_input_output > 0:
-            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
-        outputs = outputs.strip()
-
-        ans_id = shortuuid.uuid()
-        ans_file.write(json.dumps({"question_id": idx,
-                                   "prompt": cur_prompt,
-                                   "text": outputs,
-                                   "answer_id": ans_id,
-                                   "model_id": model_name,
-                                   "metadata": {}}) + "\n")
-        # ans_file.flush()
-    ans_file.close()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model-path", type=str, default=None)
-    parser.add_argument("--model-base", type=str, default=None)
-    parser.add_argument("--model-type", type=str, default=None)
-    parser.add_argument("--image-folder", type=str, default=None)
-    parser.add_argument("--question-file", type=str, default=None)
-    parser.add_argument("--answers-file", type=str, default=None)
-    parser.add_argument("--conv-mode", type=str, default=None)
-    parser.add_argument("--num-chunks", type=int, default=1)
-    parser.add_argument("--chunk-idx", type=int, default=0)
-    parser.add_argument("--temperature", type=float, default=0.2)
-    parser.add_argument("--top_p", type=float, default=None)
-    parser.add_argument("--num_beams", type=int, default=1)
-    parser.add_argument("--max_new_tokens", type=int, default=128)
-    args = parser.parse_args()
-
-    eval_model(args)

bunny/eval/model_vqa_mmbench.py

@@ -1,167 +0,0 @@
-import argparse
-import torch
-import os
-import json
-import pandas as pd
-from tqdm import tqdm
-import shortuuid
-
-from bunny.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
-from bunny.conversation import conv_templates, SeparatorStyle
-from bunny.model.builder import load_pretrained_model
-from bunny.util.utils import disable_torch_init
-from bunny.util.mm_utils import tokenizer_image_token, process_images, load_image_from_base64, \
-    get_model_name_from_path
-
-import math
-
-all_options = ['A', 'B', 'C', 'D']
-
-
-def split_list(lst, n):
-    """Split a list into n (roughly) equal-sized chunks"""
-    chunk_size = math.ceil(len(lst) / n)  # integer division
-    return [lst[i:i + chunk_size] for i in range(0, len(lst), chunk_size)]
-
-
-def get_chunk(lst, n, k):
-    chunks = split_list(lst, n)
-    return chunks[k]
-
-
-def is_none(value):
-    if value is None:
-        return True
-    if type(value) is float and math.isnan(value):
-        return True
-    if type(value) is str and value.lower() == 'nan':
-        return True
-    if type(value) is str and value.lower() == 'none':
-        return True
-    return False
-
-
-def get_options(row, options):
-    parsed_options = []
-    for option in options:
-        option_value = row[option]
-        if is_none(option_value):
-            break
-        parsed_options.append(option_value)
-    return parsed_options
-
-
-def eval_model(args):
-    # Model
-    disable_torch_init()
-    model_path = os.path.expanduser(args.model_path)
-    model_name = get_model_name_from_path(model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name,
-                                                                           args.model_type)
-
-    questions = pd.read_table(os.path.expanduser(args.question_file))
-    questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
-    answers_file = os.path.expanduser(args.answers_file)
-    os.makedirs(os.path.dirname(answers_file), exist_ok=True)
-    ans_file = open(answers_file, "w")
-
-    for index, row in tqdm(questions.iterrows(), total=len(questions)):
-        options = get_options(row, all_options)
-        cur_option_char = all_options[:len(options)]
-
-        if args.all_rounds:
-            num_rounds = len(options)
-        else:
-            num_rounds = 1
-
-        for round_idx in range(num_rounds):
-            idx = row['index']
-            question = row['question']
-            hint = row['hint']
-            image = load_image_from_base64(row['image'])
-            if not is_none(hint):
-                question = hint + '\n' + question
-            for option_char, option in zip(all_options[:len(options)], options):
-                question = question + '\n' + option_char + '. ' + option
-            qs = cur_prompt = question
-
-            qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
-
-            if args.single_pred_prompt:
-                if args.lang == 'cn':
-                    qs = qs + '\n' + "请直接回答选项字母。"
-                else:
-                    qs = qs + '\n' + "Answer with the option's letter from the given choices directly."
-
-            conv = conv_templates[args.conv_mode].copy()
-            conv.append_message(conv.roles[0], qs)
-            conv.append_message(conv.roles[1], None)
-            prompt = conv.get_prompt()
-
-            input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(
-                0).cuda()
-
-            image_tensor = process_images([image], image_processor, model.config)[0]
-
-            stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
-
-            with torch.inference_mode():
-                output_ids = model.generate(
-                    input_ids,
-                    images=image_tensor.unsqueeze(0).to(dtype=model.dtype, device='cuda', non_blocking=True),
-                    do_sample=True if args.temperature > 0 else False,
-                    temperature=args.temperature,
-                    top_p=args.top_p,
-                    num_beams=args.num_beams,
-                    # no_repeat_ngram_size=3,
-                    max_new_tokens=128,
-                    use_cache=True)
-
-            input_token_len = input_ids.shape[1]
-            n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-            if n_diff_input_output > 0:
-                print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-            outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
-            outputs = outputs.strip()
-            if outputs.endswith(stop_str):
-                outputs = outputs[:-len(stop_str)]
-            outputs = outputs.strip()
-
-            ans_id = shortuuid.uuid()
-            ans_file.write(json.dumps({"question_id": idx,
-                                       "round_id": round_idx,
-                                       "prompt": cur_prompt,
-                                       "text": outputs,
-                                       "options": options,
-                                       "option_char": cur_option_char,
-                                       "answer_id": ans_id,
-                                       "model_id": model_name,
-                                       "metadata": {}}) + "\n")
-            ans_file.flush()
-
-            # rotate options
-            options = options[1:] + options[:1]
-            cur_option_char = cur_option_char[1:] + cur_option_char[:1]
-    ans_file.close()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model-path", type=str, default=None)
-    parser.add_argument("--model-base", type=str, default=None)
-    parser.add_argument("--model-type", type=str, default=None)
-    parser.add_argument("--image-folder", type=str, default=None)
-    parser.add_argument("--question-file", type=str, default=None)
-    parser.add_argument("--answers-file", type=str, default=None)
-    parser.add_argument("--conv-mode", type=str, default=None)
-    parser.add_argument("--num-chunks", type=int, default=1)
-    parser.add_argument("--chunk-idx", type=int, default=0)
-    parser.add_argument("--temperature", type=float, default=0.2)
-    parser.add_argument("--top_p", type=float, default=None)
-    parser.add_argument("--num_beams", type=int, default=1)
-    parser.add_argument("--all-rounds", action="store_true")
-    parser.add_argument("--single-pred-prompt", action="store_true")
-    parser.add_argument("--lang", type=str, default="en")
-    args = parser.parse_args()
-
-    eval_model(args)

bunny/eval/model_vqa_mmmu.py

@@ -1,326 +0,0 @@
-import re
-import random
-import numpy as np
-import os
-import json
-import yaml
-import torch
-
-from tqdm import tqdm
-from datasets import load_dataset, concatenate_datasets
-from argparse import ArgumentParser
-
-from bunny.model.builder import load_pretrained_model
-from bunny.util.mm_utils import get_model_name_from_path, tokenizer_image_token, process_images
-from bunny.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
-from bunny.conversation import conv_templates
-
-CAT_SHORT2LONG = {
-    'acc': 'Accounting',
-    'agri': 'Agriculture',
-    'arch': 'Architecture_and_Engineering',
-    'art': 'Art',
-    'art_theory': 'Art_Theory',
-    'bas_med': 'Basic_Medical_Science',
-    'bio': 'Biology',
-    'chem': 'Chemistry',
-    'cli_med': 'Clinical_Medicine',
-    'cs': 'Computer_Science',
-    'design': 'Design',
-    'diag_med': 'Diagnostics_and_Laboratory_Medicine',
-    'econ': 'Economics',
-    'elec': 'Electronics',
-    'ep': 'Energy_and_Power',
-    'fin': 'Finance',
-    'geo': 'Geography',
-    'his': 'History',
-    'liter': 'Literature',
-    'manage': 'Manage',
-    'mark': 'Marketing',
-    'mate': 'Materials',
-    'math': 'Math',
-    'mech': 'Mechanical_Engineering',
-    'music': 'Music',
-    'phar': 'Pharmacy',
-    'phys': 'Physics',
-    'psy': 'Psychology',
-    'pub_health': 'Public_Health',
-    'socio': 'Sociology'
-}
-
-
-# ----------- Process Multi-choice -------------
-def parse_multi_choice_response(response, all_choices, index2ans):
-    """
-    Parse the prediction from the generated response.
-    Return the predicted index e.g., A, B, C, D.
-    """
-    for char in [',', '.', '!', '?', ';', ':', "'"]:
-        response = response.strip(char)
-    response = " " + response + " "  # add space to avoid partial match
-
-    index_ans = True
-    ans_with_brack = False
-    candidates = []
-    for choice in all_choices:  # e.g., (A) (B) (C) (D)
-        if f'({choice})' in response:
-            candidates.append(choice)
-            ans_with_brack = True
-
-    if len(candidates) == 0:
-        for choice in all_choices:  # e.g., A B C D
-            if f' {choice} ' in response:
-                candidates.append(choice)
-
-    # if all above doesn't get candidates, check if the content is larger than 5 tokens and try to parse the example
-    if len(candidates) == 0 and len(response.split()) > 5:
-        for index, ans in index2ans.items():
-            if ans.lower() in response.lower():
-                candidates.append(index)
-                index_ans = False  # it's content ans.
-
-    if len(candidates) == 0:  # still not get answer, randomly choose one.
-        pred_index = random.choice(all_choices)
-    elif len(candidates) > 1:
-        start_indexes = []
-        if index_ans:
-            if ans_with_brack:
-                for can in candidates:
-                    index = response.rfind(f'({can})')
-                    start_indexes.append(index)  # -1 will be ignored anyway
-                # start_indexes = [generated_response.index(f'({can})') for can in candidates]
-            else:
-                for can in candidates:
-                    index = response.rfind(f" {can} ")
-                    start_indexes.append(index)
-        else:
-            for can in candidates:
-                index = response.lower().rfind(index2ans[can].lower())
-                start_indexes.append(index)
-        # get the last one
-        pred_index = candidates[np.argmax(start_indexes)]
-    else:  # if only one candidate, use it.
-        pred_index = candidates[0]
-
-    return pred_index
-
-
-def call_bunny_engine_df(args, sample, model, tokenizer=None, processor=None):
-    def deal_with_prompt(input_text):
-        qs = input_text
-        qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
-        return qs
-
-    prompt = sample['final_input_prompt']
-    prompt = deal_with_prompt(prompt)
-
-    conv = conv_templates[args.conv_mode].copy()
-    conv.append_message(conv.roles[0], prompt)
-    conv.append_message(conv.roles[1], None)
-    prompt = conv.get_prompt()
-
-    input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
-
-    image = sample['image']
-    if image is not None:
-        output_ids = model.generate(
-            input_ids,
-            images=image.unsqueeze(0).to(dtype=model.dtype, device='cuda', non_blocking=True),
-            do_sample=False,
-            temperature=0,
-            top_p=None,
-            # num_beams=5,
-            max_new_tokens=128,
-            use_cache=True)
-
-        input_token_len = input_ids.shape[1]
-        # n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-        # if n_diff_input_output > 0:
-        #     print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-        response = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
-    else:  # multiple images actually
-        if sample['question_type'] == 'multiple-choice':
-            all_choices = sample['all_choices']
-            response = random.choice(all_choices)
-        else:
-            response = 'INVALID GENERATION FOR MULTIPLE IMAGE INPUTS'
-
-    return response
-
-
-def load_yaml(file_path):
-    with open(file_path, 'r') as stream:
-        try:
-            yaml_dict = yaml.safe_load(stream)
-        except yaml.YAMLError as exc:
-            print(exc)
-
-    return yaml_dict
-
-
-def parse_img_path(text):
-    matches = re.findall("<img='(.*?)'>", text)
-    return matches
-
-
-def process_single_sample(data):
-    question = data['question']
-    o_imgs_paths = []
-    for option in data['options']:
-        current_o_imgs_paths = parse_img_path(option)
-        for img_path in current_o_imgs_paths:
-            o_imgs_paths.append(img_path)
-
-    if len(o_imgs_paths) > 1:  # multiple images in options, used for random selection
-        return {'id': data['id'], 'question': question, 'options': data['options'], 'answer': data['answer'],
-                'image': None, 'question_type': data['question_type']}
-    else:
-        return {'id': data['id'], 'question': question, 'options': data['options'], 'answer': data['answer'],
-                'image': data['image_1'], 'question_type': data['question_type']}
-
-
-# DATA PROCESSING
-def construct_prompt(sample, config):
-    question = sample['question']
-    options = eval(sample['options'])
-    example = ""
-    if sample['question_type'] == 'multiple-choice':
-        start_chr = 'A'
-        prediction_range = []
-        index2ans = {}
-        for option in options:
-            prediction_range.append(start_chr)
-            example += f"({start_chr}) {option}\n"
-            index2ans[start_chr] = option
-            start_chr = chr(ord(start_chr) + 1)
-        empty_prompt_sample_structure = config['multi_choice_example_format']
-        empty_prompt = empty_prompt_sample_structure.format(question, example)
-        res_dict = {}
-        res_dict['index2ans'] = index2ans
-        res_dict['correct_choice'] = sample['answer']
-        res_dict['all_choices'] = prediction_range
-        res_dict['empty_prompt'] = empty_prompt
-        if config['task_instructions']:
-            res_dict['final_input_prompt'] = config['task_instructions'].strip() + '\n\n' + empty_prompt
-        else:
-            res_dict['final_input_prompt'] = empty_prompt
-
-        res_dict['gt_content'] = options[ord(sample['answer'].upper()) - ord('A')]
-    else:
-        empty_prompt_sample_structure = config['short_ans_example_format']
-        empty_prompt = empty_prompt_sample_structure.format(question)
-        res_dict = {}
-        res_dict['empty_prompt'] = empty_prompt
-        if config['task_instructions']:
-            res_dict['final_input_prompt'] = config['task_instructions'].strip() + '\n\n' + empty_prompt
-        else:
-            res_dict['final_input_prompt'] = empty_prompt
-        res_dict['gt_content'] = sample['answer']
-
-    res_dict.update(sample)
-    return res_dict
-
-
-def run_model(args, samples, model, call_model_engine_fn=None, tokenizer=None, processor=None):
-    out_samples = dict()
-    with torch.no_grad():
-        for sample in tqdm(samples):
-            if args.small_gpu_usage:
-                sample['image'] = sample['image'].cuda()
-            response = call_model_engine_fn(args, sample, model, tokenizer, processor)
-            if args.small_gpu_usage:
-                sample['image'] = sample['image'].cpu()
-
-            if sample['question_type'] == 'multiple-choice':
-                pred_ans = parse_multi_choice_response(response, sample['all_choices'], sample['index2ans'])
-            else:  # open question
-                pred_ans = response
-            out_samples[sample['id']] = pred_ans
-    return out_samples
-
-
-def set_seed(seed_value):
-    """
-    Set the seed for PyTorch (both CPU and CUDA), Python, and NumPy for reproducible results.
-
-    :param seed_value: An integer value to be used as the seed.
-    """
-    torch.manual_seed(seed_value)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(seed_value)
-        torch.cuda.manual_seed_all(seed_value)  # For multi-GPU setups
-    random.seed(seed_value)
-    np.random.seed(seed_value)
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = False
-
-
-def main():
-    parser = ArgumentParser()
-    parser.add_argument('--model-path', type=str, default=None)
-    parser.add_argument('--model-base', type=str, default=None)
-    parser.add_argument("--model-type", type=str, default=None)
-    parser.add_argument("--conv-mode", type=str, default=None)
-    parser.add_argument('--data-path', type=str, default=None)
-    parser.add_argument('--config-path', type=str, default=None)
-    parser.add_argument('--output-path', type=str, default=None)
-    parser.add_argument('--split', type=str, default='validation')
-    parser.add_argument('--seed', type=int, default=42)
-    parser.add_argument("--small-gpu-usage", action="store_true")
-
-    args = parser.parse_args()
-    device = torch.device("cuda") if torch.cuda.is_available() else "cpu"
-    set_seed(args.seed)
-
-    print('bunny_initializing...')
-    processor = None
-    call_model_engine = call_bunny_engine_df
-
-    # load config and process to one value
-    args.config = load_yaml(args.config_path)
-    for key, value in args.config.items():
-        if key != 'eval_params' and type(value) == list:
-            assert len(value) == 1, 'key {} has more than one value'.format(key)
-            args.config[key] = value[0]
-
-    # run for each subject
-    sub_dataset_list = []
-    for subject in CAT_SHORT2LONG.values():
-        sub_dataset = load_dataset(args.data_path, subject, split=args.split)
-        sub_dataset_list.append(sub_dataset)
-
-    # merge all dataset
-    dataset = concatenate_datasets(sub_dataset_list)
-
-    # load model
-    model_path = os.path.expanduser(args.model_path)
-    model_name = get_model_name_from_path(model_path)
-    tokenizer, model, vis_processors, context_len = load_pretrained_model(model_path, args.model_base, model_name,
-                                                                          args.model_type)
-
-    samples = []
-    print('Processing MMMU dataset...')
-    for sample in tqdm(dataset):
-        sample = process_single_sample(sample)
-
-        sample = construct_prompt(sample, args.config)
-        if sample['image']:
-            sample['image'] = process_images([sample['image'].convert('RGB')], vis_processors, model.config)[0]
-
-            if not args.small_gpu_usage:
-                sample['image'] = sample['image'].to(device)
-
-        samples.append(sample)
-
-    print('Start to evaluate...')
-    # run ex
-    out_samples = run_model(args, samples, model, call_model_engine, tokenizer, processor)
-
-    os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
-
-    with open(args.output_path, 'w') as f:
-        json.dump(out_samples, f, indent=4)
-
-
-if __name__ == '__main__':
-    main()

bunny/eval/model_vqa_science.py

@@ -1,119 +0,0 @@
-import argparse
-import torch
-import os
-import json
-from tqdm import tqdm
-import shortuuid
-
-from bunny.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
-from bunny.conversation import conv_templates, SeparatorStyle
-from bunny.model.builder import load_pretrained_model
-from bunny.util.utils import disable_torch_init
-from bunny.util.mm_utils import tokenizer_image_token, get_model_name_from_path
-
-from PIL import Image
-import math
-
-
-def split_list(lst, n):
-    """Split a list into n (roughly) equal-sized chunks"""
-    chunk_size = math.ceil(len(lst) / n)  # integer division
-    return [lst[i:i + chunk_size] for i in range(0, len(lst), chunk_size)]
-
-
-def get_chunk(lst, n, k):
-    chunks = split_list(lst, n)
-    return chunks[k]
-
-
-def eval_model(args):
-    # Model
-    disable_torch_init()
-    model_path = os.path.expanduser(args.model_path)
-    model_name = get_model_name_from_path(model_path)
-    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name,
-                                                                           args.model_type)
-
-    questions = json.load(open(os.path.expanduser(args.question_file), "r"))
-    questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
-    answers_file = os.path.expanduser(args.answers_file)
-    os.makedirs(os.path.dirname(answers_file), exist_ok=True)
-    ans_file = open(answers_file, "w")
-    for i, line in enumerate(tqdm(questions)):
-        idx = line["id"]
-        question = line['conversations'][0]
-        qs = question['value'].replace('<image>', '').strip()
-        cur_prompt = qs
-
-        if 'image' in line:
-            image_file = line["image"]
-            image = Image.open(os.path.join(args.image_folder, image_file))
-            image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
-            images = image_tensor.unsqueeze(0).to(dtype=model.dtype, device='cuda', non_blocking=True)
-
-            qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
-            cur_prompt = '<image>' + '\n' + cur_prompt
-        else:
-            images = None
-
-        if args.single_pred_prompt:
-            qs = qs + '\n' + "Answer with the option's letter from the given choices directly."
-            cur_prompt = cur_prompt + '\n' + "Answer with the option's letter from the given choices directly."
-
-        conv = conv_templates[args.conv_mode].copy()
-        conv.append_message(conv.roles[0], qs)
-        conv.append_message(conv.roles[1], None)
-        prompt = conv.get_prompt()
-
-        input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
-
-        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
-
-        with torch.inference_mode():
-            output_ids = model.generate(
-                input_ids,
-                images=images,
-                do_sample=True if args.temperature > 0 else False,
-                temperature=args.temperature,
-                max_new_tokens=1024,
-                use_cache=True
-            )
-
-        input_token_len = input_ids.shape[1]
-        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
-        if n_diff_input_output > 0:
-            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
-        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
-        outputs = outputs.strip()
-        if outputs.endswith(stop_str):
-            outputs = outputs[:-len(stop_str)]
-        outputs = outputs.strip()
-
-        ans_id = shortuuid.uuid()
-        ans_file.write(json.dumps({"question_id": idx,
-                                   "prompt": cur_prompt,
-                                   "text": outputs,
-                                   "answer_id": ans_id,
-                                   "model_id": model_name,
-                                   "metadata": {}}) + "\n")
-        ans_file.flush()
-    ans_file.close()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model-path", type=str, default=None)
-    parser.add_argument("--model-base", type=str, default=None)
-    parser.add_argument("--model-type", type=str, default=None)
-    parser.add_argument("--image-folder", type=str, default=None)
-    parser.add_argument("--question-file", type=str, default=None)
-    parser.add_argument("--answers-file", type=str, default=None)
-    parser.add_argument("--conv-mode", type=str, default=None)
-    parser.add_argument("--num-chunks", type=int, default=1)
-    parser.add_argument("--chunk-idx", type=int, default=0)
-    parser.add_argument("--temperature", type=float, default=0.2)
-    parser.add_argument("--single-pred-prompt", action="store_true")
-
-    args = parser.parse_args()
-
-    eval_model(args)

bunny/model/__init__.py

@@ -1,6 +0,0 @@
-from .language_model.bunny_phi import BunnyPhiForCausalLM, BunnyPhiConfig
-from .language_model.bunny_stablelm import BunnyStableLMForCausalLM, BunnyStableLMConfig
-from .language_model.bunny_qwen import BunnyQwen2ForCausalLM, BunnyQwen2Config
-from .language_model.bunny_minicpm import BunnyMiniCPMForCausalLM, BunnyMiniCPMConfig
-from .language_model.bunny_llama import BunnyLlamaForCausalLM, BunnyLlamaConfig
-from .language_model.bunny_phi3 import BunnyPhi3ForCausalLM, BunnyPhi3Config

bunny/model/builder.py

@@ -1,197 +0,0 @@
-import os
-import warnings
-import torch
-
-from transformers import AutoTokenizer, AutoConfig, BitsAndBytesConfig, logging
-
-logging.set_verbosity_error()
-warnings.filterwarnings('ignore')
-
-from bunny.model import *
-
-
-def load_pretrained_model(model_path, model_base, model_name, model_type, load_8bit=False, load_4bit=False,
-                          device_map="auto", device="cuda", **kwargs):
-    if model_type not in {'phi-1.5', 'phi-2', 'phi-3', 'stablelm-2', 'qwen1.5-1.8b', 'minicpm', 'llama3-8b'}:
-        raise ValueError(f"Unknown Model Type {model_type}")
-
-    kwargs = {"device_map": device_map, **kwargs}
-
-    if device != "cuda":
-        kwargs['device_map'] = {"": device}
-
-    if load_8bit:
-        kwargs['load_in_8bit'] = True
-    elif load_4bit:
-        kwargs['load_in_4bit'] = True
-        kwargs['quantization_config'] = BitsAndBytesConfig(
-            load_in_4bit=True,
-            bnb_4bit_compute_dtype=torch.float16,
-            bnb_4bit_use_double_quant=True,
-            bnb_4bit_quant_type='nf4'
-        )
-    else:
-        kwargs['torch_dtype'] = torch.float16
-
-    # Load Bunny model
-    if 'lora' in model_name.lower() and model_base is None:
-        warnings.warn(
-            'There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument.')
-    if 'lora' in model_name.lower() and model_base is not None:
-        lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
-
-        print('Loading Bunny from base model...')
-        if model_type == 'phi-1.5' or model_type == 'phi-2':
-            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
-            model = BunnyPhiForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
-                                                        config=lora_cfg_pretrained, **kwargs)
-        elif model_type == 'phi-3':
-            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
-            model = BunnyPhi3ForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
-                                                         config=lora_cfg_pretrained, **kwargs)
-        elif model_type == 'stablelm-2':
-            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True, trust_remote_code=True)
-            model = BunnyStableLMForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
-                                                             config=lora_cfg_pretrained, **kwargs)
-        elif model_type == 'qwen1.5-1.8b':
-            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
-            model = BunnyQwen2ForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained,
-                                                          **kwargs)
-        elif model_type == 'minicpm':
-            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
-            model = BunnyMiniCPMForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
-                                                            config=lora_cfg_pretrained,
-                                                            **kwargs)
-        elif model_type == 'llama3-8b':
-            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
-            model = BunnyLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
-                                                          config=lora_cfg_pretrained,
-                                                          **kwargs)
-
-        token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
-        if model.lm_head.weight.shape[0] != token_num:
-            model.lm_head.weight = torch.nn.Parameter(
-                torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
-            model.model.embed_tokens.weight = torch.nn.Parameter(
-                torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
-
-        print('Loading additional Bunny weights...')
-        if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')):
-            non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), map_location='cpu')
-        else:
-            # this is probably from HF Hub
-            from huggingface_hub import hf_hub_download
-            def load_from_hf(repo_id, filename, subfolder=None):
-                cache_file = hf_hub_download(
-                    repo_id=repo_id,
-                    filename=filename,
-                    subfolder=subfolder)
-                return torch.load(cache_file, map_location='cpu')
-
-            non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin')
-
-        non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in
-                               non_lora_trainables.items()}
-        if any(k.startswith('model.model.') for k in non_lora_trainables):
-            non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in
-                                   non_lora_trainables.items()}
-        model.load_state_dict(non_lora_trainables, strict=False)
-
-        from peft import PeftModel
-        print('Loading LoRA weights...')
-        model = PeftModel.from_pretrained(model, model_path)
-        print('Merging LoRA weights...')
-        model = model.merge_and_unload()
-        print('Model is loaded...')
-    elif model_base is not None:
-        # this may be mm projector only
-        print('Loading Bunny from base model...')
-
-        cfg_pretrained = AutoConfig.from_pretrained(model_path)
-        if model_type == 'phi-1.5' or model_type == 'phi-2':
-            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
-            model = BunnyPhiForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
-                                                        config=cfg_pretrained, **kwargs)
-        elif model_type == 'phi-3':
-            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
-            model = BunnyPhi3ForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
-                                                         config=cfg_pretrained, **kwargs)
-        elif model_type == 'stablelm-2':
-            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True, trust_remote_code=True)
-            model = BunnyStableLMForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,
-                                                             config=cfg_pretrained, **kwargs)
-        elif model_type == 'qwen1.5-1.8b':
-            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
-            model = BunnyQwen2ForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained,
-                                                          **kwargs)
-        elif model_type == 'minicpm':
-            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
-            model = BunnyMiniCPMForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained,
-                                                            **kwargs)
-        elif model_type == 'llama3-8b':
-            tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
-            model = BunnyLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained,
-                                                          **kwargs)
-
-        mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
-        mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
-        model.load_state_dict(mm_projector_weights, strict=False)
-    else:
-        if model_type == 'phi-1.5' or model_type == 'phi-2':
-            tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
-            model = BunnyPhiForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
-        elif model_type == 'phi-3':
-            tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
-            model = BunnyPhi3ForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
-        elif model_type == 'stablelm-2':
-            tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True, trust_remote_code=True)
-            model = BunnyStableLMForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
-        elif model_type == 'qwen1.5-1.8b':
-            tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
-            model = BunnyQwen2ForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
-        elif model_type == 'minicpm':
-            tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
-            model = BunnyMiniCPMForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
-        elif model_type == 'llama3-8b':
-            tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
-            model = BunnyLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
-
-    model.resize_token_embeddings(len(tokenizer))
-
-    vision_tower = model.get_vision_tower()
-    if not vision_tower.is_loaded:
-        vision_tower.load_model()
-
-    # if getattr(model.config, "unfreeze_vision_tower", False):
-    #     if 'lora' in model_name.lower():
-    #         assert model_base is not None
-    #         vision_non_lora_trainables = {k[19:]: v for k, v in non_lora_trainables.items() if
-    #                                       k.startswith('model.vision_tower.')}
-    #         vision_tower.load_state_dict(vision_non_lora_trainables, strict=False)
-    #     else:
-    #         assert model_base is None
-    #         from safetensors.torch import load_file
-    #         vision_weights = {}
-    #         for file_name in os.listdir(model_path):
-    #             if file_name.endswith('safetensors'):
-    #                 vision_weights.update(
-    #                     {k[19:]: v for k, v in load_file(os.path.join(model_path, file_name)).items() if
-    #                      k.startswith('model.vision_tower.')})
-    #         vision_tower.load_state_dict(vision_weights, strict=True)
-
-    vision_tower.to(device=device, dtype=torch.float16)
-    image_processor = vision_tower.image_processor
-
-    if hasattr(model.config, "max_sequence_length"):
-        context_len = model.config.max_sequence_length
-    else:
-        context_len = 2048
-
-    if model_type == 'llama3-8b':
-        tokenizer.eos_token_id = 128001
-        model.generation_config.pad_token_id = tokenizer.eos_token_id
-
-    if model.generation_config.pad_token_id is None:
-        model.generation_config.pad_token_id = model.generation_config.eos_token_id
-
-    return tokenizer, model, image_processor, context_len

bunny/model/bunny_arch.py

@@ -1,230 +0,0 @@
-from abc import ABC, abstractmethod
-
-import torch
-
-from .multimodal_encoder.builder import build_vision_tower
-from .multimodal_projector.builder import build_vision_projector
-
-from bunny.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX
-
-
-class BunnyMetaModel:
-
-    def __init__(self, config):
-        super(BunnyMetaModel, self).__init__(config)
-
-        if hasattr(config, "mm_vision_tower"):
-            self.vision_tower = build_vision_tower(config, delay_load=False)
-            # self.vision_tower = build_vision_tower(config, delay_load=not getattr(config, 'continuous_training', False))
-            if getattr(config, 'continuous_training', False):
-                config.continuous_training = False
-            self.mm_projector = build_vision_projector(config)
-
-    def get_vision_tower(self):
-        vision_tower = getattr(self, 'vision_tower', None)
-        if type(vision_tower) is list:
-            vision_tower = vision_tower[0]
-        return vision_tower
-
-    def initialize_vision_modules(self, model_args):
-        vision_tower = model_args.vision_tower
-
-        pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
-
-        self.config.mm_vision_tower = vision_tower
-
-        if self.get_vision_tower() is None:
-            vision_tower = build_vision_tower(model_args)
-            self.vision_tower = vision_tower
-        else:
-            vision_tower = self.vision_tower
-            vision_tower.load_model()
-
-        self.config.use_mm_proj = True
-        self.config.mm_projector_type = getattr(model_args, 'mm_projector_type')
-        self.config.mm_hidden_size = vision_tower.hidden_size
-
-        if getattr(self, 'mm_projector', None) is None:
-            self.mm_projector = build_vision_projector(self.config)
-        else:
-            # In case it is frozen by LoRA
-            for p in self.mm_projector.parameters():
-                p.requires_grad = True
-
-        if pretrain_mm_mlp_adapter is not None:
-            mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
-
-            def get_w(weights, keyword):
-                return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
-
-            self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
-
-
-class BunnyMetaForCausalLM(ABC):
-
-    @abstractmethod
-    def get_model(self):
-        pass
-
-    def get_vision_tower(self):
-        return self.get_model().get_vision_tower()
-
-    def encode_images(self, images):
-        image_features = self.get_model().get_vision_tower()(images)
-        image_features = self.get_model().mm_projector(image_features)
-        return image_features
-
-    def prepare_inputs_labels_for_multimodal(
-            self, input_ids, position_ids, attention_mask, past_key_values, labels, images
-    ):
-        vision_tower = self.get_vision_tower()
-        if vision_tower is None or images is None or input_ids.shape[1] == 1:
-            if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[
-                1] == 1:
-                target_shape = past_key_values[-1][-1].shape[-2] + 1
-                attention_mask = torch.cat((attention_mask, torch.ones(
-                    (attention_mask.shape[0], target_shape - attention_mask.shape[1]),
-                    dtype=attention_mask.dtype,
-                    device=attention_mask.device
-                )), dim=1)
-                position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
-            return input_ids, position_ids, attention_mask, past_key_values, None, labels
-
-        if type(images) is list or images.ndim == 5:
-            concat_images = torch.cat([image for image in images], dim=0)
-            image_features = self.encode_images(concat_images)
-            split_sizes = [image.shape[0] for image in images]
-            image_features = torch.split(image_features, split_sizes, dim=0)
-            image_features = [x.flatten(0, 1).to(self.device) for x in image_features]
-        else:
-            image_features = self.encode_images(images).to(self.device)
-
-        # Let's just add dummy tensors if they do not exist,
-        # it is a headache to deal with None all the time.
-        # But it is not ideal, and if you have a better idea,
-        # please open an issue / submit a PR, thanks.
-        _labels = labels
-        _position_ids = position_ids
-        _attention_mask = attention_mask
-        if attention_mask is None:
-            attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
-        else:
-            attention_mask = attention_mask.bool()
-        if position_ids is None:
-            position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
-        if labels is None:
-            labels = torch.full_like(input_ids, IGNORE_INDEX)
-
-        input_ids_temp = input_ids  # points to the actual input_ids tensor
-
-        # remove the padding using attention_mask -- TODO: double check
-        input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in
-                     zip(input_ids, attention_mask)]
-        labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
-
-        # -- TODO: better implementation?
-        # replace IMAGE_TOKEN_INDEX(-200) with 0 to be compatible with repetition penalty
-        input_ids_temp[input_ids_temp == IMAGE_TOKEN_INDEX] = 0
-
-        new_input_embeds = []
-        new_labels = []
-        cur_image_idx = 0
-        for batch_idx, cur_input_ids in enumerate(input_ids):
-            num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
-            if num_images == 0:
-                cur_image_features = image_features[cur_image_idx]
-                cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
-                cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
-                new_input_embeds.append(cur_input_embeds)
-                new_labels.append(labels[batch_idx])
-                cur_image_idx += 1
-                continue
-
-            image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [
-                cur_input_ids.shape[0]]
-            cur_input_ids_noim = []
-            cur_labels = labels[batch_idx]
-            cur_labels_noim = []
-            for i in range(len(image_token_indices) - 1):
-                cur_input_ids_noim.append(cur_input_ids[image_token_indices[i] + 1:image_token_indices[i + 1]])
-                cur_labels_noim.append(cur_labels[image_token_indices[i] + 1:image_token_indices[i + 1]])
-            split_sizes = [x.shape[0] for x in cur_labels_noim]
-            cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
-            cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
-            cur_new_input_embeds = []
-            cur_new_labels = []
-
-            for i in range(num_images + 1):
-                cur_new_input_embeds.append(cur_input_embeds_no_im[i])
-                cur_new_labels.append(cur_labels_noim[i])
-                if i < num_images:
-                    cur_image_features = image_features[cur_image_idx]
-                    cur_image_idx += 1
-                    cur_new_input_embeds.append(cur_image_features)
-                    cur_new_labels.append(
-                        torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device,
-                                   dtype=cur_labels.dtype))
-
-            cur_new_input_embeds = torch.cat(cur_new_input_embeds)
-            cur_new_labels = torch.cat(cur_new_labels)
-
-            new_input_embeds.append(cur_new_input_embeds)
-            new_labels.append(cur_new_labels)
-
-        # Truncate sequences to max length as image embeddings can make the sequence longer
-        tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None)
-        if tokenizer_model_max_length is not None:
-            new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
-            new_labels = [x[:tokenizer_model_max_length] for x in new_labels]
-
-        # Combine them
-        max_len = max(x.shape[0] for x in new_input_embeds)
-        batch_size = len(new_input_embeds)
-
-        new_input_embeds_padded = []
-        new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype,
-                                       device=new_labels[0].device)
-        attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
-        position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)
-
-        for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
-            cur_len = cur_new_embed.shape[0]
-            if getattr(self.config, 'tokenizer_padding_side', 'right') == "left":
-                new_input_embeds_padded.append(torch.cat((
-                    torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype,
-                                device=cur_new_embed.device),
-                    cur_new_embed
-                ), dim=0))
-                if cur_len > 0:
-                    new_labels_padded[i, -cur_len:] = cur_new_labels
-                    attention_mask[i, -cur_len:] = True
-                    position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype,
-                                                              device=position_ids.device)
-            else:
-                new_input_embeds_padded.append(torch.cat((
-                    cur_new_embed,
-                    torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype,
-                                device=cur_new_embed.device)
-                ), dim=0))
-                if cur_len > 0:
-                    new_labels_padded[i, :cur_len] = cur_new_labels
-                    attention_mask[i, :cur_len] = True
-                    position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype,
-                                                             device=position_ids.device)
-
-        new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
-
-        if _labels is None:
-            new_labels = None
-        else:
-            new_labels = new_labels_padded
-
-        if _attention_mask is None:
-            attention_mask = None
-        else:
-            attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
-
-        if _position_ids is None:
-            position_ids = None
-
-        return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels

bunny/model/language_model/bunny_llama.py

@@ -1,102 +0,0 @@
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-from transformers import AutoConfig, AutoModelForCausalLM
-
-from .llama import LlamaModel, LlamaConfig, LlamaForCausalLM
-
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-from ..bunny_arch import BunnyMetaModel, BunnyMetaForCausalLM
-
-
-class BunnyLlamaConfig(LlamaConfig):
-    model_type = "bunny-llama"
-
-
-class BunnyLlamaModel(BunnyMetaModel, LlamaModel):
-    config_class = BunnyLlamaConfig
-
-    def __init__(self, config: LlamaConfig):
-        super(BunnyLlamaModel, self).__init__(config)
-
-
-class BunnyLlamaForCausalLM(LlamaForCausalLM, BunnyMetaForCausalLM):
-    config_class = BunnyLlamaConfig
-
-    def __init__(self, config):
-        super(LlamaForCausalLM, self).__init__(config)
-        self.model = BunnyLlamaModel(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_model(self):
-        return self.model
-
-    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,
-            images: Optional[torch.FloatTensor] = None,
-            return_dict: Optional[bool] = None,
-            cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        if inputs_embeds is None:
-            (
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                inputs_embeds,
-                labels
-            ) = self.prepare_inputs_labels_for_multimodal(
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                labels,
-                images
-            )
-
-        return super().forward(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            labels=labels,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            cache_position=None
-        )
-
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, attention_mask=None,
-                                      **kwargs):
-        images = kwargs.pop("images", None)
-
-        _inputs = super().prepare_inputs_for_generation(
-            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask,
-            **kwargs
-        )
-
-        if images is not None:
-            _inputs['images'] = images
-
-        return _inputs
-
-
-AutoConfig.register("bunny-llama", BunnyLlamaConfig)
-AutoModelForCausalLM.register(BunnyLlamaConfig, BunnyLlamaForCausalLM)

bunny/model/language_model/bunny_minicpm.py

@@ -1,103 +0,0 @@
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-from transformers import AutoConfig, AutoModelForCausalLM
-
-from bunny.model.language_model.minicpm.modeling_minicpm import MiniCPMModel, MiniCPMForCausalLM
-from bunny.model.language_model.minicpm.configuration_minicpm import MiniCPMConfig
-
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-from ..bunny_arch import BunnyMetaModel, BunnyMetaForCausalLM
-
-
-class BunnyMiniCPMConfig(MiniCPMConfig):
-    model_type = "bunny-minicpm"
-
-
-class BunnyMiniCPMModel(BunnyMetaModel, MiniCPMModel):
-    config_class = BunnyMiniCPMConfig
-
-    def __init__(self, config: MiniCPMConfig):
-        super(BunnyMiniCPMModel, self).__init__(config)
-
-
-class BunnyMiniCPMForCausalLM(MiniCPMForCausalLM, BunnyMetaForCausalLM):
-    config_class = BunnyMiniCPMConfig
-
-    def __init__(self, config):
-        super(MiniCPMForCausalLM, self).__init__(config)
-        self.model = BunnyMiniCPMModel(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_model(self):
-        return self.model
-
-    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,
-            images: Optional[torch.FloatTensor] = None,
-            return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-
-        if inputs_embeds is None:
-            (
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                inputs_embeds,
-                labels
-            ) = self.prepare_inputs_labels_for_multimodal(
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                labels,
-                images
-            )
-            if inputs_embeds is not None:
-                inputs_embeds *= self.get_model().config.scale_emb
-
-        return super().forward(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            labels=labels,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict
-        )
-
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, attention_mask=None,
-                                      **kwargs):
-        images = kwargs.pop("images", None)
-
-        _inputs = super().prepare_inputs_for_generation(
-            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask,
-            **kwargs
-        )
-
-        if images is not None:
-            _inputs['images'] = images
-        return _inputs
-
-
-AutoConfig.register("bunny-minicpm", BunnyMiniCPMConfig)
-AutoModelForCausalLM.register(BunnyMiniCPMConfig, BunnyMiniCPMForCausalLM)

bunny/model/language_model/bunny_phi.py

@@ -1,100 +0,0 @@
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-from transformers import AutoConfig, AutoModelForCausalLM
-
-from .phi import PhiModel, PhiConfig, PhiForCausalLM
-
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-from ..bunny_arch import BunnyMetaModel, BunnyMetaForCausalLM
-
-
-class BunnyPhiConfig(PhiConfig):
-    model_type = "bunny-phi"
-
-
-class BunnyPhiModel(BunnyMetaModel, PhiModel):
-    config_class = BunnyPhiConfig
-
-    def __init__(self, config: PhiConfig):
-        super(BunnyPhiModel, self).__init__(config)
-
-
-class BunnyPhiForCausalLM(PhiForCausalLM, BunnyMetaForCausalLM):
-    config_class = BunnyPhiConfig
-
-    def __init__(self, config):
-        super(PhiForCausalLM, self).__init__(config)
-        self.model = BunnyPhiModel(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_model(self):
-        return self.model
-
-    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,
-            images: Optional[torch.FloatTensor] = None,
-            return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-
-        if inputs_embeds is None:
-            (
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                inputs_embeds,
-                labels
-            ) = self.prepare_inputs_labels_for_multimodal(
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                labels,
-                images
-            )
-
-        return super().forward(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            labels=labels,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict
-        )
-
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, attention_mask=None,
-                                      **kwargs):
-        images = kwargs.pop("images", None)
-
-        _inputs = super().prepare_inputs_for_generation(
-            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask,
-            **kwargs
-        )
-
-        if images is not None:
-            _inputs['images'] = images
-        return _inputs
-
-
-AutoConfig.register("bunny-phi", BunnyPhiConfig)
-AutoModelForCausalLM.register(BunnyPhiConfig, BunnyPhiForCausalLM)

bunny/model/language_model/bunny_phi3.py

@@ -1,100 +0,0 @@
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-from transformers import AutoConfig, AutoModelForCausalLM
-
-from .phi3 import Phi3Model, Phi3Config, Phi3ForCausalLM
-
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-from ..bunny_arch import BunnyMetaModel, BunnyMetaForCausalLM
-
-
-class BunnyPhi3Config(Phi3Config):
-    model_type = "bunny-phi3"
-
-
-class BunnyPhi3Model(BunnyMetaModel, Phi3Model):
-    config_class = BunnyPhi3Config
-
-    def __init__(self, config: Phi3Config):
-        super(BunnyPhi3Model, self).__init__(config)
-
-
-class BunnyPhi3ForCausalLM(Phi3ForCausalLM, BunnyMetaForCausalLM):
-    config_class = BunnyPhi3Config
-
-    def __init__(self, config):
-        super(Phi3ForCausalLM, self).__init__(config)
-        self.model = BunnyPhi3Model(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_model(self):
-        return self.model
-
-    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,
-            images: Optional[torch.FloatTensor] = None,
-            return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-
-        if inputs_embeds is None:
-            (
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                inputs_embeds,
-                labels
-            ) = self.prepare_inputs_labels_for_multimodal(
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                labels,
-                images
-            )
-
-        return super().forward(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            labels=labels,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict
-        )
-
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, attention_mask=None,
-                                      **kwargs):
-        images = kwargs.pop("images", None)
-
-        _inputs = super().prepare_inputs_for_generation(
-            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask,
-            **kwargs
-        )
-
-        if images is not None:
-            _inputs['images'] = images
-        return _inputs
-
-
-AutoConfig.register("bunny-phi3", BunnyPhi3Config)
-AutoModelForCausalLM.register(BunnyPhi3Config, BunnyPhi3ForCausalLM)

bunny/model/language_model/bunny_qwen.py

@@ -1,100 +0,0 @@
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-from transformers import AutoConfig, AutoModelForCausalLM
-
-from .qwen2 import Qwen2Model, Qwen2Config, Qwen2ForCausalLM
-
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-from ..bunny_arch import BunnyMetaModel, BunnyMetaForCausalLM
-
-
-class BunnyQwen2Config(Qwen2Config):
-    model_type = "bunny-qwen2"
-
-
-class BunnyQwen2Model(BunnyMetaModel, Qwen2Model):
-    config_class = BunnyQwen2Config
-
-    def __init__(self, config: Qwen2Config):
-        super(BunnyQwen2Model, self).__init__(config)
-
-
-class BunnyQwen2ForCausalLM(Qwen2ForCausalLM, BunnyMetaForCausalLM):
-    config_class = BunnyQwen2Config
-
-    def __init__(self, config):
-        super(Qwen2ForCausalLM, self).__init__(config)
-        self.model = BunnyQwen2Model(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_model(self):
-        return self.model
-
-    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,
-            images: Optional[torch.FloatTensor] = None,
-            return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-
-        if inputs_embeds is None:
-            (
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                inputs_embeds,
-                labels
-            ) = self.prepare_inputs_labels_for_multimodal(
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                labels,
-                images
-            )
-
-        return super().forward(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            labels=labels,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict
-        )
-
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, attention_mask=None,
-                                      **kwargs):
-        images = kwargs.pop("images", None)
-
-        _inputs = super().prepare_inputs_for_generation(
-            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask,
-            **kwargs
-        )
-
-        if images is not None:
-            _inputs['images'] = images
-        return _inputs
-
-
-AutoConfig.register("bunny-qwen2", BunnyQwen2Config)
-AutoModelForCausalLM.register(BunnyQwen2Config, BunnyQwen2ForCausalLM)

bunny/model/language_model/bunny_stablelm.py

@@ -1,100 +0,0 @@
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-from transformers import AutoConfig, AutoModelForCausalLM
-
-from bunny.model.language_model.stable_lm.modeling_stablelm_epoch import StableLMEpochModel, StableLMEpochConfig, \
-    StableLMEpochForCausalLM
-
-from transformers.modeling_outputs import CausalLMOutputWithPast
-
-from bunny.model.bunny_arch import BunnyMetaModel, BunnyMetaForCausalLM
-
-
-class BunnyStableLMConfig(StableLMEpochConfig):
-    model_type = "bunny-stablelm"
-
-
-class BunnyStableLMModel(BunnyMetaModel, StableLMEpochModel):
-    config_class = BunnyStableLMConfig
-
-    def __init__(self, config: StableLMEpochConfig):
-        super(BunnyStableLMModel, self).__init__(config)
-
-
-class BunnyStableLMForCausalLM(StableLMEpochForCausalLM, BunnyMetaForCausalLM):
-    config_class = BunnyStableLMConfig
-
-    def __init__(self, config):
-        super(StableLMEpochForCausalLM, self).__init__(config)
-        self.model = BunnyStableLMModel(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_model(self):
-        return self.model
-
-    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,
-            images: Optional[torch.FloatTensor] = None,
-            return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        if inputs_embeds is None:
-            (
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                inputs_embeds,
-                labels
-            ) = self.prepare_inputs_labels_for_multimodal(
-                input_ids,
-                position_ids,
-                attention_mask,
-                past_key_values,
-                labels,
-                images
-            )
-
-        return super().forward(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            labels=labels,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict
-        )
-
-    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, attention_mask=None,
-                                      **kwargs):
-        images = kwargs.pop("images", None)
-
-        _inputs = super().prepare_inputs_for_generation(
-            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, attention_mask=attention_mask,
-            **kwargs
-        )
-
-        if images is not None:
-            _inputs['images'] = images
-        return _inputs
-
-
-AutoConfig.register("bunny-stablelm", BunnyStableLMConfig)
-AutoModelForCausalLM.register(BunnyStableLMConfig, BunnyStableLMForCausalLM)

bunny/model/language_model/llama/__init__.py

@@ -1,114 +0,0 @@
-# Copyright 2022 EleutherAI and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import TYPE_CHECKING
-
-from transformers.utils import (
-    OptionalDependencyNotAvailable,
-    _LazyModule,
-    is_flax_available,
-    is_sentencepiece_available,
-    is_tokenizers_available,
-    is_torch_available,
-)
-
-
-_import_structure = {
-    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
-}
-
-try:
-    if not is_sentencepiece_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_llama"] = ["LlamaTokenizer"]
-
-try:
-    if not is_tokenizers_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_llama_fast"] = ["LlamaTokenizerFast"]
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_llama"] = [
-        "LlamaForCausalLM",
-        "LlamaModel",
-        "LlamaPreTrainedModel",
-        "LlamaForSequenceClassification",
-        "LlamaForQuestionAnswering",
-    ]
-
-try:
-    if not is_flax_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_flax_llama"] = ["FlaxLlamaForCausalLM", "FlaxLlamaModel", "FlaxLlamaPreTrainedModel"]
-
-
-if TYPE_CHECKING:
-    from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
-
-    try:
-        if not is_sentencepiece_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_llama import LlamaTokenizer
-
-    try:
-        if not is_tokenizers_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_llama_fast import LlamaTokenizerFast
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_llama import (
-            LlamaForCausalLM,
-            LlamaForQuestionAnswering,
-            LlamaForSequenceClassification,
-            LlamaModel,
-            LlamaPreTrainedModel,
-        )
-
-    try:
-        if not is_flax_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_flax_llama import FlaxLlamaForCausalLM, FlaxLlamaModel, FlaxLlamaPreTrainedModel
-
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

bunny/model/language_model/llama/configuration_llama.py

@@ -1,191 +0,0 @@
-# coding=utf-8
-# 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 transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-# from ..deprecated._archive_maps import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP  # noqa: F401, E402
-
-
-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 decoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer decoder.
-        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`.
-        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. Llama 1 supports up to 2048 tokens,
-            Llama 2 up to 4096, CodeLlama up to 16384.
-        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-06):
-            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`.
-        pad_token_id (`int`, *optional*):
-            Padding token id.
-        bos_token_id (`int`, *optional*, defaults to 1):
-            Beginning of stream token id.
-        eos_token_id (`int`, *optional*, defaults to 2):
-            End of stream token id.
-        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/main/perf_train_gpu_many#tensor-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).
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether to tie weight embeddings
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
-            strategies: linear and dynamic. Their scaling factor must be a 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.
-        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
-            Whether to use a bias in the query, key, value and output projection layers during self-attention.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-
-    ```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=None,
-        bos_token_id=1,
-        eos_token_id=2,
-        pretraining_tp=1,
-        tie_word_embeddings=False,
-        rope_theta=10000.0,
-        rope_scaling=None,
-        attention_bias=False,
-        attention_dropout=0.0,
-        **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_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self._rope_scaling_validation()
-        self.attention_bias = attention_bias
-        self.attention_dropout = attention_dropout
-
-        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) or len(self.rope_scaling) != 2:
-            raise ValueError(
-                "`rope_scaling` must be a dictionary with two fields, `type` 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"]:
-            raise ValueError(
-                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], 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 a float > 1, got {rope_scaling_factor}")

bunny/model/language_model/llama/modeling_llama.py

@@ -1,1844 +0,0 @@
-# coding=utf-8
-# 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
-import warnings
-from typing import List, Optional, Tuple, Union
-
-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.cache_utils import Cache, DynamicCache, StaticCache
-# from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-from dataclasses import dataclass
-@dataclass
-class AttentionMaskConverter:
-    """
-    A utility attention mask class that allows one to:
-        - Create a causal 4d mask
-        - Create a causal 4d mask with slided window
-        - Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length,
-          key_value_length) that can be multiplied with attention scores
-
-    Examples:
-
-    ```python
-    >>> import torch
-    >>> from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-
-    >>> converter = AttentionMaskConverter(True)
-    >>> converter.to_4d(torch.tensor([[0, 0, 0, 1, 1]]), 5, key_value_length=5, dtype=torch.float32)
-    tensor([[[[-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
-            [-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
-            [-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
-            [-3.4028e+38, -3.4028e+38, -3.4028e+38,  0.0000e+00, -3.4028e+38],
-            [-3.4028e+38, -3.4028e+38, -3.4028e+38,  0.0000e+00,  0.0000e+00]]]])
-    ```
-
-    Parameters:
-        is_causal (`bool`):
-            Whether the attention mask should be a uni-directional (causal) or bi-directional mask.
-
-        sliding_window (`int`, *optional*):
-            Optionally, the sliding window masks can be created if `sliding_window` is defined to a positive integer.
-    """
-
-    is_causal: bool
-    sliding_window: int
-
-    def __init__(self, is_causal: bool, sliding_window: Optional[int] = None):
-        self.is_causal = is_causal
-        self.sliding_window = sliding_window
-
-        if self.sliding_window is not None and self.sliding_window <= 0:
-            raise ValueError(
-                f"Make sure that when passing `sliding_window` that its value is a strictly positive integer, not `{self.sliding_window}`"
-            )
-
-    def to_causal_4d(
-        self,
-        batch_size: int,
-        query_length: int,
-        key_value_length: int,
-        dtype: torch.dtype,
-        device: Union[torch.device, "str"] = "cpu",
-    ) -> Optional[torch.Tensor]:
-        """
-        Creates a causal 4D mask of (bsz, head_dim=1, query_length, key_value_length) shape and adds large negative
-        bias to upper right hand triangular matrix (causal mask).
-        """
-        if not self.is_causal:
-            raise ValueError(f"Please use `to_causal_4d` only if {self.__class__} has `is_causal` set to True.")
-
-        # If shape is not cached, create a new causal mask and cache it
-        input_shape = (batch_size, query_length)
-        past_key_values_length = key_value_length - query_length
-
-        # create causal mask
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        causal_4d_mask = None
-        if input_shape[-1] > 1 or self.sliding_window is not None:
-            causal_4d_mask = self._make_causal_mask(
-                input_shape,
-                dtype,
-                device=device,
-                past_key_values_length=past_key_values_length,
-                sliding_window=self.sliding_window,
-            )
-
-        return causal_4d_mask
-
-    def to_4d(
-        self,
-        attention_mask_2d: torch.Tensor,
-        query_length: int,
-        dtype: torch.dtype,
-        key_value_length: Optional[int] = None,
-    ) -> torch.Tensor:
-        """
-        Converts 2D attention mask to 4D attention mask by expanding mask to (bsz, head_dim=1, query_length,
-        key_value_length) shape and by adding a large negative bias to not-attended positions. If attention_mask is
-        causal, a causal mask will be added.
-        """
-        input_shape = (attention_mask_2d.shape[0], query_length)
-
-        # create causal mask
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        causal_4d_mask = None
-        if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal:
-            if key_value_length is None:
-                raise ValueError(
-                    "This attention mask converter is causal. Make sure to pass `key_value_length` to correctly create a causal mask."
-                )
-
-            past_key_values_length = key_value_length - query_length
-            causal_4d_mask = self._make_causal_mask(
-                input_shape,
-                dtype,
-                device=attention_mask_2d.device,
-                past_key_values_length=past_key_values_length,
-                sliding_window=self.sliding_window,
-            )
-        elif self.sliding_window is not None:
-            raise NotImplementedError("Sliding window is currently only implemented for causal masking")
-
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to(
-            attention_mask_2d.device
-        )
-
-        if causal_4d_mask is not None:
-            expanded_attn_mask = causal_4d_mask.masked_fill(expanded_attn_mask.bool(), torch.finfo(dtype).min)
-
-        # expanded_attn_mask + causal_4d_mask can cause some overflow
-        expanded_4d_mask = expanded_attn_mask
-
-        return expanded_4d_mask
-
-    @staticmethod
-    def _make_causal_mask(
-        input_ids_shape: torch.Size,
-        dtype: torch.dtype,
-        device: torch.device,
-        past_key_values_length: int = 0,
-        sliding_window: Optional[int] = None,
-    ):
-        """
-        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)
-
-        # add lower triangular sliding window mask if necessary
-        if sliding_window is not None:
-            diagonal = past_key_values_length - sliding_window - 1
-
-            context_mask = torch.tril(torch.ones_like(mask, dtype=torch.bool), diagonal=diagonal)
-            mask.masked_fill_(context_mask, torch.finfo(dtype).min)
-
-        return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
-
-    @staticmethod
-    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)
-
-    @staticmethod
-    def _unmask_unattended(
-        expanded_mask: torch.FloatTensor,
-        min_dtype: float,
-    ):
-        # fmt: off
-        """
-        Attend to all tokens in masked rows from the expanded attention mask, for example the relevant first rows when
-        using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
-        Details: https://github.com/pytorch/pytorch/issues/110213
-
-        `expanded_mask` is [bsz, num_masks, tgt_seq_len, src_seq_len] or [bsz, tgt_seq_len, src_seq_len].
-        `attention_mask` is [bsz, src_seq_len].
-
-        The dimension num_masks of `expanded_mask` is most often 1, but it can also be the number of heads in the case of alibi attention bias.
-
-        For example, if `expanded_mask` is (e.g. here left-padding case)
-        ```
-        [[[[0, 0, 0],
-           [0, 0, 0],
-           [0, 0, 1]]],
-         [[[1, 0, 0],
-           [1, 1, 0],
-           [1, 1, 1]]],
-         [[[0, 0, 0],
-           [0, 1, 0],
-           [0, 1, 1]]]]
-        ```
-        then the modified `expanded_mask` will be
-        ```
-        [[[[1, 1, 1],   <-- modified
-           [1, 1, 1],   <-- modified
-           [0, 0, 1]]],
-         [[[1, 0, 0],
-           [1, 1, 0],
-           [1, 1, 1]]],
-         [[[1, 1, 1],   <-- modified
-           [0, 1, 0],
-           [0, 1, 1]]]]
-        ```
-        """
-        # fmt: on
-        if expanded_mask.dtype == torch.bool:
-            raise ValueError(
-                "AttentionMaskConverter._unmask_unattended expects a float `expanded_mask`, got a BoolTensor."
-            )
-
-        return expanded_mask.mul(~torch.all(expanded_mask == min_dtype, dim=-1, keepdim=True))
-
-    @staticmethod
-    def _ignore_causal_mask_sdpa(
-        attention_mask: Optional[torch.Tensor],
-        inputs_embeds: torch.Tensor,
-        past_key_values_length: int,
-        sliding_window: Optional[int] = None,
-    ) -> bool:
-        """
-        Detects whether the optional user-specified attention_mask & the automatically created causal mask can be ignored in case PyTorch's SDPA is used, rather relying on SDPA's `is_causal` argument.
-
-        In case no token is masked in the `attention_mask` argument, if `query_length == 1` or
-        `key_value_length == query_length`, we rather rely on SDPA `is_causal` argument to use causal/non-causal masks,
-        allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed).
-        """
-
-        batch_size, query_length = inputs_embeds.shape[0], inputs_embeds.shape[1]
-        key_value_length = query_length + past_key_values_length
-
-        is_tracing = (
-            torch.jit.is_tracing()
-            or isinstance(inputs_embeds, torch.fx.Proxy)
-            or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
-        )
-
-        ignore_causal_mask = False
-
-        if attention_mask is None:
-            # TODO: When tracing with TorchDynamo with fullgraph=True, the model is recompiled depending on the input shape, thus SDPA's `is_causal` argument is rightfully updated (see https://gist.github.com/fxmarty/1313f39037fc1c112508989628c57363). However, when using `torch.export` or
-            # or `torch.onnx.dynamo_export`, we must pass an example input, and `is_causal` behavior is hard-coded. If a user exports a model with q_len > 1, the exported model will hard-code `is_causal=True` which is in general wrong (see https://github.com/pytorch/pytorch/issues/108108).
-            # Thus, we currently can NOT set `ignore_causal_mask = True` here. We would need a `torch._dynamo.is_exporting()` flag.
-            #
-            # Besides, jit.trace can not handle the `q_len > 1` condition for `is_causal` (`TypeError: scaled_dot_product_attention(): argument 'is_causal' must be bool, not Tensor`).
-            if (
-                not is_tracing
-                and (query_length == 1 or key_value_length == query_length)
-                and (sliding_window is None or key_value_length < sliding_window)
-            ):
-                ignore_causal_mask = True
-        elif sliding_window is None or key_value_length < sliding_window:
-            if len(attention_mask.shape) == 4:
-                expected_shape = (batch_size, 1, query_length, key_value_length)
-                if tuple(attention_mask.shape) != expected_shape:
-                    raise ValueError(
-                        f"Incorrect 4D attention_mask shape: {tuple(attention_mask.shape)}; expected: {expected_shape}."
-                    )
-            elif not is_tracing and torch.all(attention_mask == 1):
-                if query_length == 1 or key_value_length == query_length:
-                    # For query_length == 1, causal attention and bi-directional attention are the same.
-                    ignore_causal_mask = True
-
-                # Unfortunately, for query_length > 1 and key_value_length != query_length, we cannot generally ignore the attention mask, as SDPA causal mask generation
-                # may be wrong. We will set `is_causal=False` in SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
-                # Reference: https://github.com/pytorch/pytorch/issues/108108
-                # TODO: maybe revisit this with https://github.com/pytorch/pytorch/pull/114823 in PyTorch 2.3.
-
-        return ignore_causal_mask
-
-
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutputWithPast,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
-from transformers.utils import (
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_flash_attn_2_available,
-    is_flash_attn_greater_or_equal_2_10,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_llama import LlamaConfig
-
-
-if is_flash_attn_2_available():
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "LlamaConfig"
-
-
-def _get_unpad_data(attention_mask):
-    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max().item()
-    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-
-
-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)
-
-
-ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
-
-
-class LlamaRotaryEmbedding(nn.Module):
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
-        super().__init__()
-        self.scaling_factor = scaling_factor
-        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, dtype=torch.int64).float().to(device) / self.dim))
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        # For BC we register cos and sin cached
-        self.max_seq_len_cached = max_position_embeddings
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
-        t = t / self.scaling_factor
-        freqs = torch.outer(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().to(torch.get_default_dtype()), persistent=False)
-        self.register_buffer("_sin_cached", emb.sin().to(torch.get_default_dtype()), persistent=False)
-
-    @property
-    def sin_cached(self):
-        logger.warning_once(
-            "The sin_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use "
-            "the forward method of RoPE from now on instead. It is not used in the `LlamaAttention` class"
-        )
-        return self._sin_cached
-
-    @property
-    def cos_cached(self):
-        logger.warning_once(
-            "The cos_cached attribute will be removed in 4.39. Bear in mind that its contents changed in v4.38. Use "
-            "the forward method of RoPE from now on instead. It is not used in the `LlamaAttention` class"
-        )
-        return self._cos_cached
-
-    @torch.no_grad()
-    def forward(self, x, position_ids):
-        # x: [bs, num_attention_heads, seq_len, head_size]
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
-        position_ids_expanded = position_ids[:, None, :].float()
-        # Force float32 since bfloat16 loses precision on long contexts
-        # See https://github.com/huggingface/transformers/pull/29285
-        device_type = x.device.type
-        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
-        with torch.autocast(device_type=device_type, enabled=False):
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-            cos = emb.cos()
-            sin = emb.sin()
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-
-class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
-    """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
-
-    def forward(self, x, position_ids):
-        # difference to the original RoPE: a scaling factor is aplied to the position ids
-        position_ids = position_ids.float() / self.scaling_factor
-        cos, sin = super().forward(x, position_ids)
-        return cos, sin
-
-
-class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
-    """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
-
-    def forward(self, x, position_ids):
-        # difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
-        seq_len = torch.max(position_ids) + 1
-        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, dtype=torch.int64).float().to(x.device) / self.dim)
-            )
-            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: this may break with compilation
-
-        cos, sin = super().forward(x, position_ids)
-        return cos, sin
-
-
-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=None, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`, *optional*):
-            Deprecated and unused.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_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, layer_idx: Optional[int] = None):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        if layer_idx is None:
-            logger.warning_once(
-                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
-                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
-                "when creating this class."
-            )
-
-        self.attention_dropout = config.attention_dropout
-        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
-        self.rope_theta = config.rope_theta
-        self.is_causal = True
-
-        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=config.attention_bias)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
-        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
-        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,
-                base=self.rope_theta,
-            )
-        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,
-                    base=self.rope_theta,
-                )
-            elif scaling_type == "dynamic":
-                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
-                    self.head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                )
-            else:
-                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> 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)
-
-        past_key_value = getattr(self, "past_key_value", past_key_value)
-        cos, sin = self.rotary_emb(value_states, position_ids)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
-        if attention_mask is not None:  # no matter the length, we just slice it
-            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
-            attn_weights = attn_weights + causal_mask
-
-        # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
-        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
-        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)
-
-        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 LlamaFlashAttention2(LlamaAttention):
-    """
-    Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays
-    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
-    flash attention and deal with padding tokens in case the input contains any of them.
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
-        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
-        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        output_attentions = False
-
-        bsz, q_len, _ = hidden_states.size()
-
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        # Flash attention requires the input to have the shape
-        # batch_size x seq_length x head_dim x hidden_dim
-        # therefore we just need to keep the original shape
-        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)
-
-        cos, sin = self.rotary_emb(value_states, position_ids)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        past_key_value = getattr(self, "past_key_value", past_key_value)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
-        # to be able to avoid many of these transpose/reshape/view.
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        dropout_rate = self.attention_dropout if self.training else 0.0
-
-        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
-        # therefore the input hidden states gets silently casted in float32. Hence, we need
-        # cast them back in the correct dtype just to be sure everything works as expected.
-        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
-        # in fp32. (LlamaRMSNorm handles it correctly)
-
-        input_dtype = query_states.dtype
-        if input_dtype == torch.float32:
-            if torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
-            # Handle the case where the model is quantized
-            elif hasattr(self.config, "_pre_quantization_dtype"):
-                target_dtype = self.config._pre_quantization_dtype
-            else:
-                target_dtype = self.q_proj.weight.dtype
-
-            logger.warning_once(
-                f"The input hidden states seems to be silently casted in float32, this might be related to"
-                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
-                f" {target_dtype}."
-            )
-
-            query_states = query_states.to(target_dtype)
-            key_states = key_states.to(target_dtype)
-            value_states = value_states.to(target_dtype)
-
-        attn_output = self._flash_attention_forward(
-            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
-        )
-
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-    def _flash_attention_forward(
-        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
-    ):
-        """
-        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
-        first unpad the input, then computes the attention scores and pad the final attention scores.
-
-        Args:
-            query_states (`torch.Tensor`):
-                Input query states to be passed to Flash Attention API
-            key_states (`torch.Tensor`):
-                Input key states to be passed to Flash Attention API
-            value_states (`torch.Tensor`):
-                Input value states to be passed to Flash Attention API
-            attention_mask (`torch.Tensor`):
-                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
-                position of padding tokens and 1 for the position of non-padding tokens.
-            dropout (`float`):
-                Attention dropout
-            softmax_scale (`float`, *optional*):
-                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
-        """
-        if not self._flash_attn_uses_top_left_mask:
-            causal = self.is_causal
-        else:
-            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
-            causal = self.is_causal and query_length != 1
-
-        # Contains at least one padding token in the sequence
-        if attention_mask is not None:
-            batch_size = query_states.shape[0]
-            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
-                query_states, key_states, value_states, attention_mask, query_length
-            )
-
-            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
-
-            attn_output_unpad = flash_attn_varlen_func(
-                query_states,
-                key_states,
-                value_states,
-                cu_seqlens_q=cu_seqlens_q,
-                cu_seqlens_k=cu_seqlens_k,
-                max_seqlen_q=max_seqlen_in_batch_q,
-                max_seqlen_k=max_seqlen_in_batch_k,
-                dropout_p=dropout,
-                softmax_scale=softmax_scale,
-                causal=causal,
-            )
-
-            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
-        else:
-            attn_output = flash_attn_func(
-                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
-            )
-
-        return attn_output
-
-    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
-        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
-
-        key_layer = index_first_axis(
-            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
-        )
-        value_layer = index_first_axis(
-            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
-        )
-        if query_length == kv_seq_len:
-            query_layer = index_first_axis(
-                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
-            )
-            cu_seqlens_q = cu_seqlens_k
-            max_seqlen_in_batch_q = max_seqlen_in_batch_k
-            indices_q = indices_k
-        elif query_length == 1:
-            max_seqlen_in_batch_q = 1
-            cu_seqlens_q = torch.arange(
-                batch_size + 1, dtype=torch.int32, device=query_layer.device
-            )  # There is a memcpy here, that is very bad.
-            indices_q = cu_seqlens_q[:-1]
-            query_layer = query_layer.squeeze(1)
-        else:
-            # The -q_len: slice assumes left padding.
-            attention_mask = attention_mask[:, -query_length:]
-            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
-
-        return (
-            query_layer,
-            key_layer,
-            value_layer,
-            indices_q,
-            (cu_seqlens_q, cu_seqlens_k),
-            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
-        )
-
-
-class LlamaSdpaAttention(LlamaAttention):
-    """
-    Llama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
-    `LlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
-    SDPA API.
-    """
-
-    # Adapted from LlamaAttention.forward
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if output_attentions:
-            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
-            logger.warning_once(
-                "LlamaModel is using LlamaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
-                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
-            )
-            return super().forward(
-                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,
-                cache_position=cache_position,
-            )
-
-        bsz, q_len, _ = hidden_states.size()
-
-        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)
-
-        cos, sin = self.rotary_emb(value_states, position_ids)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        # In case static cache is used, it is an instance attribute.
-        past_key_value = getattr(self, "past_key_value", past_key_value)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        causal_mask = attention_mask
-        if attention_mask is not None:
-            causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
-
-        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
-        # Reference: https://github.com/pytorch/pytorch/issues/112577.
-        if query_states.device.type == "cuda" and causal_mask is not None:
-            query_states = query_states.contiguous()
-            key_states = key_states.contiguous()
-            value_states = value_states.contiguous()
-
-        # In case we are not compiling, we may set `causal_mask` to None, which is required to dispatch to SDPA's Flash Attention 2 backend, rather
-        # relying on the `is_causal` argument.
-        attn_output = torch.nn.functional.scaled_dot_product_attention(
-            query_states,
-            key_states,
-            value_states,
-            attn_mask=causal_mask,
-            dropout_p=self.attention_dropout if self.training else 0.0,
-            is_causal=causal_mask is None and q_len > 1,
-        )
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
-
-        attn_output = self.o_proj(attn_output)
-
-        return attn_output, None, past_key_value
-
-
-LLAMA_ATTENTION_CLASSES = {
-    "eager": LlamaAttention,
-    "flash_attention_2": LlamaFlashAttention2,
-    "sdpa": LlamaSdpaAttention,
-}
-
-
-class LlamaDecoderLayer(nn.Module):
-    def __init__(self, config: LlamaConfig, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-
-        self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
-
-        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,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> 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_size, sequence_length)` if flash attention is used or `(batch_size, 1,
-                query_sequence_length, key_sequence_length)` if default attention is used.
-            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
-        """
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-
-        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,
-            cache_position=cache_position,
-            **kwargs,
-        )
-        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"]
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_cache_class = True
-
-    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 _setup_cache(self, cache_cls, max_batch_size, max_cache_len: Optional[int] = None):
-        if self.config._attn_implementation == "flash_attention_2" and cache_cls == StaticCache:
-            raise ValueError(
-                "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
-                "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
-            )
-
-        for layer in self.model.layers:
-            device = layer.input_layernorm.weight.device
-            if hasattr(self.config, "_pre_quantization_dtype"):
-                dtype = self.config._pre_quantization_dtype
-            else:
-                dtype = layer.self_attn.o_proj.weight.dtype
-            layer.self_attn.past_key_value = cache_cls(
-                self.config, max_batch_size, max_cache_len, device=device, dtype=dtype
-            )
-
-    def _reset_cache(self):
-        for layer in self.model.layers:
-            layer.self_attn.past_key_value = None
-
-
-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 `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 (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
-            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
-            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
-
-            Two formats are allowed:
-            - a [`~cache_utils.Cache`] instance;
-            - 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)`). This is also known as the legacy
-            cache format.
-
-            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
-            legacy cache format will be returned.
-
-            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
-            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `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.
-        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
-            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
-            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
-            the complete sequence length.
-"""
-
-
-@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, layer_idx) for layer_idx 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
-
-    @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,
-        cache_position: Optional[torch.LongTensor] = 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 None) ^ (inputs_embeds is not None):
-            raise ValueError(
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
-
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
-            )
-            use_cache = False
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        past_seen_tokens = 0
-        if use_cache:  # kept for BC (cache positions)
-            if not isinstance(past_key_values, StaticCache):
-                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-                past_seen_tokens = past_key_values.get_seq_length()
-
-        if cache_position is None:
-            if isinstance(past_key_values, StaticCache):
-                raise ValueError("cache_position is a required argument when using StaticCache.")
-            cache_position = torch.arange(
-                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
-            )
-
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-
-        causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_seen_tokens)
-
-        # embed positions
-        hidden_states = inputs_embeds
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = None
-
-        for decoder_layer in self.layers:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-                layer_outputs = self._gradient_checkpointing_func(
-                    decoder_layer.__call__,
-                    hidden_states,
-                    causal_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
-                    cache_position,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=causal_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                    cache_position=cache_position,
-                )
-
-            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 = None
-        if use_cache:
-            next_cache = (
-                next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache
-            )
-        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,
-        )
-
-    def _update_causal_mask(
-        self,
-        attention_mask: torch.Tensor,
-        input_tensor: torch.Tensor,
-        cache_position: torch.Tensor,
-        past_seen_tokens: int,
-    ):
-        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
-        # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
-        # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
-        # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
-
-        if self.config._attn_implementation == "flash_attention_2":
-            if attention_mask is not None and 0.0 in attention_mask:
-                return attention_mask
-            return None
-
-        if self.config._attn_implementation == "sdpa":
-            # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument,
-            # in order to dispatch on Flash Attention 2.
-            if AttentionMaskConverter._ignore_causal_mask_sdpa(
-                attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens
-            ):
-                return None
-
-        dtype, device = input_tensor.dtype, input_tensor.device
-        min_dtype = torch.finfo(dtype).min
-        sequence_length = input_tensor.shape[1]
-        if hasattr(getattr(self.layers[0], "self_attn", {}), "past_key_value"):  # static cache
-            target_length = self.config.max_position_embeddings
-        else:  # dynamic cache
-            target_length = (
-                attention_mask.shape[-1]
-                if isinstance(attention_mask, torch.Tensor)
-                else past_seen_tokens + sequence_length + 1
-            )
-
-        causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
-        if sequence_length != 1:
-            causal_mask = torch.triu(causal_mask, diagonal=1)
-        causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
-        causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
-        if attention_mask is not None:
-            causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
-            if attention_mask.dim() == 2:
-                mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
-                causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
-            elif attention_mask.dim() == 4:
-                # backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
-                # cache. In that case, the 4D attention mask attends to the newest tokens only.
-                if attention_mask.shape[-2] < cache_position[0] + sequence_length:
-                    offset = cache_position[0]
-                else:
-                    offset = 0
-                mask_shape = attention_mask.shape
-                mask_slice = (attention_mask.eq(0.0)).to(dtype=dtype) * min_dtype
-                causal_mask[
-                    : mask_shape[0], : mask_shape[1], offset : mask_shape[2] + offset, : mask_shape[3]
-                ] = mask_slice
-
-        if (
-            self.config._attn_implementation == "sdpa"
-            and attention_mask is not None
-            and attention_mask.device.type == "cuda"
-        ):
-            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
-            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
-            # Details: https://github.com/pytorch/pytorch/issues/110213
-            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
-
-        return causal_mask
-
-
-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,
-        cache_position: Optional[torch.LongTensor] = 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("meta-llama/Llama-2-7b-hf")
-        >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
-
-        >>> 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,
-            cache_position=cache_position,
-        )
-
-        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, cache_position=None, **kwargs
-    ):
-        # With static cache, the `past_key_values` is None
-        # TODO joao: standardize interface for the different Cache classes and remove of this if
-        has_static_cache = False
-        if past_key_values is None:
-            past_key_values = getattr(getattr(self.model.layers[0], "self_attn", {}), "past_key_value", None)
-            has_static_cache = past_key_values is not None
-
-        past_length = 0
-        if past_key_values is not None:
-            if isinstance(past_key_values, Cache):
-                past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
-                max_cache_length = (
-                    torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
-                    if past_key_values.get_max_length() is not None
-                    else None
-                )
-                cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
-            # TODO joao: remove this `else` after `generate` prioritizes `Cache` objects
-            else:
-                cache_length = past_length = past_key_values[0][0].shape[2]
-                max_cache_length = None
-
-            # Keep only the unprocessed tokens:
-            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
-            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
-            # input)
-            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
-                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
-            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
-            # input_ids based on the past_length.
-            elif past_length < input_ids.shape[1]:
-                input_ids = input_ids[:, past_length:]
-            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
-            else:
-                remove_prefix_length = input_ids.shape[1] - 1
-                input_ids = input_ids[:, remove_prefix_length:]
-            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
-            if (
-                max_cache_length is not None
-                and attention_mask is not None
-                and cache_length + input_ids.shape[1] > max_cache_length
-            ):
-                attention_mask = attention_mask[:, -max_cache_length:]
-
-        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[:, -input_ids.shape[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:
-            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
-            # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
-            # TODO: use `next_tokens` directly instead.
-            model_inputs = {"input_ids": input_ids.contiguous()}
-
-        input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
-        if cache_position is None:
-            cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)
-        else:
-            cache_position = cache_position[-input_length:]
-
-        if has_static_cache:
-            past_key_values = None
-
-        model_inputs.update(
-            {
-                "position_ids": position_ids,
-                "cache_position": cache_position,
-                "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:
-                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
-                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
-                sequence_lengths = sequence_lengths % input_ids.shape[-1]
-                sequence_lengths = sequence_lengths.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,
-        )
-
-
-@add_start_docstrings(
-    """
-The Llama Model transformer with a span classification head on top for extractive question-answering tasks like
-SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
-    """,
-    LLAMA_START_DOCSTRING,
-)
-class LlamaForQuestionAnswering(LlamaPreTrainedModel):
-    base_model_prefix = "transformer"
-
-    # Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->Llama
-    def __init__(self, config):
-        super().__init__(config)
-        self.transformer = LlamaModel(config)
-        self.qa_outputs = nn.Linear(config.hidden_size, 2)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.transformer.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.transformer.embed_tokens = value
-
-    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[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,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            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,
-        )

bunny/model/language_model/llama/tokenization_llama.py

@@ -1,471 +0,0 @@
-# coding=utf-8
-# 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.
-
-"""Tokenization classes for LLaMA."""
-import os
-from shutil import copyfile
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
-
-import sentencepiece as spm
-
-from transformers.convert_slow_tokenizer import import_protobuf
-from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
-from transformers.utils import logging
-
-
-if TYPE_CHECKING:
-    from transformers.tokenization_utils_base import TextInput
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
-
-SPIECE_UNDERLINE = "▁"
-
-B_INST, E_INST = "[INST]", "[/INST]"
-B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
-
-# fmt: off
-DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
-answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
- that your responses are socially unbiased and positive in nature.
-
-If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
-correct. If you don't know the answer to a question, please don't share false information."""
-# fmt: on
-
-
-class LlamaTokenizer(PreTrainedTokenizer):
-    """
-    Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
-    no padding token in the original model.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
-            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
-        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
-            The end of sequence token.
-        pad_token (`str` or `tokenizers.AddedToken`, *optional*):
-            A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
-            attention mechanisms or loss computation.
-        sp_model_kwargs (`Dict[str, Any]`, `Optional`, *optional*):
-            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
-            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
-            to set:
-
-            - `enable_sampling`: Enable subword regularization.
-            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
-
-              - `nbest_size = {0,1}`: No sampling is performed.
-              - `nbest_size > 1`: samples from the nbest_size results.
-              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
-                using forward-filtering-and-backward-sampling algorithm.
-
-            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
-              BPE-dropout.
-
-        add_bos_token (`bool`, *optional*, defaults to `True`):
-            Whether or not to add an `bos_token` at the start of sequences.
-        add_eos_token (`bool`, *optional*, defaults to `False`):
-            Whether or not to add an `eos_token` at the end of sequences.
-        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
-            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
-            extra spaces.
-        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
-            Whether or not the default system prompt for Llama should be used.
-        spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
-            Whether or not to add spaces between special tokens.
-        legacy (`bool`, *optional*):
-            Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622
-            and #25224 which includes fixes to properly handle tokens that appear after special tokens. A simple
-            example:
-
-            - `legacy=True`:
-            ```python
-            >>> from transformers import T5Tokenizer
-
-            >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=True)
-            >>> tokenizer.encode("Hello <extra_id_0>.")
-            [8774, 32099, 3, 5, 1]
-            ```
-            - `legacy=False`:
-            ```python
-            >>> from transformers import T5Tokenizer
-
-            >>> tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base", legacy=False)
-            >>> tokenizer.encode("Hello <extra_id_0>.")  # the extra space `[3]` is no longer here
-            [8774, 32099, 5, 1]
-            ```
-            Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details.
-        add_prefix_space (`bool`, *optional*, defaults to `True`):
-            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
-            other word.
-
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    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,
-        use_default_system_prompt=False,
-        spaces_between_special_tokens=False,
-        legacy=None,
-        add_prefix_space=True,
-        **kwargs,
-    ):
-        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-        bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
-        eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
-        unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
-        pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
-
-        if legacy is None:
-            logger.warning_once(
-                f"You are using the default legacy behaviour of the {self.__class__}. This is"
-                " expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you."
-                " If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it"
-                " means, and thoroughly read the reason why this was added as explained in"
-                " https://github.com/huggingface/transformers/pull/24565"
-            )
-            legacy = True
-
-        self.legacy = legacy
-        self.vocab_file = vocab_file
-        self.add_bos_token = add_bos_token
-        self.add_eos_token = add_eos_token
-        self.use_default_system_prompt = use_default_system_prompt
-        self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", False))
-        self.add_prefix_space = add_prefix_space
-
-        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,
-            use_default_system_prompt=use_default_system_prompt,
-            spaces_between_special_tokens=spaces_between_special_tokens,
-            legacy=legacy,
-            add_prefix_space=add_prefix_space,
-            **kwargs,
-        )
-
-    @property
-    def unk_token_length(self):
-        return len(self.sp_model.encode(str(self.unk_token)))
-
-    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor
-    def get_spm_processor(self, from_slow=False):
-        tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        if self.legacy or from_slow:  # no dependency on protobuf
-            tokenizer.Load(self.vocab_file)
-            return tokenizer
-
-        with open(self.vocab_file, "rb") as f:
-            sp_model = f.read()
-            model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)")
-            model = model_pb2.ModelProto.FromString(sp_model)
-            normalizer_spec = model_pb2.NormalizerSpec()
-            normalizer_spec.add_dummy_prefix = False
-            model.normalizer_spec.MergeFrom(normalizer_spec)
-            sp_model = model.SerializeToString()
-            tokenizer.LoadFromSerializedProto(sp_model)
-        return tokenizer
-
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        state["sp_model"] = None
-        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
-        return state
-
-    def __setstate__(self, d):
-        self.__dict__ = d
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
-
-    @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
-
-    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
-    def tokenize(self, text: "TextInput", **kwargs) -> List[str]:
-        """
-        Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
-        first token is special.
-        """
-        if self.legacy or len(text) == 0:
-            return super().tokenize(text, **kwargs)
-
-        text = text.replace(SPIECE_UNDERLINE, " ")
-        if self.add_prefix_space:
-            text = SPIECE_UNDERLINE + text
-
-        tokens = super().tokenize(text, **kwargs)
-
-        if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
-            tokens = tokens[1:]
-        return tokens
-
-    # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize
-    def _tokenize(self, text, **kwargs):
-        """
-        Returns a tokenized string.
-
-        We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
-        SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
-        `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the
-        `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
-        `self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
-        """
-        tokens = self.sp_model.encode(text, out_type=str)
-        if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")):
-            return tokens
-
-        # 1. Encode string + prefix ex: "<unk> Hey"
-        tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
-        # 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
-        return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
-
-    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."""
-        # since we manually add the prefix space, we have to remove it when decoding
-        if tokens[0].startswith(SPIECE_UNDERLINE) and self.add_prefix_space:
-            tokens[0] = tokens[0][1:]
-
-        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 and self.legacy:
-                    out_string += " "
-                out_string += self.sp_model.decode(current_sub_tokens) + token
-                prev_is_special = True
-                current_sub_tokens = []
-            else:
-                if prev_is_special and i == 1 and self.add_prefix_space and not token.startswith(SPIECE_UNDERLINE):
-                    out_string += " "
-                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
-
-    @property
-    def default_chat_template(self):
-        """
-        LLaMA uses [INST] and [/INST] to indicate user messages, and <<SYS>> and <</SYS>> to indicate system messages.
-        Assistant messages do not have special tokens, because LLaMA chat models are generally trained with strict
-        user/assistant/user/assistant message ordering, and so assistant messages can be identified from the ordering
-        rather than needing special tokens. The system message is partly 'embedded' in the first user message, which
-        results in an unusual token ordering when it is present. This template should definitely be changed if you wish
-        to fine-tune a model with more flexible role ordering!
-
-        The output should look something like:
-
-        <bos>[INST] B_SYS SystemPrompt E_SYS Prompt [/INST] Answer <eos><bos>[INST] Prompt [/INST] Answer <eos>
-        <bos>[INST] Prompt [/INST]
-
-        The reference for this chat template is [this code
-        snippet](https://github.com/facebookresearch/llama/blob/556949fdfb72da27c2f4a40b7f0e4cf0b8153a28/llama/generation.py#L320-L362)
-        in the original repository.
-        """
-        logger.warning_once(
-            "\nNo chat template is defined for this tokenizer - using the default template "
-            f"for the {self.__class__.__name__} class. If the default is not appropriate for "
-            "your model, please set `tokenizer.chat_template` to an appropriate template. "
-            "See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
-        )
-        template = (
-            "{% if messages[0]['role'] == 'system' %}"
-            "{% set loop_messages = messages[1:] %}"  # Extract system message if it's present
-            "{% set system_message = messages[0]['content'] %}"
-            "{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}"
-            "{% set loop_messages = messages %}"  # Or use the default system message if the flag is set
-            "{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}"
-            "{% else %}"
-            "{% set loop_messages = messages %}"
-            "{% set system_message = false %}"
-            "{% endif %}"
-            "{% for message in loop_messages %}"  # Loop over all non-system messages
-            "{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}"
-            "{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}"
-            "{% endif %}"
-            "{% if loop.index0 == 0 and system_message != false %}"  # Embed system message in first message
-            "{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}"
-            "{% else %}"
-            "{% set content = message['content'] %}"
-            "{% endif %}"
-            "{% if message['role'] == 'user' %}"  # After all of that, handle messages/roles in a fairly normal way
-            "{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}"
-            "{% elif message['role'] == 'system' %}"
-            "{{ '<<SYS>>\\n' + content.strip() + '\\n<</SYS>>\\n\\n' }}"
-            "{% elif message['role'] == 'assistant' %}"
-            "{{ ' '  + content.strip() + ' ' + eos_token }}"
-            "{% endif %}"
-            "{% endfor %}"
-        )
-        template = template.replace("USE_DEFAULT_PROMPT", "true" if self.use_default_system_prompt else "false")
-        default_message = DEFAULT_SYSTEM_PROMPT.replace("\n", "\\n").replace("'", "\\'")
-        template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message)
-
-        return template

bunny/model/language_model/llama/tokenization_llama_fast.py

@@ -1,281 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import os
-from shutil import copyfile
-from typing import Optional, Tuple
-
-from tokenizers import processors
-
-from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
-from transformers.utils import is_sentencepiece_available, logging
-from transformers.utils.versions import require_version
-
-
-require_version("tokenizers>=0.13.3")
-
-if is_sentencepiece_available():
-    from .tokenization_llama import LlamaTokenizer
-else:
-    LlamaTokenizer = None
-
-logger = logging.get_logger(__name__)
-VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}
-
-B_INST, E_INST = "[INST]", "[/INST]"
-B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
-
-# fmt: off
-DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
-answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
- that your responses are socially unbiased and positive in nature.
-
-If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
-correct. If you don't know the answer to a question, please don't share false information."""
-# fmt: on
-
-
-class LlamaTokenizerFast(PreTrainedTokenizerFast):
-    """
-    Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.
-
-    This uses notably ByteFallback and no normalization.
-
-    ```python
-    >>> from transformers import LlamaTokenizerFast
-
-    >>> tokenizer = LlamaTokenizerFast.from_pretrained("hf-internal-testing/llama-tokenizer")
-    >>> tokenizer.encode("Hello this is a test")
-    [1, 15043, 445, 338, 263, 1243]
-    ```
-
-    If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or
-    call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the
-    values of the first token and final token of an encoded sequence will not be correct). For more details, checkout
-    [post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.
-
-
-    This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
-    refer to this superclass for more information regarding those methods.
-
-    Args:
-        vocab_file (`str`, *optional*):
-            [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that
-            contains the vocabulary necessary to instantiate a tokenizer.
-        tokenizer_file (`str`, *optional*):
-            [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
-            contains everything needed to load the tokenizer.
-        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
-            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
-            extra spaces.
-        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<s>"`):
-            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
-        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"</s>"`):
-            The end of sequence token.
-        add_bos_token (`bool`, *optional*, defaults to `True`):
-            Whether or not to add an `bos_token` at the start of sequences.
-        add_eos_token (`bool`, *optional*, defaults to `False`):
-            Whether or not to add an `eos_token` at the end of sequences.
-        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
-            Whether or not the default system prompt for Llama should be used.
-        add_prefix_space (`bool`, *optional*):
-            Whether or not the tokenizer should automatically add a prefix space
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    slow_tokenizer_class = LlamaTokenizer
-    padding_side = "left"
-    model_input_names = ["input_ids", "attention_mask"]
-
-    def __init__(
-        self,
-        vocab_file=None,
-        tokenizer_file=None,
-        clean_up_tokenization_spaces=False,
-        unk_token="<unk>",
-        bos_token="<s>",
-        eos_token="</s>",
-        add_bos_token=True,
-        add_eos_token=False,
-        use_default_system_prompt=False,
-        add_prefix_space=None,
-        **kwargs,
-    ):
-        if add_prefix_space is not None:
-            logger.warning_once(
-                "You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers"
-            )
-            kwargs["from_slow"] = True
-
-        super().__init__(
-            vocab_file=vocab_file,
-            tokenizer_file=tokenizer_file,
-            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            unk_token=unk_token,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            add_bos_token=add_bos_token,
-            add_eos_token=add_eos_token,
-            use_default_system_prompt=use_default_system_prompt,
-            **kwargs,
-        )
-        self._add_bos_token = add_bos_token
-        self._add_eos_token = add_eos_token
-        self.update_post_processor()
-        self.use_default_system_prompt = use_default_system_prompt
-        self.vocab_file = vocab_file
-
-    @property
-    def can_save_slow_tokenizer(self) -> bool:
-        return os.path.isfile(self.vocab_file) if self.vocab_file else False
-
-    def update_post_processor(self):
-        """
-        Updates the underlying post processor with the current `bos_token` and `eos_token`.
-        """
-        bos = self.bos_token
-        bos_token_id = self.bos_token_id
-        if bos is None and self.add_bos_token:
-            raise ValueError("add_bos_token = True but bos_token = None")
-
-        eos = self.eos_token
-        eos_token_id = self.eos_token_id
-        if eos is None and self.add_eos_token:
-            raise ValueError("add_eos_token = True but eos_token = None")
-
-        single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
-        pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
-
-        special_tokens = []
-        if self.add_bos_token:
-            special_tokens.append((bos, bos_token_id))
-        if self.add_eos_token:
-            special_tokens.append((eos, eos_token_id))
-        self._tokenizer.post_processor = processors.TemplateProcessing(
-            single=single, pair=pair, special_tokens=special_tokens
-        )
-
-    @property
-    def add_eos_token(self):
-        return self._add_eos_token
-
-    @property
-    def add_bos_token(self):
-        return self._add_bos_token
-
-    @add_eos_token.setter
-    def add_eos_token(self, value):
-        self._add_eos_token = value
-        self.update_post_processor()
-
-    @add_bos_token.setter
-    def add_bos_token(self, value):
-        self._add_bos_token = value
-        self.update_post_processor()
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not self.can_save_slow_tokenizer:
-            raise ValueError(
-                "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
-                "tokenizer."
-            )
-
-        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):
-            copyfile(self.vocab_file, out_vocab_file)
-
-        return (out_vocab_file,)
-
-    @property
-    # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.default_chat_template
-    def default_chat_template(self):
-        """
-        LLaMA uses [INST] and [/INST] to indicate user messages, and <<SYS>> and <</SYS>> to indicate system messages.
-        Assistant messages do not have special tokens, because LLaMA chat models are generally trained with strict
-        user/assistant/user/assistant message ordering, and so assistant messages can be identified from the ordering
-        rather than needing special tokens. The system message is partly 'embedded' in the first user message, which
-        results in an unusual token ordering when it is present. This template should definitely be changed if you wish
-        to fine-tune a model with more flexible role ordering!
-
-        The output should look something like:
-
-        <bos>[INST] B_SYS SystemPrompt E_SYS Prompt [/INST] Answer <eos><bos>[INST] Prompt [/INST] Answer <eos>
-        <bos>[INST] Prompt [/INST]
-
-        The reference for this chat template is [this code
-        snippet](https://github.com/facebookresearch/llama/blob/556949fdfb72da27c2f4a40b7f0e4cf0b8153a28/llama/generation.py#L320-L362)
-        in the original repository.
-        """
-        logger.warning_once(
-            "\nNo chat template is defined for this tokenizer - using the default template "
-            f"for the {self.__class__.__name__} class. If the default is not appropriate for "
-            "your model, please set `tokenizer.chat_template` to an appropriate template. "
-            "See https://huggingface.co/docs/transformers/main/chat_templating for more information.\n"
-        )
-        template = (
-            "{% if messages[0]['role'] == 'system' %}"
-            "{% set loop_messages = messages[1:] %}"  # Extract system message if it's present
-            "{% set system_message = messages[0]['content'] %}"
-            "{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}"
-            "{% set loop_messages = messages %}"  # Or use the default system message if the flag is set
-            "{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}"
-            "{% else %}"
-            "{% set loop_messages = messages %}"
-            "{% set system_message = false %}"
-            "{% endif %}"
-            "{% for message in loop_messages %}"  # Loop over all non-system messages
-            "{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}"
-            "{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}"
-            "{% endif %}"
-            "{% if loop.index0 == 0 and system_message != false %}"  # Embed system message in first message
-            "{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}"
-            "{% else %}"
-            "{% set content = message['content'] %}"
-            "{% endif %}"
-            "{% if message['role'] == 'user' %}"  # After all of that, handle messages/roles in a fairly normal way
-            "{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}"
-            "{% elif message['role'] == 'system' %}"
-            "{{ '<<SYS>>\\n' + content.strip() + '\\n<</SYS>>\\n\\n' }}"
-            "{% elif message['role'] == 'assistant' %}"
-            "{{ ' '  + content.strip() + ' ' + eos_token }}"
-            "{% endif %}"
-            "{% endfor %}"
-        )
-        template = template.replace("USE_DEFAULT_PROMPT", "true" if self.use_default_system_prompt else "false")
-        default_message = DEFAULT_SYSTEM_PROMPT.replace("\n", "\\n").replace("'", "\\'")
-        template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message)
-
-        return template
-
-    # TODO ArthurZ let's rely on the template processor instead, refactor all fast tokenizers
-    # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.build_inputs_with_special_tokens
-    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

bunny/model/language_model/minicpm/configuration_minicpm.py

@@ -1,202 +0,0 @@
-# coding=utf-8
-# 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.
-""" MiniCPM model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
-
-
-class MiniCPMConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM
-    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 MiniCPM-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 MiniCPM model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`MiniCPMModel`]
-        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 decoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer decoder.
-        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`.
-        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. MiniCPM 1 supports up to 2048 tokens,
-            MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.
-        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-06):
-            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`.
-        pad_token_id (`int`, *optional*):
-            Padding token id.
-        bos_token_id (`int`, *optional*, defaults to 1):
-            Beginning of stream token id.
-        eos_token_id (`int`, *optional*, defaults to 2):
-            End of stream token id.
-        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).
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether to tie weight embeddings
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
-            strategies: linear and dynamic. Their scaling factor must be a 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/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
-            experimental feature, subject to breaking API changes in future versions.
-        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
-            Whether to use a bias in the query, key, value and output projection layers during self-attention.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-
-    ```python
-    >>> from transformers import MiniCPMModel, MiniCPMConfig
-
-    >>> # Initializing a MiniCPM minicpm-7b style configuration
-    >>> configuration = MiniCPMConfig()
-
-    >>> # Initializing a model from the minicpm-7b style configuration
-    >>> model = MiniCPMModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "minicpm"
-    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=None,
-        bos_token_id=1,
-        eos_token_id=2,
-        pretraining_tp=1,
-        tie_word_embeddings=True,
-        rope_theta=10000.0,
-        rope_scaling=None,
-        attention_bias=False,
-        attention_dropout=0.0,
-        scale_emb=1,
-        dim_model_base=1,
-        scale_depth=1,
-        **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_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self._rope_scaling_validation()
-        self.attention_bias = attention_bias
-        self.attention_dropout = attention_dropout
-        self.scale_emb = scale_emb
-        self.dim_model_base = dim_model_base
-        self.scale_depth = scale_depth
-
-        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,
-        )
-        try:
-            import flash_attn
-            self._attn_implementation = "flash_attention_2"
-        except:
-            pass
-
-    def _rope_scaling_validation(self):
-        """
-        Validate the `rope_scaling` configuration.
-        """
-        if self.rope_scaling is None:
-            return
-
-        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
-            raise ValueError(
-                "`rope_scaling` must be a dictionary with with two fields, `type` 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"]:
-            raise ValueError(
-                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], 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 a float > 1, got {rope_scaling_factor}")

bunny/model/language_model/minicpm/modeling_minicpm.py

@@ -1,1456 +0,0 @@
-# coding=utf-8
-# 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 MiniCPM model."""
-import math
-import warnings
-from typing import List, Optional, Tuple, Union, Dict
-
-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.cache_utils import Cache, DynamicCache
-from transformers.modeling_attn_mask_utils import (
-    AttentionMaskConverter,
-    _prepare_4d_attention_mask,
-    _prepare_4d_causal_attention_mask,
-    _prepare_4d_causal_attention_mask_for_sdpa,
-)
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
-from transformers.modeling_utils import PreTrainedModel
-from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
-from transformers.utils import (
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_flash_attn_2_available,
-    is_flash_attn_greater_or_equal_2_10,
-    logging,
-    replace_return_docstrings,
-)
-from transformers.utils.import_utils import is_torch_fx_available
-from .configuration_minicpm import MiniCPMConfig
-import re
-
-try:
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
-except:
-    pass
-
-
-# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
-# It means that the function will not be traced through and simply appear as a node in the graph.
-if is_torch_fx_available():
-    if not is_torch_greater_or_equal_than_1_13:
-        import torch.fx
-
-    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "MiniCPMConfig"
-
-
-def _get_unpad_data(attention_mask):
-    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max().item()
-    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-
-
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    warnings.warn(
-        "Calling `transformers.models.minicpm.modeling_minicpm._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
-    )
-    return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
-
-
-def _make_causal_mask(
-    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
-):
-    warnings.warn(
-        "Calling `transformers.models.minicpm.modeling_minicpm._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.minicpm.modeling_minicpm.AttentionMaskConverter._make_causal_mask"
-    )
-    return AttentionMaskConverter._make_causal_mask(
-        input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
-    )
-
-# @torch.jit.script  # type: ignore
-def rms_layernorm(hidden: torch.Tensor, weight: torch.Tensor, eps: float):
-    old_dtype = hidden.dtype
-    variance = hidden.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)
-    hidden = (hidden * torch.rsqrt(variance + eps)).to(old_dtype)
-    return hidden * weight
-
-
-class MiniCPMRMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        MiniCPMRMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        return rms_layernorm(hidden_states, self.weight, self.variance_epsilon)
-
-
-ALL_LAYERNORM_LAYERS.append(MiniCPMRMSNorm)
-
-
-class MiniCPMRotaryEmbedding(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()
-            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32
-        )
-
-    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)
-        freqs = torch.outer(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().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().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[:seq_len].to(dtype=x.dtype),
-            self.sin_cached[:seq_len].to(dtype=x.dtype),
-        )
-
-
-class MiniCPMLinearScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
-    """MiniCPMRotaryEmbedding 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 = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
-        t = t / self.scaling_factor
-
-        freqs = torch.outer(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().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
-
-
-class MiniCPMDynamicNTKScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
-    """MiniCPMRotaryEmbedding 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.outer(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().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
-
-
-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, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`):
-            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
-            used to pass offsetted position ids when working with a KV-cache.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    # cos = cos[position_ids].unsqueeze(unsqueeze_dim)
-    # sin = sin[position_ids].unsqueeze(unsqueeze_dim)
-    # q_embed = (q * cos) + (rotate_half(q) * sin)
-    # k_embed = (k * cos) + (rotate_half(k) * sin)
-    orig_dtype = k.dtype
-    cos = cos[position_ids].unsqueeze(unsqueeze_dim)  # [bs, 1, seq_len, dim]
-    sin = sin[position_ids].unsqueeze(unsqueeze_dim)  # [bs, 1, seq_len, dim]
-    q_fp32 = q.to(dtype=torch.float32, device=q.device)
-    k_fp32 = k.to(dtype=torch.float32, device=k.device)
-    q_embed = (q_fp32 * cos) + (rotate_half(q_fp32) * sin)
-    k_embed = (k_fp32 * cos) + (rotate_half(k_fp32) * sin)
-    return q_embed.to(dtype=orig_dtype), k_embed.to(dtype=orig_dtype)
-
-class MiniCPMMLP(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 MiniCPMAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: MiniCPMConfig, layer_idx: Optional[int] = None):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        if layer_idx is None:
-            logger.warning_once(
-                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
-                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
-                "when creating this class."
-            )
-
-        self.attention_dropout = config.attention_dropout
-        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
-        self.rope_theta = config.rope_theta
-        self.is_causal = True
-
-        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=config.attention_bias)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
-        self._init_rope()
-
-    def _init_rope(self):
-        if self.config.rope_scaling is None:
-            self.rotary_emb = MiniCPMRotaryEmbedding(
-                self.head_dim,
-                max_position_embeddings=self.max_position_embeddings,
-                base=self.rope_theta,
-            )
-        else:
-            scaling_type = self.config.rope_scaling["type"]
-            scaling_factor = self.config.rope_scaling["factor"]
-            if scaling_type == "linear":
-                self.rotary_emb = MiniCPMLinearScalingRotaryEmbedding(
-                    self.head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                )
-            elif scaling_type == "dynamic":
-                self.rotary_emb = MiniCPMDynamicNTKScalingRotaryEmbedding(
-                    self.head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                )
-            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[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-
-        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:
-            if self.layer_idx is None:
-                raise ValueError(
-                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
-                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
-                    "with a layer index."
-                )
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-        cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
-
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        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_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
-        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)
-
-        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 MiniCPMFlashAttention2(MiniCPMAttention):
-    """
-    MiniCPM flash attention module. This module inherits from `MiniCPMAttention` as the weights of the module stays
-    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
-    flash attention and deal with padding tokens in case the input contains any of them.
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
-        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
-        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        # MiniCPMFlashAttention2 attention does not support output_attentions
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-
-            # overwrite attention_mask with padding_mask
-            attention_mask = kwargs.pop("padding_mask")
-
-        output_attentions = False
-
-        bsz, q_len, _ = hidden_states.size()
-
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        # Flash attention requires the input to have the shape
-        # batch_size x seq_length x head_dim x hidden_dim
-        # therefore we just need to keep the original shape
-        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.get_usable_length(kv_seq_len, self.layer_idx)
-        cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
-        # to be able to avoid many of these transpose/reshape/view.
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        dropout_rate = self.attention_dropout if self.training else 0.0
-
-        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
-        # therefore the input hidden states gets silently casted in float32. Hence, we need
-        # cast them back in the correct dtype just to be sure everything works as expected.
-        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
-        # in fp32. (MiniCPMRMSNorm handles it correctly)
-
-        input_dtype = query_states.dtype
-        if input_dtype == torch.float32:
-            # Handle the case where the model is quantized
-            if hasattr(self.config, "_pre_quantization_dtype"):
-                target_dtype = self.config._pre_quantization_dtype
-            else:
-                target_dtype = self.q_proj.weight.dtype
-
-            logger.warning_once(
-                f"The input hidden states seems to be silently casted in float32, this might be related to"
-                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
-                f" {target_dtype}."
-            )
-
-            query_states = query_states.to(target_dtype)
-            key_states = key_states.to(target_dtype)
-            value_states = value_states.to(target_dtype)
-
-        attn_output = self._flash_attention_forward(
-            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
-        )
-
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-    def _flash_attention_forward(
-        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
-    ):
-        """
-        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
-        first unpad the input, then computes the attention scores and pad the final attention scores.
-
-        Args:
-            query_states (`torch.Tensor`):
-                Input query states to be passed to Flash Attention API
-            key_states (`torch.Tensor`):
-                Input key states to be passed to Flash Attention API
-            value_states (`torch.Tensor`):
-                Input value states to be passed to Flash Attention API
-            attention_mask (`torch.Tensor`):
-                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
-                position of padding tokens and 1 for the position of non-padding tokens.
-            dropout (`int`, *optional*):
-                Attention dropout
-            softmax_scale (`float`, *optional*):
-                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
-        """
-        if not self._flash_attn_uses_top_left_mask:
-            causal = self.is_causal
-        else:
-            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in MiniCPMFlashAttention2 __init__.
-            causal = self.is_causal and query_length != 1
-        # Contains at least one padding token in the sequence
-        if attention_mask is not None:
-            batch_size = query_states.shape[0]
-            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
-                query_states, key_states, value_states, attention_mask, query_length
-            )
-
-            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
-            attn_output_unpad = flash_attn_varlen_func(
-                query_states,
-                key_states,
-                value_states,
-                cu_seqlens_q=cu_seqlens_q,
-                cu_seqlens_k=cu_seqlens_k,
-                max_seqlen_q=max_seqlen_in_batch_q,
-                max_seqlen_k=max_seqlen_in_batch_k,
-                dropout_p=dropout,
-                softmax_scale=softmax_scale,
-                causal=causal,
-            )
-
-            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
-        else:
-            attn_output = flash_attn_func(
-                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
-            )
-
-        return attn_output
-
-    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
-        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
-
-        key_layer = index_first_axis(
-            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
-        )
-        value_layer = index_first_axis(
-            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
-        )
-        if query_length == kv_seq_len:
-            query_layer = index_first_axis(
-                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
-            )
-            cu_seqlens_q = cu_seqlens_k
-            max_seqlen_in_batch_q = max_seqlen_in_batch_k
-            indices_q = indices_k
-        elif query_length == 1:
-            max_seqlen_in_batch_q = 1
-            cu_seqlens_q = torch.arange(
-                batch_size + 1, dtype=torch.int32, device=query_layer.device
-            )  # There is a memcpy here, that is very bad.
-            indices_q = cu_seqlens_q[:-1]
-            query_layer = query_layer.squeeze(1)
-        else:
-            # The -q_len: slice assumes left padding.
-            attention_mask = attention_mask[:, -query_length:]
-            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
-
-        return (
-            query_layer,
-            key_layer,
-            value_layer,
-            indices_q,
-            (cu_seqlens_q, cu_seqlens_k),
-            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
-        )
-
-
-class MiniCPMSdpaAttention(MiniCPMAttention):
-    """
-    MiniCPM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
-    `MiniCPMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
-    SDPA API.
-    """
-
-    # Adapted from MiniCPMAttention.forward
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if output_attentions:
-            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
-            logger.warning_once(
-                "MiniCPMModel is using MiniCPMSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
-                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
-            )
-            return super().forward(
-                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,
-            )
-
-        bsz, q_len, _ = hidden_states.size()
-
-        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.get_usable_length(kv_seq_len, self.layer_idx)
-        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)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        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()}"
-                )
-
-        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
-        # Reference: https://github.com/pytorch/pytorch/issues/112577.
-        if query_states.device.type == "cuda" and attention_mask is not None:
-            query_states = query_states.contiguous()
-            key_states = key_states.contiguous()
-            value_states = value_states.contiguous()
-
-        attn_output = torch.nn.functional.scaled_dot_product_attention(
-            query_states,
-            key_states,
-            value_states,
-            attn_mask=attention_mask,
-            dropout_p=self.attention_dropout if self.training else 0.0,
-            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
-            is_causal=self.is_causal and attention_mask is None and q_len > 1,
-        )
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-        attn_output = self.o_proj(attn_output)
-
-        return attn_output, None, past_key_value
-
-
-MINICPM_ATTENTION_CLASSES = {
-    "eager": MiniCPMAttention,
-    "flash_attention_2": MiniCPMFlashAttention2,
-    "sdpa": MiniCPMSdpaAttention,
-}
-
-
-class MiniCPMDecoderLayer(nn.Module):
-    def __init__(self, config: MiniCPMConfig, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.self_attn = MINICPM_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
-
-        self.mlp = MiniCPMMLP(config)
-        self.input_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-        self.scale_depth = config.scale_depth
-        self.num_hidden_layers = config.num_hidden_layers
-
-    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,
-        **kwargs,
-    ) -> 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_size, sequence_length)` if flash attention is used or `(batch_size, 1,
-                query_sequence_length, key_sequence_length)` if default attention is used.
-            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
-        """
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-
-        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,
-            **kwargs,
-        )
-        
-        hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        if use_cache:
-            outputs += (present_key_value,)
-
-        return outputs
-
-
-MINICPM_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 ([`MiniCPMConfig`]):
-            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 MiniCPM Model outputting raw hidden-states without any specific head on top.",
-    MINICPM_START_DOCSTRING,
-)
-class MiniCPMPreTrainedModel(PreTrainedModel):
-    config_class = MiniCPMConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["MiniCPMDecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_cache_class = True
-
-    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_()
-
-
-MINICPM_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 `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 (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
-            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
-            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
-
-            Two formats are allowed:
-            - a [`~cache_utils.Cache`] instance;
-            - 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)`). This is also known as the legacy
-            cache format.
-
-            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
-            legacy cache format will be returned.
-
-            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
-            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `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 MiniCPM Model outputting raw hidden-states without any specific head on top.",
-    MINICPM_START_DOCSTRING,
-)
-class MiniCPMModel(MiniCPMPreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`]
-
-    Args:
-        config: MiniCPMConfig
-    """
-
-    def __init__(self, config: MiniCPMConfig):
-        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(
-            [MiniCPMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self._use_sdpa = config._attn_implementation == "sdpa"
-        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
-
-        self.norm = MiniCPMRMSNorm(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
-
-    @add_start_docstrings_to_model_forward(MINICPM_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 input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape[:2]
-        elif inputs_embeds is not None:
-            batch_size, seq_length = inputs_embeds.shape[:2]
-        else:
-            raise ValueError("You have to specify either input_ids or 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
-
-        past_key_values_length = 0
-        if use_cache:
-            use_legacy_cache = not isinstance(past_key_values, Cache)
-            if use_legacy_cache:
-                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            past_key_values_length = past_key_values.get_usable_length(seq_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)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids) * self.config.scale_emb
-
-
-        if self._use_flash_attention_2:
-            # 2d mask is passed through the layers
-            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
-        elif self._use_sdpa and not output_attentions:
-            # output_attentions=True can not be supported when using SDPA, and we fall back on
-            # the manual implementation that requires a 4D causal mask in all cases.
-            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
-                attention_mask,
-                (batch_size, seq_length),
-                inputs_embeds,
-                past_key_values_length,
-            )
-        else:
-            # 4d mask is passed through the layers
-            attention_mask = _prepare_4d_causal_attention_mask(
-                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
-            )
-
-        # embed positions
-        hidden_states = inputs_embeds
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = None
-
-        for decoder_layer in self.layers:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-                layer_outputs = self._gradient_checkpointing_func(
-                    decoder_layer.__call__,
-                    hidden_states,
-                    attention_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    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 = None
-        if use_cache:
-            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
-        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 MiniCPMForCausalLM(MiniCPMPreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = MiniCPMModel(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(MINICPM_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, MiniCPMForCausalLM
-
-        >>> model = MiniCPMForCausalLM.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 / (self.config.hidden_size / self.config.dim_model_base))
-        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 is not None:
-            if isinstance(past_key_values, Cache):
-                cache_length = past_key_values.get_seq_length()
-                past_length = past_key_values.seen_tokens
-                max_cache_length = past_key_values.get_max_length()
-            else:
-                cache_length = past_length = past_key_values[0][0].shape[2]
-                max_cache_length = None
-
-            # Keep only the unprocessed tokens:
-            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
-            # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
-            # input)
-            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
-                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
-            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
-            # input_ids based on the past_length.
-            elif past_length < input_ids.shape[1]:
-                input_ids = input_ids[:, past_length:]
-            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
-            else:
-                remove_prefix_length = input_ids.shape[1] - 1
-                input_ids = input_ids[:, remove_prefix_length:]
-            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
-            if (
-                max_cache_length is not None
-                and attention_mask is not None
-                and cache_length + input_ids.shape[1] > max_cache_length
-            ):
-                attention_mask = attention_mask[:, -max_cache_length:]
-
-        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[:, -input_ids.shape[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
-    
-    @torch.inference_mode()
-    def chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = "user",
-             max_length: int = 4096, num_beams=1, do_sample=True, top_p=0.8, temperature=0.3, logits_processor=None,
-             **kwargs):
-        if history is None:
-            history = []
-        if logits_processor:
-            gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
-                        "temperature": temperature, "logits_processor": logits_processor, **kwargs}
-        else:
-            gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
-                        "temperature": temperature, "logits_processor": logits_processor, **kwargs}
-        
-        history.append({"role": role, "content": query})
-        history_str = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=False)
-        inputs = tokenizer(history_str, return_tensors='pt').to(self.device)
-        outputs = self.generate(**inputs, **gen_kwargs)
-        outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
-        response = tokenizer.decode(outputs)
-        pattern = re.compile(r".*?(?=<AI>|<用户>)", re.DOTALL)
-        matches = pattern.findall(response)
-        if len(matches) > 0:
-            response = matches[0]
-        history.append({"role": "assistant", "content": response})
-        return response, history
-
-
-@add_start_docstrings(
-    """
-    The MiniCPM Model transformer with a sequence classification head on top (linear layer).
-
-    [`MiniCPMForSequenceClassification`] 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).
-    """,
-    MINICPM_START_DOCSTRING,
-)
-class MiniCPMForSequenceClassification(MiniCPMPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = MiniCPMModel(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(MINICPM_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).int().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,
-        )

bunny/model/language_model/phi/__init__.py

@@ -1,69 +0,0 @@
-# Copyright 2023 Microsoft and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-from typing import TYPE_CHECKING
-
-from transformers.utils import (
-    OptionalDependencyNotAvailable,
-    _LazyModule,
-    is_sentencepiece_available,
-    is_tokenizers_available,
-    is_torch_available,
-)
-
-
-_import_structure = {
-    "configuration_phi": ["PHI_PRETRAINED_CONFIG_ARCHIVE_MAP", "PhiConfig"],
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_phi"] = [
-        "PHI_PRETRAINED_MODEL_ARCHIVE_LIST",
-        "PhiPreTrainedModel",
-        "PhiModel",
-        "PhiForCausalLM",
-        "PhiForSequenceClassification",
-        "PhiForTokenClassification",
-    ]
-
-
-if TYPE_CHECKING:
-    from .configuration_phi import PHI_PRETRAINED_CONFIG_ARCHIVE_MAP, PhiConfig
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_phi import (
-            PHI_PRETRAINED_MODEL_ARCHIVE_LIST,
-            PhiForCausalLM,
-            PhiForSequenceClassification,
-            PhiForTokenClassification,
-            PhiModel,
-            PhiPreTrainedModel,
-        )
-
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

bunny/model/language_model/phi/configuration_phi.py

@@ -1,195 +0,0 @@
-# coding=utf-8
-# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-""" Phi model configuration"""
-
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-PHI_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "microsoft/phi-1": "https://huggingface.co/microsoft/phi-1/resolve/main/config.json",
-    "microsoft/phi-1_5": "https://huggingface.co/microsoft/phi-1_5/resolve/main/config.json",
-    "microsoft/phi-2": "https://huggingface.co/microsoft/phi-2/resolve/main/config.json",
-}
-
-
-class PhiConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi
-    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 Phi
-    [microsoft/phi-1](https://huggingface.co/microsoft/phi-1).
-
-    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 51200):
-            Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`PhiModel`].
-        hidden_size (`int`, *optional*, defaults to 2048):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 8192):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 24):
-            Number of hidden layers in the Transformer decoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer decoder.
-        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`.
-        resid_pdrop (`float`, *optional*, defaults to 0.0):
-            Dropout probability for mlp outputs.
-        embd_pdrop (`int`, *optional*, defaults to 0.0):
-            The dropout ratio for the embeddings.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio after computing the attention scores.
-        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
-            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. Phi-1 and Phi-1.5 supports up to 2048
-            tokens.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
-            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`. Whether to tie weight embeddings or not.
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether to tie weight embeddings
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two 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/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
-            is an experimental feature, subject to breaking API changes in future versions.
-        partial_rotary_factor (`float`, *optional*, defaults to 0.5):
-            Percentage of the query and keys which will have rotary embedding.
-        qk_layernorm (`bool`, *optional*, defaults to `False`):
-            Whether or not to normalize the Queries and Keys after projecting the hidden states.
-        bos_token_id (`int`, *optional*, defaults to 1):
-            Denotes beginning of sequences token id.
-        eos_token_id (`int`, *optional*, defaults to 2):
-            Denotes end of sequences token id.
-
-    Example:
-
-    ```python
-    >>> from transformers import PhiModel, PhiConfig
-
-    >>> # Initializing a Phi-1 style configuration
-    >>> configuration = PhiConfig.from_pretrained("microsoft/phi-1")
-
-    >>> # Initializing a model from the configuration
-    >>> model = PhiModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "phi"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=51200,
-        hidden_size=2048,
-        intermediate_size=8192,
-        num_hidden_layers=24,
-        num_attention_heads=32,
-        num_key_value_heads=None,
-        resid_pdrop=0.0,
-        embd_pdrop=0.0,
-        attention_dropout=0.0,
-        hidden_act="gelu_new",
-        max_position_embeddings=2048,
-        initializer_range=0.02,
-        layer_norm_eps=1e-5,
-        use_cache=True,
-        tie_word_embeddings=False,
-        rope_theta=10000.0,
-        rope_scaling=None,
-        partial_rotary_factor=0.5,
-        qk_layernorm=False,
-        bos_token_id=1,
-        eos_token_id=2,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.resid_pdrop = resid_pdrop
-        self.embd_pdrop = embd_pdrop
-        self.attention_dropout = attention_dropout
-        self.hidden_act = hidden_act
-        self.max_position_embeddings = max_position_embeddings
-        self.initializer_range = initializer_range
-        self.layer_norm_eps = layer_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self.partial_rotary_factor = partial_rotary_factor
-        self.qk_layernorm = qk_layernorm
-        self._rope_scaling_validation()
-
-        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.llama.configuration_llama.LlamaConfig._rope_scaling_validation
-    def _rope_scaling_validation(self):
-        """
-        Validate the `rope_scaling` configuration.
-        """
-        if self.rope_scaling is None:
-            return
-
-        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
-            raise ValueError(
-                "`rope_scaling` must be a dictionary with with two fields, `type` 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"]:
-            raise ValueError(
-                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], 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 a float > 1, got {rope_scaling_factor}")

bunny/model/language_model/phi/modeling_phi.py

@@ -1,1374 +0,0 @@
-# coding=utf-8
-# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-""" PyTorch Phi model."""
-
-
-import math
-from typing import List, Optional, Tuple, Union
-
-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.cache_utils import Cache, DynamicCache
-from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    SequenceClassifierOutputWithPast,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_flash_attn_2_available,
-    is_flash_attn_greater_or_equal_2_10,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_phi import PhiConfig
-
-
-if is_flash_attn_2_available():
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "microsoft/phi-1"
-_CONFIG_FOR_DOC = "PhiConfig"
-
-PHI_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "microsoft/phi-1",
-    "microsoft/phi-1_5",
-    "microsoft/phi-2",
-    # See all Phi models at https://huggingface.co/models?filter=phi
-]
-
-
-# Copied from transformers.models.llama.modeling_llama._get_unpad_data
-def _get_unpad_data(attention_mask):
-    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max().item()
-    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-
-
-# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Phi
-class PhiRotaryEmbedding(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, dtype=torch.int64).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=torch.int64).type_as(self.inv_freq)
-
-        freqs = torch.outer(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().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().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[:seq_len].to(dtype=x.dtype),
-            self.sin_cached[:seq_len].to(dtype=x.dtype),
-        )
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Phi
-class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
-    """PhiRotaryEmbedding 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 = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
-        t = t / self.scaling_factor
-
-        freqs = torch.outer(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().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Phi
-class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
-    """PhiRotaryEmbedding 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, dtype=torch.int64).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=torch.int64).type_as(self.inv_freq)
-
-        freqs = torch.outer(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().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
-
-
-# Copied from transformers.models.llama.modeling_llama.rotate_half
-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)
-
-
-# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`):
-            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
-            used to pass offsetted position ids when working with a KV-cache.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
-    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Phi
-class PhiMLP(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.activation_fn = ACT2FN[config.hidden_act]
-        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
-        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.fc1(hidden_states)
-        hidden_states = self.activation_fn(hidden_states)
-        hidden_states = self.fc2(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
-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 PhiAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: PhiConfig, layer_idx: Optional[int] = None):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        if layer_idx is None:
-            logger.warning_once(
-                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
-                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
-                "when creating this class."
-            )
-
-        self.attention_dropout = config.attention_dropout
-        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
-        self.rope_theta = config.rope_theta
-        self.partial_rotary_factor = config.partial_rotary_factor
-        self.is_causal = True
-
-        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=True)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
-        self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=True)
-
-        self.qk_layernorm = config.qk_layernorm
-        if self.qk_layernorm:
-            self.q_layernorm = nn.LayerNorm(
-                config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True
-            )
-            self.k_layernorm = nn.LayerNorm(
-                config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True
-            )
-
-        self._init_rope()
-
-    def _init_rope(self):
-        if self.config.rope_scaling is None:
-            self.rotary_emb = PhiRotaryEmbedding(
-                int(self.partial_rotary_factor * self.head_dim),
-                max_position_embeddings=self.max_position_embeddings,
-                base=self.rope_theta,
-            )
-        else:
-            scaling_type = self.config.rope_scaling["type"]
-            scaling_factor = self.config.rope_scaling["factor"]
-            if scaling_type == "linear":
-                self.rotary_emb = PhiLinearScalingRotaryEmbedding(
-                    int(self.partial_rotary_factor * self.head_dim),
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                )
-            elif scaling_type == "dynamic":
-                self.rotary_emb = PhiDynamicNTKScalingRotaryEmbedding(
-                    int(self.partial_rotary_factor * self.head_dim),
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                )
-            else:
-                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = 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()
-
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        if self.qk_layernorm:
-            query_states = self.q_layernorm(query_states)
-            key_states = self.k_layernorm(key_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:
-            if self.layer_idx is None:
-                raise ValueError(
-                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
-                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
-                    "with a layer index."
-                )
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-
-        # Partial rotary embedding
-        query_rot, query_pass = (
-            query_states[..., : self.rotary_emb.dim],
-            query_states[..., self.rotary_emb.dim :],
-        )
-        key_rot, key_pass = (
-            key_states[..., : self.rotary_emb.dim],
-            key_states[..., self.rotary_emb.dim :],
-        )
-        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
-        query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
-
-        # [batch_size, seq_length, num_heads, head_dim]
-        query_states = torch.cat((query_rot, query_pass), dim=-1)
-        key_states = torch.cat((key_rot, key_pass), dim=-1)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        # Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow
-        attn_weights = torch.matmul(
-            query_states.to(torch.float32), key_states.to(torch.float32).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(value_states.dtype)
-        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
-
-        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)
-
-        attn_output = self.dense(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-class PhiFlashAttention2(PhiAttention):
-    """
-    Phi flash attention module. This module inherits from `PhiAttention` as the weights of the module stays
-    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
-    flash attention and deal with padding tokens in case the input contains any of them.
-    """
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
-        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
-        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        # PhiFlashAttention2 attention does not support output_attentions
-
-        output_attentions = False
-
-        bsz, q_len, _ = hidden_states.size()
-
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        if self.qk_layernorm:
-            query_states = self.q_layernorm(query_states)
-            key_states = self.k_layernorm(key_states)
-
-        # Flash attention requires the input to have the shape
-        # batch_size x seq_length x head_dim x hidden_dim
-        # therefore we just need to keep the original shape
-        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.get_usable_length(kv_seq_len, self.layer_idx)
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-
-        # Partial rotary embedding
-        query_rot, query_pass = (
-            query_states[..., : self.rotary_emb.dim],
-            query_states[..., self.rotary_emb.dim :],
-        )
-        key_rot, key_pass = (
-            key_states[..., : self.rotary_emb.dim],
-            key_states[..., self.rotary_emb.dim :],
-        )
-        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
-        query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
-
-        # [batch_size, seq_length, num_heads, head_dim]
-        query_states = torch.cat((query_rot, query_pass), dim=-1)
-        key_states = torch.cat((key_rot, key_pass), dim=-1)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
-        # to be able to avoid many of these transpose/reshape/view.
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        attn_dropout = self.attention_dropout if self.training else 0.0
-
-        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
-        # therefore the input hidden states gets silently casted in float32. Hence, we need
-        # cast them back in the correct dtype just to be sure everything works as expected.
-        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
-        # in fp32.
-
-        if query_states.dtype == torch.float32:
-            if torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
-            # Handle the case where the model is quantized
-            elif hasattr(self.config, "_pre_quantization_dtype"):
-                target_dtype = self.config._pre_quantization_dtype
-            else:
-                target_dtype = self.q_proj.weight.dtype
-
-            logger.warning_once(
-                f"The input hidden states seems to be silently casted in float32, this might be related to"
-                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
-                f" {target_dtype}."
-            )
-
-            query_states = query_states.to(target_dtype)
-            key_states = key_states.to(target_dtype)
-            value_states = value_states.to(target_dtype)
-
-        attn_output = self._flash_attention_forward(
-            query_states, key_states, value_states, attention_mask, q_len, dropout=attn_dropout, softmax_scale=None
-        )
-
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
-        attn_output = self.dense(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
-    def _flash_attention_forward(
-        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
-    ):
-        """
-        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
-        first unpad the input, then computes the attention scores and pad the final attention scores.
-
-        Args:
-            query_states (`torch.Tensor`):
-                Input query states to be passed to Flash Attention API
-            key_states (`torch.Tensor`):
-                Input key states to be passed to Flash Attention API
-            value_states (`torch.Tensor`):
-                Input value states to be passed to Flash Attention API
-            attention_mask (`torch.Tensor`):
-                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
-                position of padding tokens and 1 for the position of non-padding tokens.
-            dropout (`int`, *optional*):
-                Attention dropout
-            softmax_scale (`float`, *optional*):
-                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
-        """
-        if not self._flash_attn_uses_top_left_mask:
-            causal = self.is_causal
-        else:
-            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
-            causal = self.is_causal and query_length != 1
-
-        # Contains at least one padding token in the sequence
-        if attention_mask is not None:
-            batch_size = query_states.shape[0]
-            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
-                query_states, key_states, value_states, attention_mask, query_length
-            )
-
-            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
-
-            attn_output_unpad = flash_attn_varlen_func(
-                query_states,
-                key_states,
-                value_states,
-                cu_seqlens_q=cu_seqlens_q,
-                cu_seqlens_k=cu_seqlens_k,
-                max_seqlen_q=max_seqlen_in_batch_q,
-                max_seqlen_k=max_seqlen_in_batch_k,
-                dropout_p=dropout,
-                softmax_scale=softmax_scale,
-                causal=causal,
-            )
-
-            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
-        else:
-            attn_output = flash_attn_func(
-                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
-            )
-
-        return attn_output
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
-    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
-        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
-
-        key_layer = index_first_axis(
-            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
-        )
-        value_layer = index_first_axis(
-            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
-        )
-        if query_length == kv_seq_len:
-            query_layer = index_first_axis(
-                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
-            )
-            cu_seqlens_q = cu_seqlens_k
-            max_seqlen_in_batch_q = max_seqlen_in_batch_k
-            indices_q = indices_k
-        elif query_length == 1:
-            max_seqlen_in_batch_q = 1
-            cu_seqlens_q = torch.arange(
-                batch_size + 1, dtype=torch.int32, device=query_layer.device
-            )  # There is a memcpy here, that is very bad.
-            indices_q = cu_seqlens_q[:-1]
-            query_layer = query_layer.squeeze(1)
-        else:
-            # The -q_len: slice assumes left padding.
-            attention_mask = attention_mask[:, -query_length:]
-            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
-
-        return (
-            query_layer,
-            key_layer,
-            value_layer,
-            indices_q,
-            (cu_seqlens_q, cu_seqlens_k),
-            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
-        )
-
-
-PHI_ATTENTION_CLASSES = {
-    "eager": PhiAttention,
-    "flash_attention_2": PhiFlashAttention2,
-}
-
-
-class PhiDecoderLayer(nn.Module):
-    def __init__(self, config: PhiConfig, layer_idx: int):
-        super().__init__()
-        self.self_attn = PHI_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
-        self.mlp = PhiMLP(config)
-        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.resid_dropout = nn.Dropout(config.resid_pdrop)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    ) -> 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.
-            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)
-            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
-        attn_outputs, 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,
-        )
-        attn_outputs = self.resid_dropout(attn_outputs)
-
-        feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
-        hidden_states = attn_outputs + feed_forward_hidden_states + residual
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        if use_cache:
-            outputs += (present_key_value,)
-
-        return outputs
-
-
-PHI_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 ([`PhiConfig`]):
-            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 Phi Model outputting raw hidden-states without any specific head on top.",
-    PHI_START_DOCSTRING,
-)
-class PhiPreTrainedModel(PreTrainedModel):
-    config_class = PhiConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["PhiDecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = True
-    _supports_cache_class = True
-
-    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_()
-
-
-PHI_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 `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 (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
-            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
-            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
-
-            Two formats are allowed:
-            - a [`~cache_utils.Cache`] instance;
-            - 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)`). This is also known as the legacy
-            cache format.
-
-            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
-            legacy cache format will be returned.
-
-            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
-            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `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 Phi Model outputting raw hidden-states without any specific head on top.",
-    PHI_START_DOCSTRING,
-)
-class PhiModel(PhiPreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`]
-
-    Args:
-        config: PhiConfig
-    """
-
-    def __init__(self, config: PhiConfig):
-        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.embed_dropout = nn.Dropout(config.embd_pdrop)
-        self.layers = nn.ModuleList(
-            [PhiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
-
-        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
-
-    @add_start_docstrings_to_model_forward(PHI_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 input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape[:2]
-        elif inputs_embeds is not None:
-            batch_size, seq_length = inputs_embeds.shape[:2]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        past_key_values_length = 0
-
-        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
-
-        if use_cache:
-            use_legacy_cache = not isinstance(past_key_values, Cache)
-            if use_legacy_cache:
-                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            past_key_values_length = past_key_values.get_usable_length(seq_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)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        inputs_embeds = self.embed_dropout(inputs_embeds)
-
-        # Attention mask.
-        if self._use_flash_attention_2:
-            # 2d mask is passed through the layers
-            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
-        else:
-            # 4d mask is passed through the layers
-            attention_mask = _prepare_4d_causal_attention_mask(
-                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
-            )
-
-        hidden_states = inputs_embeds
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = None
-
-        for decoder_layer in self.layers:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-                layer_outputs = self._gradient_checkpointing_func(
-                    decoder_layer.__call__,
-                    hidden_states,
-                    attention_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    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.final_layernorm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        next_cache = None
-        if use_cache:
-            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
-        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 PhiForCausalLM(PhiPreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = PhiModel(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
-    def get_decoder(self):
-        return self.model
-
-    @add_start_docstrings_to_model_forward(PHI_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, PhiForCausalLM
-
-        >>> model = PhiForCausalLM.from_pretrained("microsoft/phi-1")
-        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1")
-
-        >>> prompt = "This is an example script ."
-        >>> 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]
-        'This is an example script .\n\n\n\nfrom typing import List\n\ndef find_most_common_letter(words: List[str'
-        ```"""
-
-        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)
-        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,
-        )
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.prepare_inputs_for_generation
-    def prepare_inputs_for_generation(
-        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
-    ):
-        if past_key_values is not None:
-            if isinstance(past_key_values, Cache):
-                cache_length = past_key_values.get_seq_length()
-                past_length = past_key_values.seen_tokens
-                max_cache_length = past_key_values.get_max_length()
-            else:
-                cache_length = past_length = past_key_values[0][0].shape[2]
-                max_cache_length = None
-
-            # Keep only the unprocessed tokens:
-            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
-            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
-            # input)
-            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
-                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
-            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
-            # input_ids based on the past_length.
-            elif past_length < input_ids.shape[1]:
-                input_ids = input_ids[:, past_length:]
-            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
-            else:
-                remove_prefix_length = input_ids.shape[1] - 1
-                input_ids = input_ids[:, remove_prefix_length:]
-            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
-            if (
-                max_cache_length is not None
-                and attention_mask is not None
-                and cache_length + input_ids.shape[1] > max_cache_length
-            ):
-                attention_mask = attention_mask[:, -max_cache_length:]
-
-        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[:, -input_ids.shape[1] :]
-
-        if past_key_value := getattr(self.model.layers[0].self_attn, "past_key_value", None):
-            # generation with static cache
-            seen_tokens = past_key_value.get_seq_length()
-            input_ids = input_ids[:, seen_tokens:]
-            position_ids = position_ids[:, seen_tokens:]
-
-        # 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
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
-    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 PhiModel with a sequence classification head on top (linear layer).
-
-    [`PhiForSequenceClassification`] 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).
-    """,
-    PHI_START_DOCSTRING,
-)
-# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with LLAMA->PHI,Llama->Phi with self.transformer->self.model, transformer_outputs->model_outputs
-class PhiForSequenceClassification(PhiPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = PhiModel(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(PHI_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
-
-        model_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 = model_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:
-                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
-                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
-                sequence_lengths = sequence_lengths % input_ids.shape[-1]
-                sequence_lengths = sequence_lengths.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,) + model_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=model_outputs.past_key_values,
-            hidden_states=model_outputs.hidden_states,
-            attentions=model_outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    PhiModel with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
-    Named-Entity-Recognition (NER) tasks.
-    """,
-    PHI_START_DOCSTRING,
-)
-# Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with MPT->PHI,Mpt->Phi,self.transformer->self.model,transformer_outputs->model_outputs
-class PhiForTokenClassification(PhiPreTrainedModel):
-    def __init__(self, config: PhiConfig):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.model = PhiModel(config)
-        if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
-            classifier_dropout = config.classifier_dropout
-        elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
-            classifier_dropout = config.hidden_dropout
-        else:
-            classifier_dropout = 0.1
-        self.dropout = nn.Dropout(classifier_dropout)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        **deprecated_arguments,
-    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
-        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
-
-        model_outputs = self.model(
-            input_ids,
-            past_key_values=past_key_values,
-            attention_mask=attention_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 = model_outputs[0]
-        hidden_states = self.dropout(hidden_states)
-        logits = self.classifier(hidden_states)
-
-        loss = None
-        if labels is not None:
-            # move labels to correct device to enable model parallelism
-            labels = labels.to(logits.device)
-            batch_size, seq_length = labels.shape
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(
-                logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
-            )
-
-        if not return_dict:
-            output = (logits,) + model_outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=model_outputs.hidden_states,
-            attentions=model_outputs.attentions,
-        )

bunny/model/language_model/phi3/__init__.py

@@ -1,69 +0,0 @@
-# Copyright 2024 Microsoft and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-from typing import TYPE_CHECKING
-
-from transformers.utils import (
-    OptionalDependencyNotAvailable,
-    _LazyModule,
-    is_sentencepiece_available,
-    is_tokenizers_available,
-    is_torch_available,
-)
-
-
-_import_structure = {
-    "configuration_phi3": ["PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP", "Phi3Config"],
-}
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_phi3"] = [
-        "PHI3_PRETRAINED_MODEL_ARCHIVE_LIST",
-        "Phi3PreTrainedModel",
-        "Phi3Model",
-        "Phi3ForCausalLM",
-        "Phi3ForSequenceClassification",
-        "Phi3ForTokenClassification",
-    ]
-
-
-if TYPE_CHECKING:
-    from .configuration_phi3 import PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP, Phi3Config
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_phi3 import (
-            PHI3_PRETRAINED_MODEL_ARCHIVE_LIST,
-            Phi3ForCausalLM,
-            Phi3ForSequenceClassification,
-            Phi3ForTokenClassification,
-            Phi3Model,
-            Phi3PreTrainedModel,
-        )
-
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

bunny/model/language_model/phi3/configuration_phi3.py

@@ -1,213 +0,0 @@
-# coding=utf-8
-# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-""" Phi-3 model configuration"""
-
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "microsoft/Phi-3-mini-4k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json",
-    "microsoft/Phi-3-mini-128k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json",
-}
-
-
-class Phi3Config(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
-    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
-    [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
-
-    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 32064):
-            Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`Phi3Model`].
-        hidden_size (`int`, *optional*, defaults to 3072):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 8192):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer decoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer decoder.
-        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`.
-        resid_pdrop (`float`, *optional*, defaults to 0.0):
-            Dropout probability for mlp outputs.
-        embd_pdrop (`int`, *optional*, defaults to 0.0):
-            The dropout ratio for the embeddings.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio after computing the attention scores.
-        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 4096):
-            The maximum sequence length that this model might ever be used with.
-        original_max_position_embeddings (`int`, *optional*, defaults to 4096):
-            The maximum sequence length that this model was trained with. This is used to determine the size of the
-            original RoPE embeddings when using long scaling.
-        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-05):
-            The epsilon value used for the RMSNorm.
-        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`. Whether to tie weight embeddings or not.
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether to tie weight embeddings
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        rope_scaling (`dict`, *optional*):
-            The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
-            contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be either `su` or `yarn` and
-            the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
-            divided by the number of attention heads divided by 2.
-        bos_token_id (`int`, *optional*, defaults to 1):
-            The id of the "beginning-of-sequence" token.
-        eos_token_id (`int`, *optional*, defaults to 32000):
-            The id of the "end-of-sequence" token.
-        pad_token_id (`int`, *optional*, defaults to 32000):
-            The id of the padding token.
-        sliding_window (`int`, *optional*):
-            Sliding window attention window size. If `None`, no sliding window is applied.
-
-    Example:
-
-    ```python
-    >>> from transformers import Phi3Model, Phi3Config
-
-    >>> # Initializing a Phi-3 style configuration
-    >>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
-
-    >>> # Initializing a model from the configuration
-    >>> model = Phi3Model(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "phi3"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=32064,
-        hidden_size=3072,
-        intermediate_size=8192,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=None,
-        resid_pdrop=0.0,
-        embd_pdrop=0.0,
-        attention_dropout=0.0,
-        hidden_act="silu",
-        max_position_embeddings=4096,
-        original_max_position_embeddings=4096,
-        initializer_range=0.02,
-        rms_norm_eps=1e-5,
-        use_cache=True,
-        tie_word_embeddings=False,
-        rope_theta=10000.0,
-        rope_scaling=None,
-        bos_token_id=1,
-        eos_token_id=32000,
-        pad_token_id=32000,
-        sliding_window=None,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.resid_pdrop = resid_pdrop
-        self.embd_pdrop = embd_pdrop
-        self.attention_dropout = attention_dropout
-        self.hidden_act = hidden_act
-        self.max_position_embeddings = max_position_embeddings
-        self.original_max_position_embeddings = original_max_position_embeddings
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.rope_scaling = rope_scaling
-        self._rope_scaling_validation()
-        self.sliding_window = sliding_window
-
-        super().__init__(
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            pad_token_id=pad_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) or len(self.rope_scaling) != 3:
-            raise ValueError(
-                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
-                f"got {self.rope_scaling}"
-            )
-        rope_scaling_type = self.rope_scaling.get("type", None)
-        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
-        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
-        if rope_scaling_type is None or rope_scaling_type not in ["su", "yarn"]:
-            raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}")
-        if not (
-            isinstance(rope_scaling_short_factor, list)
-            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
-        ):
-            raise ValueError(
-                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
-            )
-        if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
-            raise ValueError(
-                f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
-            )
-        if not (
-            isinstance(rope_scaling_long_factor, list)
-            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
-        ):
-            raise ValueError(
-                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
-            )
-        if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
-            raise ValueError(
-                f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
-            )

bunny/model/language_model/phi3/modeling_phi3.py

@@ -1,1597 +0,0 @@
-# coding=utf-8
-# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-""" PyTorch Phi-3 model."""
-
-import inspect
-import math
-import warnings
-from typing import List, Optional, Tuple, Union
-
-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.cache_utils import Cache, DynamicCache
-from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    SequenceClassifierOutputWithPast,
-    TokenClassifierOutput,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_flash_attn_2_available,
-    is_flash_attn_greater_or_equal_2_10,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_phi3 import Phi3Config
-
-
-if is_flash_attn_2_available():
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
-
-    _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "microsoft/Phi-3-mini-4k-instruct"
-_CONFIG_FOR_DOC = "Phi3Config"
-
-PHI3_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "microsoft/Phi-3-mini-4k-instruct",
-    "microsoft/Phi-3-mini-128k-instruct",
-    # See all Phi-3 models at https://huggingface.co/models?filter=Phi-3
-]
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Phi3
-class Phi3RMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        Phi3RMSNorm 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)
-
-
-# Copied from transformers.models.llama.modeling_llama._get_unpad_data
-def _get_unpad_data(attention_mask):
-    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max().item()
-    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-
-
-# Copied from transformers.models.gemma.modeling_gemma.GemmaRotaryEmbedding with gemma->phi3, Gemma->Phi3
-class Phi3RotaryEmbedding(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
-        self.register_buffer("inv_freq", None, persistent=False)
-
-    @torch.no_grad()
-    def forward(self, x, position_ids, seq_len=None):
-        # x: [bs, num_attention_heads, seq_len, head_size]
-        if self.inv_freq is None:
-            self.inv_freq = 1.0 / (
-                self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim)
-            )
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
-        position_ids_expanded = position_ids[:, None, :].float()
-        # Force float32 since bfloat16 loses precision on long contexts
-        # See https://github.com/huggingface/transformers/pull/29285
-        device_type = x.device.type
-        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
-        with torch.autocast(device_type=device_type, enabled=False):
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-            cos = emb.cos()
-            sin = emb.sin()
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-
-class Phi3SuScaledRotaryEmbedding(Phi3RotaryEmbedding):
-    def __init__(self, dim, config, device=None):
-        super().__init__(dim, config.max_position_embeddings, config.rope_theta, device)
-
-        self.short_factor = config.rope_scaling["short_factor"]
-        self.long_factor = config.rope_scaling["long_factor"]
-        self.original_max_position_embeddings = config.original_max_position_embeddings
-
-    @torch.no_grad()
-    def forward(self, x, position_ids, seq_len=None):
-        seq_len = torch.max(position_ids) + 1
-        if seq_len > self.original_max_position_embeddings:
-            ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device)
-        else:
-            ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device)
-
-        inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim
-        self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape)
-
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
-        position_ids_expanded = position_ids[:, None, :].float()
-
-        # Force float32 since bfloat16 loses precision on long contexts
-        # See https://github.com/huggingface/transformers/pull/29285
-        device_type = x.device.type
-        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
-        with torch.autocast(device_type=device_type, enabled=False):
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-
-            scale = self.max_position_embeddings / self.original_max_position_embeddings
-            if scale <= 1.0:
-                scaling_factor = 1.0
-            else:
-                scaling_factor = math.sqrt(1 + math.log(scale) / math.log(self.original_max_position_embeddings))
-
-            cos = emb.cos() * scaling_factor
-            sin = emb.sin() * scaling_factor
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-
-class Phi3YarnScaledRotaryEmbedding(Phi3RotaryEmbedding):
-    def __init__(self, dim, config, device=None):
-        super().__init__(dim, config.max_position_embeddings, config.rope_theta, device)
-
-        self.short_factor = config.rope_scaling["short_factor"]
-        self.long_factor = config.rope_scaling["long_factor"]
-        self.original_max_position_embeddings = config.original_max_position_embeddings
-
-    @torch.no_grad()
-    def forward(self, x, position_ids, seq_len=None):
-        seq_len = torch.max(position_ids) + 1
-        if seq_len > self.original_max_position_embeddings:
-            ext_factors = torch.tensor(self.long_factor, dtype=torch.float32, device=x.device)
-        else:
-            ext_factors = torch.tensor(self.short_factor, dtype=torch.float32, device=x.device)
-
-        inv_freq_shape = torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim
-        self.inv_freq = 1.0 / (ext_factors * self.base**inv_freq_shape)
-
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
-        position_ids_expanded = position_ids[:, None, :].float()
-
-        # Force float32 since bfloat16 loses precision on long contexts
-        # See https://github.com/huggingface/transformers/pull/29285
-        device_type = x.device.type
-        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
-        with torch.autocast(device_type=device_type, enabled=False):
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-
-            scale = self.max_position_embeddings / self.original_max_position_embeddings
-            if scale <= 1.0:
-                scaling_factor = 1.0
-            else:
-                scaling_factor = 0.1 * math.log(scale) + 1.0
-
-            cos = emb.cos() * scaling_factor
-            sin = emb.sin() * scaling_factor
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-
-# Copied from transformers.models.llama.modeling_llama.rotate_half
-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)
-
-
-# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`, *optional*):
-            Deprecated and unused.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_dim)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-class Phi3MLP(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-
-        self.config = config
-        self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
-
-        self.activation_fn = ACT2FN[config.hidden_act]
-
-    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
-        up_states = self.gate_up_proj(hidden_states)
-
-        gate, up_states = up_states.chunk(2, dim=-1)
-        up_states = up_states * self.activation_fn(gate)
-
-        return self.down_proj(up_states)
-
-
-# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
-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 Phi3Attention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: Phi3Config, layer_idx: Optional[int] = None):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        if layer_idx is None:
-            logger.warning_once(
-                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
-                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
-                "when creating this class."
-            )
-
-        self.attention_dropout = config.attention_dropout
-        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
-        self.original_max_position_embeddings = config.original_max_position_embeddings
-        self.rope_theta = config.rope_theta
-        self.rope_scaling = config.rope_scaling
-        self.is_causal = True
-
-        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})."
-            )
-
-        op_size = self.num_heads * self.head_dim + 2 * (self.num_key_value_heads * self.head_dim)
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
-        self.qkv_proj = nn.Linear(self.hidden_size, op_size, bias=False)
-        self._init_rope()
-
-    def _init_rope(self):
-        if self.rope_scaling is None:
-            self.rotary_emb = Phi3RotaryEmbedding(
-                self.head_dim,
-                max_position_embeddings=self.max_position_embeddings,
-                base=self.rope_theta,
-            )
-        else:
-            scaling_type = self.config.rope_scaling["type"]
-            if scaling_type == "su":
-                self.rotary_emb = Phi3SuScaledRotaryEmbedding(self.head_dim, self.config)
-            elif scaling_type == "yarn":
-                self.rotary_emb = Phi3YarnScaledRotaryEmbedding(self.head_dim, self.config)
-            else:
-                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        logger.warning_once("You are not running the flash-attention implementation, expect numerical differences.")
-
-        bsz, q_len, _ = hidden_states.size()
-
-        qkv = self.qkv_proj(hidden_states)
-        query_pos = self.num_heads * self.head_dim
-        query_states = qkv[..., :query_pos]
-        key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
-        value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
-
-        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:
-            if self.layer_idx is None:
-                raise ValueError(
-                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
-                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
-                    "with a layer index."
-                )
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len)
-
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # 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)
-
-        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(value_states.dtype)
-        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
-
-        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)
-
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-class Phi3FlashAttention2(Phi3Attention):
-    """
-    Phi-3 flash attention module. This module inherits from `Phi3Attention` as the weights of the module stays
-    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
-    flash attention and deal with padding tokens in case the input contains any of them.
-    """
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
-        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
-        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        # Phi3FlashAttention2 attention does not support output_attentions
-
-        if not _flash_supports_window_size:
-            logger.warning_once(
-                "The current flash attention version does not support sliding window attention. Please use `attn_implementation='eager'` or upgrade flash-attn library."
-            )
-            raise ValueError("The current flash attention version does not support sliding window attention.")
-
-        output_attentions = False
-
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-
-            # overwrite attention_mask with padding_mask
-            attention_mask = kwargs.pop("padding_mask")
-
-        bsz, q_len, _ = hidden_states.size()
-
-        qkv = self.qkv_proj(hidden_states)
-        query_pos = self.num_heads * self.head_dim
-        query_states = qkv[..., :query_pos]
-        key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
-        value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
-
-        # Flash attention requires the input to have the shape
-        # batch_size x seq_length x head_dim x hidden_dim
-        # therefore we just need to keep the original shape
-        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:
-            if self.layer_idx is None:
-                raise ValueError(
-                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
-                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
-                    "with a layer index."
-                )
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-
-        # Because the input can be padded, the absolute sequence length depends on the max position id.
-        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
-        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=rotary_seq_len)
-
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-
-        use_sliding_windows = (
-            _flash_supports_window_size
-            and getattr(self.config, "sliding_window", None) is not None
-            and kv_seq_len > self.config.sliding_window
-        )
-
-        if past_key_value is not None:
-            # Activate slicing cache only if the config has a value `sliding_windows` attribute
-            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
-            if (
-                getattr(self.config, "sliding_window", None) is not None
-                and kv_seq_len > self.config.sliding_window
-                and cache_has_contents
-            ):
-                slicing_tokens = 1 - self.config.sliding_window
-
-                past_key = past_key_value[self.layer_idx][0]
-                past_value = past_key_value[self.layer_idx][1]
-
-                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
-                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
-
-                if past_key.shape[-2] != self.config.sliding_window - 1:
-                    raise ValueError(
-                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
-                        f" {past_key.shape}"
-                    )
-
-                if attention_mask is not None:
-                    attention_mask = attention_mask[:, slicing_tokens:]
-                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
-
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # 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)
-
-        attn_dropout = self.attention_dropout if self.training else 0.0
-
-        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
-        # therefore the input hidden states gets silently casted in float32. Hence, we need
-        # cast them back in the correct dtype just to be sure everything works as expected.
-        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
-        # in fp32.
-
-        if query_states.dtype == torch.float32:
-            if torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
-            # Handle the case where the model is quantized
-            elif hasattr(self.config, "_pre_quantization_dtype"):
-                target_dtype = self.config._pre_quantization_dtype
-            else:
-                target_dtype = self.qkv_proj.weight.dtype
-
-            logger.warning_once(
-                f"The input hidden states seems to be silently casted in float32, this might be related to"
-                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
-                f" {target_dtype}."
-            )
-
-            query_states = query_states.to(target_dtype)
-            key_states = key_states.to(target_dtype)
-            value_states = value_states.to(target_dtype)
-
-        # Reashape to the expected shape for Flash Attention
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        attn_output = self._flash_attention_forward(
-            query_states,
-            key_states,
-            value_states,
-            attention_mask,
-            q_len,
-            dropout=attn_dropout,
-            use_sliding_windows=use_sliding_windows,
-        )
-
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-    # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._flash_attention_forward
-    def _flash_attention_forward(
-        self,
-        query_states,
-        key_states,
-        value_states,
-        attention_mask,
-        query_length,
-        dropout=0.0,
-        softmax_scale=None,
-        use_sliding_windows=False,
-    ):
-        """
-        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
-        first unpad the input, then computes the attention scores and pad the final attention scores.
-
-        Args:
-            query_states (`torch.Tensor`):
-                Input query states to be passed to Flash Attention API
-            key_states (`torch.Tensor`):
-                Input key states to be passed to Flash Attention API
-            value_states (`torch.Tensor`):
-                Input value states to be passed to Flash Attention API
-            attention_mask (`torch.Tensor`):
-                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
-                position of padding tokens and 1 for the position of non-padding tokens.
-            dropout (`float`):
-                Attention dropout
-            softmax_scale (`float`, *optional*):
-                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
-            use_sliding_windows (`bool`, *optional*):
-                Whether to activate sliding window attention.
-        """
-        if not self._flash_attn_uses_top_left_mask:
-            causal = self.is_causal
-        else:
-            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
-            causal = self.is_causal and query_length != 1
-
-        # Contains at least one padding token in the sequence
-        if attention_mask is not None:
-            batch_size = query_states.shape[0]
-            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
-                query_states, key_states, value_states, attention_mask, query_length
-            )
-
-            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
-
-            if not use_sliding_windows:
-                attn_output_unpad = flash_attn_varlen_func(
-                    query_states,
-                    key_states,
-                    value_states,
-                    cu_seqlens_q=cu_seqlens_q,
-                    cu_seqlens_k=cu_seqlens_k,
-                    max_seqlen_q=max_seqlen_in_batch_q,
-                    max_seqlen_k=max_seqlen_in_batch_k,
-                    dropout_p=dropout,
-                    softmax_scale=softmax_scale,
-                    causal=causal,
-                )
-            else:
-                attn_output_unpad = flash_attn_varlen_func(
-                    query_states,
-                    key_states,
-                    value_states,
-                    cu_seqlens_q=cu_seqlens_q,
-                    cu_seqlens_k=cu_seqlens_k,
-                    max_seqlen_q=max_seqlen_in_batch_q,
-                    max_seqlen_k=max_seqlen_in_batch_k,
-                    dropout_p=dropout,
-                    softmax_scale=softmax_scale,
-                    causal=causal,
-                    window_size=(self.config.sliding_window, self.config.sliding_window),
-                )
-
-            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
-        else:
-            if not use_sliding_windows:
-                attn_output = flash_attn_func(
-                    query_states,
-                    key_states,
-                    value_states,
-                    dropout,
-                    softmax_scale=softmax_scale,
-                    causal=causal,
-                )
-            else:
-                attn_output = flash_attn_func(
-                    query_states,
-                    key_states,
-                    value_states,
-                    dropout,
-                    softmax_scale=softmax_scale,
-                    causal=causal,
-                    window_size=(self.config.sliding_window, self.config.sliding_window),
-                )
-
-        return attn_output
-
-    # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input
-    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
-        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
-
-        # On the first iteration we need to properly re-create the padding mask
-        # by slicing it on the proper place
-        if kv_seq_len != attention_mask.shape[-1]:
-            attention_mask_num_tokens = attention_mask.shape[-1]
-            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
-
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
-
-        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
-        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
-
-        if query_length == kv_seq_len:
-            query_layer = index_first_axis(
-                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
-            )
-            cu_seqlens_q = cu_seqlens_k
-            max_seqlen_in_batch_q = max_seqlen_in_batch_k
-            indices_q = indices_k
-        elif query_length == 1:
-            max_seqlen_in_batch_q = 1
-            cu_seqlens_q = torch.arange(
-                batch_size + 1, dtype=torch.int32, device=query_layer.device
-            )  # There is a memcpy here, that is very bad.
-            indices_q = cu_seqlens_q[:-1]
-            query_layer = query_layer.squeeze(1)
-        else:
-            # The -q_len: slice assumes left padding.
-            attention_mask = attention_mask[:, -query_length:]
-            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
-
-        return (
-            query_layer,
-            key_layer,
-            value_layer,
-            indices_q,
-            (cu_seqlens_q, cu_seqlens_k),
-            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
-        )
-
-
-# copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Phi3
-# TODO @Arthur no longer copied from LLama after static cache
-class Phi3SdpaAttention(Phi3Attention):
-    """
-    Phi3 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
-    `Phi3Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
-    SDPA API.
-    """
-
-    # Adapted from Phi3Attention.forward
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if output_attentions:
-            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
-            logger.warning_once(
-                "Phi3Model is using Phi3SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
-                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
-            )
-            return super().forward(
-                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,
-            )
-
-        bsz, q_len, _ = hidden_states.size()
-
-        qkv = self.qkv_proj(hidden_states)
-        query_pos = self.num_heads * self.head_dim
-        query_states = qkv[..., :query_pos]
-        key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
-        value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
-
-        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.get_usable_length(kv_seq_len, self.layer_idx)
-        cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len)
-
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        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()}"
-                )
-
-        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
-        # Reference: https://github.com/pytorch/pytorch/issues/112577.
-        if query_states.device.type == "cuda" and attention_mask is not None:
-            query_states = query_states.contiguous()
-            key_states = key_states.contiguous()
-            value_states = value_states.contiguous()
-
-        attn_output = torch.nn.functional.scaled_dot_product_attention(
-            query_states,
-            key_states,
-            value_states,
-            attn_mask=attention_mask,
-            dropout_p=self.attention_dropout if self.training else 0.0,
-            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
-            is_causal=self.is_causal and attention_mask is None and q_len > 1,
-        )
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
-
-        attn_output = self.o_proj(attn_output)
-
-        return attn_output, None, past_key_value
-
-
-PHI3_ATTENTION_CLASSES = {
-    "eager": Phi3Attention,
-    "flash_attention_2": Phi3FlashAttention2,
-    "sdpa": Phi3SdpaAttention,
-}
-
-
-class Phi3DecoderLayer(nn.Module):
-    def __init__(self, config: Phi3Config, layer_idx: int):
-        super().__init__()
-
-        self.config = config
-        self.self_attn = PHI3_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
-
-        self.mlp = Phi3MLP(config)
-        self.input_layernorm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-        self.resid_attn_dropout = nn.Dropout(config.resid_pdrop)
-        self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop)
-        self.post_attention_layernorm = Phi3RMSNorm(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,
-        **kwargs,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-        """
-        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.
-            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)
-            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
-        attn_outputs, 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 + self.resid_attn_dropout(attn_outputs)
-
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + self.resid_mlp_dropout(hidden_states)
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        if use_cache:
-            outputs += (present_key_value,)
-
-        return outputs
-
-
-PHI3_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 ([`Phi3Config`]):
-            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 Phi-3 model outputting raw hidden-states without any specific head on top.",
-    PHI3_START_DOCSTRING,
-)
-class Phi3PreTrainedModel(PreTrainedModel):
-    config_class = Phi3Config
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["Phi3DecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = True
-    _supports_sdpa = False
-    _supports_cache_class = True
-
-    _version = "0.0.5"
-
-    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_()
-
-
-PHI3_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 `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 (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
-            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
-            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
-
-            Two formats are allowed:
-            - a [`~cache_utils.Cache`] instance;
-            - 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)`). This is also known as the legacy
-            cache format.
-
-            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
-            legacy cache format will be returned.
-
-            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
-            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `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 Phi-3 model outputting raw hidden-states without any specific head on top.",
-    PHI3_START_DOCSTRING,
-)
-class Phi3Model(Phi3PreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`]
-
-    Args:
-        config: Phi3Config
-    """
-
-    def __init__(self, config: Phi3Config):
-        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.embed_dropout = nn.Dropout(config.embd_pdrop)
-        self.layers = nn.ModuleList(
-            [Phi3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self._attn_implementation = config._attn_implementation
-        self.norm = Phi3RMSNorm(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
-
-    @add_start_docstrings_to_model_forward(PHI3_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 input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape[:2]
-        elif inputs_embeds is not None:
-            batch_size, seq_length = inputs_embeds.shape[:2]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        past_key_values_length = 0
-
-        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
-
-        if use_cache:
-            use_legacy_cache = not isinstance(past_key_values, Cache)
-            if use_legacy_cache:
-                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            past_key_values_length = past_key_values.get_usable_length(seq_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)
-
-        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
-            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
-            if is_padding_right:
-                raise ValueError(
-                    "You are attempting to perform batched generation with padding_side='right'"
-                    " this may lead to unexpected behaviour for Flash Attention version of Phi3. Make sure to "
-                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
-                )
-
-        if self._attn_implementation == "flash_attention_2":
-            # 2d mask is passed through the layers
-            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
-        else:
-            # 4d mask is passed through the layers
-            attention_mask = _prepare_4d_causal_attention_mask(
-                attention_mask,
-                (batch_size, seq_length),
-                inputs_embeds,
-                past_key_values_length,
-                sliding_window=self.config.sliding_window,
-            )
-
-        hidden_states = inputs_embeds
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = None
-
-        for decoder_layer in self.layers:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-                layer_outputs = self._gradient_checkpointing_func(
-                    decoder_layer.__call__,
-                    hidden_states,
-                    attention_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    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 = None
-        if use_cache:
-            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
-        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 Phi3ForCausalLM(Phi3PreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi3
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = Phi3Model(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()
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
-    def get_decoder(self):
-        return self.model
-
-    # Ignore copy
-    @add_start_docstrings_to_model_forward(PHI3_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, Phi3ForCausalLM
-
-        >>> model = Phi3ForCausalLM.from_pretrained("microsoft/phi-3-mini-4k-instruct")
-        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-3-mini-4k-instruct")
-
-        >>> prompt = "This is an example script ."
-        >>> 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]
-        'This is an example script .\n Certainly! Below is a sample script that demonstrates a simple task, such as calculating the sum'
-        ```"""
-
-        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)
-        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,
-        )
-
-    # Copied from transformers.models.persimmon.modeling_persimmon.PersimmonForCausalLM.prepare_inputs_for_generation
-    def prepare_inputs_for_generation(
-        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
-    ):
-        if past_key_values is not None:
-            if isinstance(past_key_values, Cache):
-                cache_length = past_key_values.get_seq_length()
-                past_length = past_key_values.seen_tokens
-                max_cache_length = past_key_values.get_max_length()
-            else:
-                cache_length = past_length = past_key_values[0][0].shape[2]
-                max_cache_length = None
-
-            # Keep only the unprocessed tokens:
-            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
-            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
-            # input)
-            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
-                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
-            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
-            # input_ids based on the past_length.
-            elif past_length < input_ids.shape[1]:
-                input_ids = input_ids[:, past_length:]
-            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
-            else:
-                remove_prefix_length = input_ids.shape[1] - 1
-                input_ids = input_ids[:, remove_prefix_length:]
-            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
-            if (
-                max_cache_length is not None
-                and attention_mask is not None
-                and cache_length + input_ids.shape[1] > max_cache_length
-            ):
-                attention_mask = attention_mask[:, -max_cache_length:]
-
-        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[:, -input_ids.shape[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
-    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
-    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 [`Phi3Model`] with a sequence classification head on top (linear layer).
-
-    [`Phi3ForSequenceClassification`] 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).
-    """,
-    PHI3_START_DOCSTRING,
-)
-# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Phi3, LLAMA->PHI3, self.transformer->self.model, transformer_outputs->model_outputs
-class Phi3ForSequenceClassification(Phi3PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = Phi3Model(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(PHI3_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
-
-        model_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 = model_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:
-                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
-                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
-                sequence_lengths = sequence_lengths % input_ids.shape[-1]
-                sequence_lengths = sequence_lengths.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,) + model_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=model_outputs.past_key_values,
-            hidden_states=model_outputs.hidden_states,
-            attentions=model_outputs.attentions,
-        )
-
-
-@add_start_docstrings(
-    """
-    [`Phi3Model`] with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
-    Named-Entity-Recognition (NER) tasks.
-    """,
-    PHI3_START_DOCSTRING,
-)
-# Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with Mpt->Phi3,MPT->PHI3,self.transformer->self.model,transformer_outputs->model_outputs
-class Phi3ForTokenClassification(Phi3PreTrainedModel):
-    def __init__(self, config: Phi3Config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-
-        self.model = Phi3Model(config)
-        if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
-            classifier_dropout = config.classifier_dropout
-        elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
-            classifier_dropout = config.hidden_dropout
-        else:
-            classifier_dropout = 0.1
-        self.dropout = nn.Dropout(classifier_dropout)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(PHI3_INPUTS_DOCSTRING)
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=TokenClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        **deprecated_arguments,
-    ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
-        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
-
-        model_outputs = self.model(
-            input_ids,
-            past_key_values=past_key_values,
-            attention_mask=attention_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 = model_outputs[0]
-        hidden_states = self.dropout(hidden_states)
-        logits = self.classifier(hidden_states)
-
-        loss = None
-        if labels is not None:
-            # move labels to correct device to enable model parallelism
-            labels = labels.to(logits.device)
-            batch_size, seq_length = labels.shape
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(
-                logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
-            )
-
-        if not return_dict:
-            output = (logits,) + model_outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return TokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=model_outputs.hidden_states,
-            attentions=model_outputs.attentions,
-        )

bunny/model/language_model/qwen2/__init__.py

@@ -1,80 +0,0 @@
-# Copyright 2024 The Qwen Team and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import TYPE_CHECKING
-
-from transformers.utils import (
-    OptionalDependencyNotAvailable,
-    _LazyModule,
-    is_tokenizers_available,
-    is_torch_available,
-)
-
-
-_import_structure = {
-    "configuration_qwen2": ["QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Qwen2Config"],
-    "tokenization_qwen2": ["Qwen2Tokenizer"],
-}
-
-try:
-    if not is_tokenizers_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_qwen2_fast"] = ["Qwen2TokenizerFast"]
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_qwen2"] = [
-        "Qwen2ForCausalLM",
-        "Qwen2Model",
-        "Qwen2PreTrainedModel",
-        "Qwen2ForSequenceClassification",
-    ]
-
-
-if TYPE_CHECKING:
-    from .configuration_qwen2 import QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP, Qwen2Config
-    from .tokenization_qwen2 import Qwen2Tokenizer
-
-    try:
-        if not is_tokenizers_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_qwen2_fast import Qwen2TokenizerFast
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_qwen2 import (
-            Qwen2ForCausalLM,
-            Qwen2ForSequenceClassification,
-            Qwen2Model,
-            Qwen2PreTrainedModel,
-        )
-
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

bunny/model/language_model/qwen2/configuration_qwen2.py

@@ -1,144 +0,0 @@
-# coding=utf-8
-# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Qwen2 model configuration"""
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "Qwen/Qwen2-7B-beta": "https://huggingface.co/Qwen/Qwen2-7B-beta/resolve/main/config.json",
-}
-
-
-class Qwen2Config(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a
-    Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of
-    Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta).
-
-    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 151936):
-            Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`Qwen2Model`]
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 22016):
-            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*, defaults to 32):
-            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 `32`.
-        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 32768):
-            The maximum sequence length that this model might ever be used with.
-        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-06):
-            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 the model's input and output word embeddings should be tied.
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        use_sliding_window (`bool`, *optional*, defaults to `False`):
-            Whether to use sliding window attention.
-        sliding_window (`int`, *optional*, defaults to 4096):
-            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
-        max_window_layers (`int`, *optional*, defaults to 28):
-            The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-
-    ```python
-    >>> from transformers import Qwen2Model, Qwen2Config
-
-    >>> # Initializing a Qwen2 style configuration
-    >>> configuration = Qwen2Config()
-
-    >>> # Initializing a model from the Qwen2-7B style configuration
-    >>> model = Qwen2Model(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "qwen2"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=151936,
-        hidden_size=4096,
-        intermediate_size=22016,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=32,
-        hidden_act="silu",
-        max_position_embeddings=32768,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        tie_word_embeddings=False,
-        rope_theta=10000.0,
-        use_sliding_window=False,
-        sliding_window=4096,
-        max_window_layers=28,
-        attention_dropout=0.0,
-        **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.use_sliding_window = use_sliding_window
-        self.sliding_window = sliding_window
-        self.max_window_layers = max_window_layers
-
-        # 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.use_cache = use_cache
-        self.rope_theta = rope_theta
-        self.attention_dropout = attention_dropout
-
-        super().__init__(
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )

bunny/model/language_model/qwen2/modeling_qwen2.py

@@ -1,1403 +0,0 @@
-# coding=utf-8
-# Copyright 2024 The Qwen team, Alibaba Group 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 Qwen2 model."""
-import inspect
-import math
-import warnings
-from typing import List, Optional, Tuple, Union
-
-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.cache_utils import Cache, DynamicCache
-from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
-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,
-    is_flash_attn_2_available,
-    is_flash_attn_greater_or_equal_2_10,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_qwen2 import Qwen2Config
-
-
-if is_flash_attn_2_available():
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
-
-    _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
-
-
-logger = logging.get_logger(__name__)
-
-
-_CHECKPOINT_FOR_DOC = "Qwen/Qwen2-7B-beta"
-_CONFIG_FOR_DOC = "Qwen2Config"
-
-QWEN2_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "Qwen/Qwen2-7B-beta",
-    # See all Qwen2 models at https://huggingface.co/models?filter=qwen2
-]
-
-
-# Copied from transformers.models.llama.modeling_llama._get_unpad_data
-def _get_unpad_data(attention_mask):
-    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max().item()
-    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Qwen2
-class Qwen2RMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        Qwen2RMSNorm 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)
-
-
-# Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Qwen2
-class Qwen2RotaryEmbedding(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, dtype=torch.int64).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=torch.int64).type_as(self.inv_freq)
-
-        freqs = torch.outer(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().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().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[:seq_len].to(dtype=x.dtype),
-            self.sin_cached[:seq_len].to(dtype=x.dtype),
-        )
-
-
-# Copied from transformers.models.llama.modeling_llama.rotate_half
-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)
-
-
-# Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`):
-            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
-            used to pass offsetted position ids when working with a KV-cache.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
-    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Qwen2
-class Qwen2MLP(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):
-        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-
-
-# Copied from transformers.models.llama.modeling_llama.repeat_kv
-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 Qwen2Attention(nn.Module):
-    """
-    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
-    and "Generating Long Sequences with Sparse Transformers".
-    """
-
-    def __init__(self, config: Qwen2Config, layer_idx: Optional[int] = None):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        if layer_idx is None:
-            logger.warning_once(
-                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
-                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
-                "when creating this class."
-            )
-
-        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
-        self.rope_theta = config.rope_theta
-        self.is_causal = True
-        self.attention_dropout = config.attention_dropout
-
-        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=True)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
-
-        self.rotary_emb = Qwen2RotaryEmbedding(
-            self.head_dim,
-            max_position_embeddings=self.max_position_embeddings,
-            base=self.rope_theta,
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-        bsz, q_len, _ = hidden_states.size()
-
-        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:
-            if self.layer_idx is None:
-                raise ValueError(
-                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
-                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
-                    "with a layer index."
-                )
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-        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)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # 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)
-
-        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_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
-        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)
-
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-class Qwen2FlashAttention2(Qwen2Attention):
-    """
-    Qwen2 flash attention module, following Qwen2 attention module. This module inherits from `Qwen2Attention`
-    as the weights of the module stays untouched. The only required change would be on the forward pass
-    where it needs to correctly call the public API of flash attention and deal with padding tokens
-    in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom
-    config.max_window_layers layers.
-    """
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
-        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
-        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        **kwargs,
-    ):
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-
-            # overwrite attention_mask with padding_mask
-            attention_mask = kwargs.pop("padding_mask")
-        bsz, q_len, _ = hidden_states.size()
-
-        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:
-            if self.layer_idx is None:
-                raise ValueError(
-                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
-                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
-                    "with a layer index."
-                )
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-
-        # Because the input can be padded, the absolute sequence length depends on the max position id.
-        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
-        cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
-
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-
-        use_sliding_windows = (
-            _flash_supports_window_size
-            and getattr(self.config, "sliding_window", None) is not None
-            and kv_seq_len > self.config.sliding_window
-            and self.config.use_sliding_window
-        )
-
-        if not _flash_supports_window_size:
-            logger.warning_once(
-                "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
-                " make sure to upgrade flash-attn library."
-            )
-
-        if past_key_value is not None:
-            # Activate slicing cache only if the config has a value `sliding_windows` attribute
-            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
-            if (
-                getattr(self.config, "sliding_window", None) is not None
-                and kv_seq_len > self.config.sliding_window
-                and cache_has_contents
-            ):
-                slicing_tokens = 1 - self.config.sliding_window
-
-                past_key = past_key_value[self.layer_idx][0]
-                past_value = past_key_value[self.layer_idx][1]
-
-                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
-                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
-
-                if past_key.shape[-2] != self.config.sliding_window - 1:
-                    raise ValueError(
-                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
-                        f" {past_key.shape}"
-                    )
-
-                if attention_mask is not None:
-                    attention_mask = attention_mask[:, slicing_tokens:]
-                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
-
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # 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)
-        dropout_rate = 0.0 if not self.training else self.attention_dropout
-
-        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
-        # therefore the input hidden states gets silently casted in float32. Hence, we need
-        # cast them back in float16 just to be sure everything works as expected.
-        input_dtype = query_states.dtype
-        if input_dtype == torch.float32:
-            if torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
-            # Handle the case where the model is quantized
-            elif hasattr(self.config, "_pre_quantization_dtype"):
-                target_dtype = self.config._pre_quantization_dtype
-            else:
-                target_dtype = self.q_proj.weight.dtype
-
-            logger.warning_once(
-                f"The input hidden states seems to be silently casted in float32, this might be related to"
-                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
-                f" {target_dtype}."
-            )
-
-            query_states = query_states.to(target_dtype)
-            key_states = key_states.to(target_dtype)
-            value_states = value_states.to(target_dtype)
-
-        # Reashape to the expected shape for Flash Attention
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        attn_output = self._flash_attention_forward(
-            query_states,
-            key_states,
-            value_states,
-            attention_mask,
-            q_len,
-            dropout=dropout_rate,
-            use_sliding_windows=use_sliding_windows,
-        )
-
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-    def _flash_attention_forward(
-        self,
-        query_states,
-        key_states,
-        value_states,
-        attention_mask,
-        query_length,
-        dropout=0.0,
-        softmax_scale=None,
-        use_sliding_windows=False,
-    ):
-        """
-        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
-        first unpad the input, then computes the attention scores and pad the final attention scores.
-
-        Args:
-            query_states (`torch.Tensor`):
-                Input query states to be passed to Flash Attention API
-            key_states (`torch.Tensor`):
-                Input key states to be passed to Flash Attention API
-            value_states (`torch.Tensor`):
-                Input value states to be passed to Flash Attention API
-            attention_mask (`torch.Tensor`):
-                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
-                position of padding tokens and 1 for the position of non-padding tokens.
-            dropout (`float`):
-                Attention dropout
-            softmax_scale (`float`, *optional*):
-                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
-            use_sliding_windows (`bool`, *optional*):
-                Whether to activate sliding window attention.
-        """
-        if not self._flash_attn_uses_top_left_mask:
-            causal = self.is_causal
-        else:
-            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
-            causal = self.is_causal and query_length != 1
-
-        # Decide whether to use SWA or not by layer index.
-        if use_sliding_windows and self.layer_idx >= self.config.max_window_layers:
-            use_sliding_windows = False
-
-        # Contains at least one padding token in the sequence
-        if attention_mask is not None:
-            batch_size = query_states.shape[0]
-            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
-                query_states, key_states, value_states, attention_mask, query_length
-            )
-
-            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
-
-            if not use_sliding_windows:
-                attn_output_unpad = flash_attn_varlen_func(
-                    query_states,
-                    key_states,
-                    value_states,
-                    cu_seqlens_q=cu_seqlens_q,
-                    cu_seqlens_k=cu_seqlens_k,
-                    max_seqlen_q=max_seqlen_in_batch_q,
-                    max_seqlen_k=max_seqlen_in_batch_k,
-                    dropout_p=dropout,
-                    softmax_scale=softmax_scale,
-                    causal=causal,
-                )
-            else:
-                attn_output_unpad = flash_attn_varlen_func(
-                    query_states,
-                    key_states,
-                    value_states,
-                    cu_seqlens_q=cu_seqlens_q,
-                    cu_seqlens_k=cu_seqlens_k,
-                    max_seqlen_q=max_seqlen_in_batch_q,
-                    max_seqlen_k=max_seqlen_in_batch_k,
-                    dropout_p=dropout,
-                    softmax_scale=softmax_scale,
-                    causal=causal,
-                    window_size=(self.config.sliding_window, self.config.sliding_window),
-                )
-
-            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
-        else:
-            if not use_sliding_windows:
-                attn_output = flash_attn_func(
-                    query_states,
-                    key_states,
-                    value_states,
-                    dropout,
-                    softmax_scale=softmax_scale,
-                    causal=causal,
-                )
-            else:
-                attn_output = flash_attn_func(
-                    query_states,
-                    key_states,
-                    value_states,
-                    dropout,
-                    softmax_scale=softmax_scale,
-                    causal=causal,
-                    window_size=(self.config.sliding_window, self.config.sliding_window),
-                )
-
-        return attn_output
-
-    # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input
-    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
-        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
-
-        # On the first iteration we need to properly re-create the padding mask
-        # by slicing it on the proper place
-        if kv_seq_len != attention_mask.shape[-1]:
-            attention_mask_num_tokens = attention_mask.shape[-1]
-            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
-
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
-
-        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
-        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
-
-        if query_length == kv_seq_len:
-            query_layer = index_first_axis(
-                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
-            )
-            cu_seqlens_q = cu_seqlens_k
-            max_seqlen_in_batch_q = max_seqlen_in_batch_k
-            indices_q = indices_k
-        elif query_length == 1:
-            max_seqlen_in_batch_q = 1
-            cu_seqlens_q = torch.arange(
-                batch_size + 1, dtype=torch.int32, device=query_layer.device
-            )  # There is a memcpy here, that is very bad.
-            indices_q = cu_seqlens_q[:-1]
-            query_layer = query_layer.squeeze(1)
-        else:
-            # The -q_len: slice assumes left padding.
-            attention_mask = attention_mask[:, -query_length:]
-            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
-
-        return (
-            query_layer,
-            key_layer,
-            value_layer,
-            indices_q,
-            (cu_seqlens_q, cu_seqlens_k),
-            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
-        )
-
-
-# Copied from transformers.models.mistral.modeling_mistral.MistralSdpaAttention with Mistral->Qwen2
-class Qwen2SdpaAttention(Qwen2Attention):
-    """
-    Qwen2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
-    `Qwen2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
-    SDPA API.
-    """
-
-    # Adapted from Qwen2Attention.forward
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if output_attentions:
-            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
-            logger.warning_once(
-                "Qwen2Model is using Qwen2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
-                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
-            )
-            return super().forward(
-                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,
-            )
-
-        bsz, q_len, _ = hidden_states.size()
-
-        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.get_usable_length(kv_seq_len, self.layer_idx)
-        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)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        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()}"
-                )
-
-        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
-        # Reference: https://github.com/pytorch/pytorch/issues/112577.
-        if query_states.device.type == "cuda" and attention_mask is not None:
-            query_states = query_states.contiguous()
-            key_states = key_states.contiguous()
-            value_states = value_states.contiguous()
-
-        attn_output = torch.nn.functional.scaled_dot_product_attention(
-            query_states,
-            key_states,
-            value_states,
-            attn_mask=attention_mask,
-            dropout_p=self.attention_dropout if self.training else 0.0,
-            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
-            is_causal=self.is_causal and attention_mask is None and q_len > 1,
-        )
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
-
-        attn_output = self.o_proj(attn_output)
-
-        return attn_output, None, past_key_value
-
-
-QWEN2_ATTENTION_CLASSES = {
-    "eager": Qwen2Attention,
-    "flash_attention_2": Qwen2FlashAttention2,
-    "sdpa": Qwen2SdpaAttention,
-}
-
-
-class Qwen2DecoderLayer(nn.Module):
-    def __init__(self, config: Qwen2Config, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-
-        if config.use_sliding_window and config._attn_implementation != "flash_attention_2":
-            logger.warning_once(
-                f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
-                "unexpected results may be encountered."
-            )
-        self.self_attn = QWEN2_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
-
-        self.mlp = Qwen2MLP(config)
-        self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = Qwen2RMSNorm(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,
-        **kwargs,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. "
-                "Please make sure use `attention_mask` instead.`"
-            )
-        """
-        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, sequence_length)` where padding elements are indicated by 0.
-            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
-
-
-QWEN2_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 ([`Qwen2Config`]):
-            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 Qwen2 Model outputting raw hidden-states without any specific head on top.",
-    QWEN2_START_DOCSTRING,
-)
-class Qwen2PreTrainedModel(PreTrainedModel):
-    config_class = Qwen2Config
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["Qwen2DecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_cache_class = True
-
-    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_()
-
-
-QWEN2_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 (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
-            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
-            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
-
-            Two formats are allowed:
-            - a [`~cache_utils.Cache`] instance;
-            - 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)`). This is also known as the legacy
-            cache format.
-
-            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
-            legacy cache format will be returned.
-
-            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
-            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `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 Qwen2 Model outputting raw hidden-states without any specific head on top.",
-    QWEN2_START_DOCSTRING,
-)
-class Qwen2Model(Qwen2PreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`]
-
-    Args:
-        config: Qwen2Config
-    """
-
-    def __init__(self, config: Qwen2Config):
-        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(
-            [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self._attn_implementation = config._attn_implementation
-        self.norm = Qwen2RMSNorm(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
-
-    @add_start_docstrings_to_model_forward(QWEN2_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")
-
-        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
-
-        past_key_values_length = 0
-
-        if use_cache:
-            use_legacy_cache = not isinstance(past_key_values, Cache)
-            if use_legacy_cache:
-                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            past_key_values_length = past_key_values.get_usable_length(seq_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)
-
-        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
-            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
-            if is_padding_right:
-                raise ValueError(
-                    "You are attempting to perform batched generation with padding_side='right'"
-                    " this may lead to unexpected behaviour for Flash Attention version of Qwen2. Make sure to "
-                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
-                )
-
-        if self._attn_implementation == "flash_attention_2":
-            # 2d mask is passed through the layers
-            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
-        elif self._attn_implementation == "sdpa" and not output_attentions:
-            # output_attentions=True can not be supported when using SDPA, and we fall back on
-            # the manual implementation that requires a 4D causal mask in all cases.
-            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
-                attention_mask,
-                (batch_size, seq_length),
-                inputs_embeds,
-                past_key_values_length,
-            )
-        else:
-            # 4d mask is passed through the layers
-            attention_mask = _prepare_4d_causal_attention_mask(
-                attention_mask,
-                (batch_size, seq_length),
-                inputs_embeds,
-                past_key_values_length,
-                sliding_window=self.config.sliding_window,
-            )
-
-        hidden_states = inputs_embeds
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        next_decoder_cache = None
-
-        for decoder_layer in self.layers:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            if self.gradient_checkpointing and self.training:
-                layer_outputs = self._gradient_checkpointing_func(
-                    decoder_layer.__call__,
-                    hidden_states,
-                    attention_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    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 = None
-        if use_cache:
-            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
-
-        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 Qwen2ForCausalLM(Qwen2PreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = Qwen2Model(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(QWEN2_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, Qwen2ForCausalLM
-
-        >>> model = Qwen2ForCausalLM.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]
-        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
-    ):
-        # Omit tokens covered by past_key_values
-        if past_key_values is not None:
-            if isinstance(past_key_values, Cache):
-                cache_length = past_key_values.get_seq_length()
-                past_length = past_key_values.seen_tokens
-                max_cache_length = past_key_values.get_max_length()
-            else:
-                cache_length = past_length = past_key_values[0][0].shape[2]
-                max_cache_length = None
-
-            # Keep only the unprocessed tokens:
-            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
-            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
-            # input)
-            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
-                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
-            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
-            # input_ids based on the past_length.
-            elif past_length < input_ids.shape[1]:
-                input_ids = input_ids[:, past_length:]
-            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
-            else:
-                remove_prefix_length = input_ids.shape[1] - 1
-                input_ids = input_ids[:, remove_prefix_length:]
-            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
-            if (
-                max_cache_length is not None
-                and attention_mask is not None
-                and cache_length + input_ids.shape[1] > max_cache_length
-            ):
-                attention_mask = attention_mask[:, -max_cache_length:]
-
-        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[:, -input_ids.shape[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 Qwen2 Model transformer with a sequence classification head on top (linear layer).
-
-    [`Qwen2ForSequenceClassification`] 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).
-    """,
-    QWEN2_START_DOCSTRING,
-)
-class Qwen2ForSequenceClassification(Qwen2PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = Qwen2Model(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(QWEN2_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:
-                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
-                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
-                sequence_lengths = sequence_lengths % input_ids.shape[-1]
-                sequence_lengths = sequence_lengths.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,
-        )

bunny/model/language_model/qwen2/tokenization_qwen2.py

@@ -1,345 +0,0 @@
-# coding=utf-8
-# Copyright 2024 The Qwen team, Alibaba Group and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tokenization classes for Qwen2."""
-
-import json
-import os
-import unicodedata
-from functools import lru_cache
-from typing import Optional, Tuple
-
-import regex as re
-
-from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {
-    "vocab_file": "vocab.json",
-    "merges_file": "merges.txt",
-}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {"qwen/qwen-tokenizer": "https://huggingface.co/qwen/qwen-tokenizer/resolve/main/vocab.json"},
-    "merges_file": {"qwen/qwen-tokenizer": "https://huggingface.co/qwen/qwen-tokenizer/resolve/main/merges.txt"},
-}
-
-MAX_MODEL_INPUT_SIZES = {"qwen/qwen-tokenizer": 32768}
-
-PRETOKENIZE_REGEX = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
-
-
-@lru_cache()
-# Copied from transformers.models.gpt2.tokenization_gpt2.bytes_to_unicode
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
-    characters the bpe code barfs on.
-
-    The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
-    if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
-    decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
-    tables between utf-8 bytes and unicode strings.
-    """
-    bs = (
-        list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
-    )
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8 + n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-# Copied from transformers.models.gpt2.tokenization_gpt2.get_pairs
-def get_pairs(word):
-    """
-    Return set of symbol pairs in a word.
-
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-class Qwen2Tokenizer(PreTrainedTokenizer):
-    """
-    Construct a Qwen2 tokenizer. Based on byte-level Byte-Pair-Encoding.
-
-    Same with GPT2Tokenizer, this tokenizer has been trained to treat spaces like parts of the tokens so a word will
-    be encoded differently whether it is at the beginning of the sentence (without space) or not:
-
-    ```python
-    >>> from transformers import Qwen2Tokenizer
-
-    >>> tokenizer = Qwen2Tokenizer.from_pretrained("Qwen/Qwen-tokenizer")
-    >>> tokenizer("Hello world")["input_ids"]
-    [9707, 1879]
-
-    >>> tokenizer(" Hello world")["input_ids"]
-    [21927, 1879]
-    ```
-    This is expected.
-
-    You should not use GPT2Tokenizer instead, because of the different pretokenization rules.
-
-    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
-    this superclass for more information regarding those methods.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-        merges_file (`str`):
-            Path to the merges file.
-        errors (`str`, *optional*, defaults to `"replace"`):
-            Paradigm to follow when decoding bytes to UTF-8. See
-            [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
-        unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        bos_token (`str`, *optional*):
-            The beginning of sequence token. Not applicable for this tokenizer.
-        eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
-            The end of sequence token.
-        pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
-            The token used for padding, for example when batching sequences of different lengths.
-        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
-            Whether or not the model should cleanup the spaces that were added when splitting the input text during the
-            tokenization process. Not applicable to this tokenizer, since tokenization does not add spaces.
-        split_special_tokens (`bool`, *optional*, defaults to `False`):
-            Whether or not the special tokens should be split during the tokenization process. The default behavior is
-            to not split special tokens. This means that if `<|endoftext|>` is the `eos_token`, then `tokenizer.tokenize("<|endoftext|>") =
-            ['<|endoftext|>`]. Otherwise, if `split_special_tokens=True`, then `tokenizer.tokenize("<|endoftext|>")` will be give `['<',
-            '|', 'endo', 'ft', 'ext', '|', '>']`. This argument is only supported for `slow` tokenizers for the moment.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = MAX_MODEL_INPUT_SIZES
-    model_input_names = ["input_ids", "attention_mask"]
-
-    def __init__(
-        self,
-        vocab_file,
-        merges_file,
-        errors="replace",
-        unk_token="<|endoftext|>",
-        bos_token=None,
-        eos_token="<|endoftext|>",
-        pad_token="<|endoftext|>",
-        clean_up_tokenization_spaces=False,
-        split_special_tokens=False,
-        **kwargs,
-    ):
-        # Qwen vocab does not contain control tokens; added tokens need to be special
-        bos_token = (
-            AddedToken(bos_token, lstrip=False, rstrip=False, special=True, normalized=False)
-            if isinstance(bos_token, str)
-            else bos_token
-        )
-        eos_token = (
-            AddedToken(eos_token, lstrip=False, rstrip=False, special=True, normalized=False)
-            if isinstance(eos_token, str)
-            else eos_token
-        )
-        unk_token = (
-            AddedToken(unk_token, lstrip=False, rstrip=False, special=True, normalized=False)
-            if isinstance(unk_token, str)
-            else unk_token
-        )
-        pad_token = (
-            AddedToken(pad_token, lstrip=False, rstrip=False, special=True, normalized=False)
-            if isinstance(pad_token, str)
-            else pad_token
-        )
-
-        with open(vocab_file, encoding="utf-8") as vocab_handle:
-            self.encoder = json.load(vocab_handle)
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.errors = errors  # how to handle errors in decoding
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        bpe_merges = []
-        with open(merges_file, encoding="utf-8") as merges_handle:
-            for line in merges_handle:
-                line = line.strip()
-                if not line or line.startswith("#"):
-                    continue
-                bpe_merges.append(tuple(line.split()))
-        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
-        # NOTE: the cache can grow without bound and will get really large for long running processes
-        # (esp. for texts of language that do not use space between word, e.g. Chinese); technically
-        # not a memory leak but appears as one.
-        # GPT2Tokenizer has the same problem, so let's be consistent.
-        self.cache = {}
-
-        self.pat = re.compile(PRETOKENIZE_REGEX)
-
-        if kwargs.get("add_prefix_space", False):
-            logger.warning_once(
-                f"{self.__class__.__name} does not support `add_prefix_space`, setting it to True has no effect."
-            )
-
-        super().__init__(
-            errors=errors,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            pad_token=pad_token,
-            unk_token=unk_token,
-            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            split_special_tokens=split_special_tokens,
-            **kwargs,
-        )
-
-    @property
-    def vocab_size(self) -> int:
-        return len(self.encoder)
-
-    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.get_vocab
-    def get_vocab(self):
-        return dict(self.encoder, **self.added_tokens_encoder)
-
-    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.bpe
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token
-
-        while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                except ValueError:
-                    new_word.extend(word[i:])
-                    break
-                else:
-                    new_word.extend(word[i:j])
-                    i = j
-
-                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
-                    new_word.append(first + second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = " ".join(word)
-        self.cache[token] = word
-        return word
-
-    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._tokenize
-    def _tokenize(self, text):
-        """Tokenize a string."""
-        bpe_tokens = []
-        for token in re.findall(self.pat, text):
-            token = "".join(
-                self.byte_encoder[b] for b in token.encode("utf-8")
-            )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
-            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
-        return bpe_tokens
-
-    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_token_to_id
-    def _convert_token_to_id(self, token):
-        """Converts a token (str) in an id using the vocab."""
-        return self.encoder.get(token, self.encoder.get(self.unk_token))
-
-    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_id_to_token
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        return self.decoder.get(index)
-
-    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.convert_tokens_to_string
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (string) in a single string."""
-        text = "".join(tokens)
-        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
-        return text
-
-    def decode(
-        self,
-        token_ids,
-        skip_special_tokens: bool = False,
-        clean_up_tokenization_spaces: Optional[bool] = False,
-        spaces_between_special_tokens: bool = False,
-        **kwargs,
-    ) -> str:
-        # `spaces_between_special_tokens` defaults to True for _decode in slow tokenizers
-        # and cannot be configured elsewhere, but it should default to False for Qwen2Tokenizer
-        return super().decode(
-            token_ids,
-            skip_special_tokens=skip_special_tokens,
-            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            spaces_between_special_tokens=spaces_between_special_tokens,
-            **kwargs,
-        )
-
-    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.save_vocabulary
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-        merge_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
-        )
-
-        with open(vocab_file, "w", encoding="utf-8") as f:
-            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
-
-        index = 0
-        with open(merge_file, "w", encoding="utf-8") as writer:
-            writer.write("#version: 0.2\n")
-            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
-                if index != token_index:
-                    logger.warning(
-                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
-                        " Please check that the tokenizer is not corrupted!"
-                    )
-                    index = token_index
-                writer.write(" ".join(bpe_tokens) + "\n")
-                index += 1
-
-        return vocab_file, merge_file
-
-    def prepare_for_tokenization(self, text, **kwargs):
-        text = unicodedata.normalize("NFC", text)
-        return (text, kwargs)

bunny/model/language_model/qwen2/tokenization_qwen2_fast.py

@@ -1,143 +0,0 @@
-# coding=utf-8
-# Copyright 2024 The Qwen team, Alibaba Group and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tokenization classes for Qwen2."""
-
-from typing import Optional, Tuple
-
-from transformers.tokenization_utils import AddedToken
-from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
-from transformers.utils import logging
-from .tokenization_qwen2 import Qwen2Tokenizer
-
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {
-    "vocab_file": "vocab.json",
-    "merges_file": "merges.txt",
-    "tokenizer_file": "tokenizer.json",
-}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {"qwen/qwen-tokenizer": "https://huggingface.co/qwen/qwen-tokenizer/resolve/main/vocab.json"},
-    "merges_file": {"qwen/qwen-tokenizer": "https://huggingface.co/qwen/qwen-tokenizer/resolve/main/merges.txt"},
-    "tokenizer_file": {
-        "qwen/qwen-tokenizer": "https://huggingface.co/qwen/qwen-tokenizer/resolve/main/tokenizer.json"
-    },
-}
-
-MAX_MODEL_INPUT_SIZES = {"qwen/qwen-tokenizer": 32768}
-
-
-class Qwen2TokenizerFast(PreTrainedTokenizerFast):
-    """
-    Construct a "fast" Qwen2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
-    Byte-Pair-Encoding.
-
-    Same with GPT2Tokenizer, this tokenizer has been trained to treat spaces like parts of the tokens so a word will
-    be encoded differently whether it is at the beginning of the sentence (without space) or not:
-
-    ```python
-    >>> from transformers import Qwen2TokenizerFast
-
-    >>> tokenizer = Qwen2TokenizerFast.from_pretrained("Qwen/Qwen-tokenizer")
-    >>> tokenizer("Hello world")["input_ids"]
-    [9707, 1879]
-
-    >>> tokenizer(" Hello world")["input_ids"]
-    [21927, 1879]
-    ```
-    This is expected.
-
-    This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
-    refer to this superclass for more information regarding those methods.
-
-    Args:
-        vocab_file (`str`, *optional*):
-            Path to the vocabulary file.
-        merges_file (`str`, *optional*):
-            Path to the merges file.
-        tokenizer_file (`str`, *optional*):
-            Path to [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
-            contains everything needed to load the tokenizer.
-        unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead. Not applicable to this tokenizer.
-        bos_token (`str`, *optional*):
-            The beginning of sequence token. Not applicable for this tokenizer.
-        eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
-            The end of sequence token.
-        pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
-            The token used for padding, for example when batching sequences of different lengths.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = MAX_MODEL_INPUT_SIZES
-    model_input_names = ["input_ids", "attention_mask"]
-    slow_tokenizer_class = Qwen2Tokenizer
-
-    def __init__(
-        self,
-        vocab_file=None,
-        merges_file=None,
-        tokenizer_file=None,
-        unk_token="<|endoftext|>",
-        bos_token=None,
-        eos_token="<|endoftext|>",
-        pad_token="<|endoftext|>",
-        **kwargs,
-    ):
-        # We need to at least pass vocab_file and merges_file to base class
-        # in case a slow tokenizer needs to be initialized; other can be
-        # configured through files.
-        # following GPT2TokenizerFast, also adding unk_token, bos_token, and eos_token
-
-        bos_token = (
-            AddedToken(bos_token, lstrip=False, rstrip=False, special=True, normalized=False)
-            if isinstance(bos_token, str)
-            else bos_token
-        )
-        eos_token = (
-            AddedToken(eos_token, lstrip=False, rstrip=False, special=True, normalized=False)
-            if isinstance(eos_token, str)
-            else eos_token
-        )
-        unk_token = (
-            AddedToken(unk_token, lstrip=False, rstrip=False, special=True, normalized=False)
-            if isinstance(unk_token, str)
-            else unk_token
-        )
-        pad_token = (
-            AddedToken(pad_token, lstrip=False, rstrip=False, special=True, normalized=False)
-            if isinstance(pad_token, str)
-            else pad_token
-        )
-
-        super().__init__(
-            vocab_file,
-            merges_file,
-            tokenizer_file=tokenizer_file,
-            unk_token=unk_token,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            pad_token=pad_token,
-            **kwargs,
-        )
-
-    # Copied from transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast.save_vocabulary
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        files = self._tokenizer.model.save(save_directory, name=filename_prefix)
-        return tuple(files)

bunny/model/language_model/stable_lm/configuration_stablelm_epoch.py

@@ -1,113 +0,0 @@
-# Copyright 2023 Stability and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" StableLM Epoch model configuration"""
-from transformers import PretrainedConfig
-from transformers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class StableLMEpochConfig(PretrainedConfig):
-    r"""
-    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 50_304):
-            Vocabulary size of the StableLM model. Defines the number of different tokens that
-            can be represented by the `inputs_ids` passed when calling [`StableLMEpochModel`].
-        intermediate_size (`int`, *optional*, defaults to 6912):
-            Dimension of the MLP representations.
-        hidden_size (`int`, *optional*, defaults to 2560):
-            Dimension of the decoder layers and the pooler layer.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer decoder.
-        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`.
-        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
-            The non-linear activation function (function or string).
-        rope_pct (`float`, *optional*, defaults to 1.0):
-            Percentage of hidden dimensions to allocate to rotary embeddings.
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        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 1e-5):
-            The standard deviation of the truncated_normal_initializer for initializing
-             all weight matrices.
-        norm_eps (`float`, *optional*, defaults to 1e-8):
-            The epsilon used by the 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`.
-        use_qkv_bias (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should use bias for qkv layers.
-        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
-            Whether to tie weight embeddings
-    """
-    model_type = "stablelm_epoch"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=50_304,
-        intermediate_size=6912,
-        hidden_size=2560,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=32,
-        hidden_act="silu",
-        rope_pct=0.25,
-        rope_theta=10_000,
-        max_position_embeddings=4096,
-        initializer_range=0.02,
-        norm_eps=1.0e-5,
-        use_cache=True,
-        use_qkv_bias=True,
-        bos_token_id=0,
-        eos_token_id=2,
-        tie_word_embeddings=False,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.intermediate_size = intermediate_size
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.num_key_value_heads = num_key_value_heads
-        self.hidden_act = hidden_act
-        self.rope_pct = rope_pct
-        self.rope_theta = rope_theta
-        self.initializer_range = initializer_range
-        self.norm_eps = norm_eps
-        self.use_cache = use_cache
-        self.use_qkv_bias = use_qkv_bias
-        self.tie_word_embeddings = tie_word_embeddings
-        super().__init__(
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )

bunny/model/language_model/stable_lm/modeling_stablelm_epoch.py

@@ -1,917 +0,0 @@
-# coding=utf-8
-# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# This code is based off the following work:
-# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
-# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
-""" PyTorch StableLM Epoch model. """
-from typing import Optional, Tuple, Union
-import math
-import warnings
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss
-
-from transformers.cache_utils import Cache
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import logging, is_flash_attn_greater_or_equal_2_10
-
-from .configuration_stablelm_epoch import StableLMEpochConfig
-
-try:
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
-except:
-    flash_attn_func, flash_attn_varlen_func = None, None
-    index_first_axis, pad_input, unpad_input = None, None, None
-
-
-logger = logging.get_logger(__name__)
-
-
-# Copied from transformers.models.llama.modeling_llama._get_unpad_data
-def _get_unpad_data(attention_mask):
-    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max().item()
-    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-
-
-# 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."""
-    batch_size, tgt_len = input_ids_shape
-    mask = torch.full((tgt_len, tgt_len), torch.finfo(torch.float16).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(batch_size, 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 `[batch_size, seq_len]` to `[batch_size, 1, tgt_seq_len, src_seq_len]`."""
-    batch_size, src_len = mask.size()
-    tgt_len = tgt_len if tgt_len is not None else src_len
-
-    expanded_mask = mask[:, None, None, :].expand(batch_size, 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 RotaryEmbedding(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        max_position_embeddings: int,
-        base: int = 10_000,
-        device: Optional[torch.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, device=device, dtype=torch.float32) / 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: int, device: torch.device, dtype: torch.dtype):
-        self.max_seq_len_cached = seq_len
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
-
-        # Don't do einsum, it converts fp32 to fp16 under AMP
-        # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
-        freqs = torch.outer(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)
-
-    def forward(self, x: torch.Tensor, seq_len: Optional[int] = None):
-        # x: [batch_size, num_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=torch.get_default_dtype())
-        return (
-            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
-        )
-
-
-def rotate_half(x: torch.Tensor):
-    """Rotates half the hidden dims of the input."""
-    x1, x2 = torch.chunk(x, 2, dim=-1)
-    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)  # [batch_size, 1, seq_len, dim]
-    sin = sin[position_ids].unsqueeze(1)  # [batch_size, 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 MLP(nn.Module):
-    def __init__(self, config: StableLMEpochConfig):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.intermediate_size = config.intermediate_size
-        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
-        self.act_fn = nn.SiLU()
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-
-
-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 Attention(nn.Module):
-    def __init__(self, config: StableLMEpochConfig):
-        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
-        self.is_causal = True
-
-        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=config.use_qkv_bias)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.use_qkv_bias)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.use_qkv_bias)
-        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
-
-        self._init_rope()
-
-    def _init_rope(self):
-        self.rotary_ndims = int(self.head_dim * self.config.rope_pct)
-        self.rotary_emb = RotaryEmbedding(
-            self.rotary_ndims,
-            max_position_embeddings=self.config.max_position_embeddings,
-            base=self.config.rope_theta,
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        attention_mask: torch.FloatTensor,
-        position_ids: torch.LongTensor,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        bsz, q_len, _ = hidden_states.size()
-
-        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)
-
-        query_rot = query_states[..., : self.rotary_ndims]
-        query_pass = query_states[..., self.rotary_ndims :]
-        key_rot = key_states[..., : self.rotary_ndims]
-        key_pass = key_states[..., self.rotary_ndims :]
-
-        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_rot, key_rot, cos, sin, position_ids)
-
-        # [batch_size, num_heads, seq_len, head_dim]
-        query_states = torch.cat((query_states, query_pass), dim=-1)
-        key_states = torch.cat((key_states, key_pass), dim=-1)
-
-        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)
-
-        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()}"
-            )
-
-        # Merge heads
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-        # Final linear projection
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-class FlashAttention2(Attention):
-    """
-    Reference: https://github.com/huggingface/transformers/blob/5d36025ca13d05151b7a0c761e90d429c4644a30/src/transformers/models/llama/modeling_llama.py#L456
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
-        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
-        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        # FlashAttention2 attention does not support output_attentions
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-
-            # overwrite attention_mask with padding_mask
-            attention_mask = kwargs.pop("padding_mask")
-
-        output_attentions = False
-
-        bsz, q_len, _ = hidden_states.size()
-
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        # Flash attention requires the input to have the shape
-        # batch_size x seq_length x head_dim x hidden_dim
-        # therefore we just need to keep the original shape
-        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)
-
-        query_rot = query_states[..., : self.rotary_ndims]
-        query_pass = query_states[..., self.rotary_ndims :]
-        key_rot = key_states[..., : self.rotary_ndims]
-        key_pass = key_states[..., self.rotary_ndims :]
-
-        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_rot, key_rot, cos, sin, position_ids)
-
-        # [batch_size, num_heads, seq_len, head_dim]
-        query_states = torch.cat((query_states, query_pass), dim=-1)
-        key_states = torch.cat((key_states, key_pass), dim=-1)
-
-        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
-
-        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
-        # to be able to avoid many of these transpose/reshape/view.
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        dropout_rate = self.attention_dropout if self.training else 0.0
-
-        attn_output = self._flash_attention_forward(
-            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
-        )
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-    def _flash_attention_forward(
-        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
-    ):
-        """
-        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
-        first unpad the input, then computes the attention scores and pad the final attention scores.
-
-        Args:
-            query_states (`torch.Tensor`):
-                Input query states to be passed to Flash Attention API
-            key_states (`torch.Tensor`):
-                Input key states to be passed to Flash Attention API
-            value_states (`torch.Tensor`):
-                Input value states to be passed to Flash Attention API
-            attention_mask (`torch.Tensor`):
-                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
-                position of padding tokens and 1 for the position of non-padding tokens.
-            dropout (`int`, *optional*):
-                Attention dropout
-            softmax_scale (`float`, *optional*):
-                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
-        """
-        if not self._flash_attn_uses_top_left_mask:
-            causal = self.is_causal
-        else:
-            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in FlashAttention2 __init__.
-            causal = self.is_causal and query_length != 1
-
-        # Contains at least one padding token in the sequence
-        if attention_mask is not None:
-            batch_size = query_states.shape[0]
-            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
-                query_states, key_states, value_states, attention_mask, query_length
-            )
-
-            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
-
-            attn_output_unpad = flash_attn_varlen_func(
-                query_states,
-                key_states,
-                value_states,
-                cu_seqlens_q=cu_seqlens_q,
-                cu_seqlens_k=cu_seqlens_k,
-                max_seqlen_q=max_seqlen_in_batch_q,
-                max_seqlen_k=max_seqlen_in_batch_k,
-                dropout_p=dropout,
-                softmax_scale=softmax_scale,
-                causal=causal,
-            )
-
-            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
-        else:
-            attn_output = flash_attn_func(
-                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
-            )
-
-        return attn_output
-
-    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
-        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
-
-        key_layer = index_first_axis(
-            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
-        )
-        value_layer = index_first_axis(
-            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
-        )
-        if query_length == kv_seq_len:
-            query_layer = index_first_axis(
-                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
-            )
-            cu_seqlens_q = cu_seqlens_k
-            max_seqlen_in_batch_q = max_seqlen_in_batch_k
-            indices_q = indices_k
-        elif query_length == 1:
-            max_seqlen_in_batch_q = 1
-            cu_seqlens_q = torch.arange(
-                batch_size + 1, dtype=torch.int32, device=query_layer.device
-            )  # There is a memcpy here, that is very bad.
-            indices_q = cu_seqlens_q[:-1]
-            query_layer = query_layer.squeeze(1)
-        else:
-            # The -q_len: slice assumes left padding.
-            attention_mask = attention_mask[:, -query_length:]
-            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
-
-        return (
-            query_layer,
-            key_layer,
-            value_layer,
-            indices_q,
-            (cu_seqlens_q, cu_seqlens_k),
-            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
-        )
-
-
-ATTENTION_CLASSES = {
-    "eager": Attention,
-    "flash_attention_2": FlashAttention2,
-}
-
-
-class DecoderLayer(nn.Module):
-    def __init__(self, config: StableLMEpochConfig):
-        super().__init__()
-        self.self_attn = ATTENTION_CLASSES[config._attn_implementation](config=config)
-        self.mlp = MLP(config)
-        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
-        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
-
-    def forward(
-        self,
-        hidden_states: Optional[torch.FloatTensor],
-        attention_mask: Optional[torch.FloatTensor] = 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,
-    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[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 StableLMEpochPreTrainedModel(PreTrainedModel):
-    """An abstract class to handle weights initialization and a simple interface
-    for downloading and loading pretrained models.
-    """
-
-    config_class = StableLMEpochConfig
-    base_model_prefix = "transformer"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["DecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = True
-
-    def _init_weights(self, module: nn.Module):
-        """Initialize the weights"""
-        if isinstance(module, nn.Linear):
-            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: nn.Module, value=False):
-        if isinstance(module, StableLMEpochModel):
-            module.gradient_checkpointing = value
-
-
-class StableLMEpochModel(StableLMEpochPreTrainedModel):
-    def __init__(self, config: StableLMEpochConfig):
-        super().__init__(config)
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
-        self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
-        self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
-
-        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
-        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: nn.Module):
-        self.embed_tokens = value
-
-    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
-    def _prepare_decoder_attention_mask(
-        self,
-        attention_mask: torch.Tensor,
-        input_shape: torch.Size,
-        inputs_embeds: torch.Tensor,
-        past_key_values_length: int,
-    ):
-        # Create causal mask
-        # [batch_size, seq_len] -> [batch_size, 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:
-            # [batch_size, seq_len] -> [batch_size, 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: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[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 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 self._use_flash_attention_2:
-            # 2d mask is passed through the layers
-            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
-        else:
-            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(
-                    "`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, past_key_value, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(decoder_layer),
-                    hidden_states,
-                    attention_mask,
-                    position_ids,
-                )
-            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 StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config: StableLMEpochConfig):
-        super().__init__(config)
-
-        self.model = StableLMEpochModel(config)
-        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: nn.Module):
-        self.lm_head = new_embeddings
-
-    def get_decoder(self):
-        return self.model
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[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,
-            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).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: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        **kwargs,
-    ):
-        # Trim decoder_input_ids if past is used
-        if past_key_values is not None:
-            past_length = past_key_values[0][0].shape[2]
-
-            # Some generation methods already pass only the last input ID
-            if input_ids.shape[1] > past_length:
-                remove_prefix_length = past_length
-            else:
-                # Default to old behavior: keep only final ID
-                remove_prefix_length = input_ids.shape[1] - 1
-
-            input_ids = input_ids[:, remove_prefix_length:]
-
-        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(
-            {
-                "attention_mask": attention_mask,
-                "past_key_values": past_key_values,
-                "use_cache": kwargs.get("use_cache"),
-                "position_ids": position_ids,
-            }
-        )
-        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
-
-
-StableLMEpochConfig.register_for_auto_class()
-StableLMEpochForCausalLM.register_for_auto_class("AutoModelForCausalLM")

bunny/model/multimodal_encoder/builder.py

@@ -1,29 +0,0 @@
-import os
-from .eva_clip.eva_clip_encoder import EvaClipVisionTower
-from .siglip.siglip_encoder import SiglipVisionTower, SiglipVisionTowerS2
-from .clip.clip_encoder import CLIPVisionTower
-
-
-def build_vision_tower(vision_tower_cfg, **kwargs):
-    vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None))
-    use_s2 = getattr(vision_tower_cfg, 'use_s2', False)
-
-    if 'sig' in vision_tower.lower():
-        if use_s2:
-            return SiglipVisionTowerS2(vision_tower, args=vision_tower_cfg, **kwargs)
-        else:
-            return SiglipVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
-    elif 'eva' in vision_tower.lower():
-        if use_s2:
-            raise ValueError(f'Currently not supporting S2 for EVA-CLIP')
-        else:
-            return EvaClipVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
-
-    elif 'clip' in vision_tower.lower():
-        if use_s2:
-            raise ValueError(f'Currently not supporting S2 for CLIP')
-        else:
-            return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
-
-    else:
-        raise ValueError(f'Unknown vision tower: {vision_tower}')

bunny/model/multimodal_encoder/clip/clip_encoder.py

@@ -1,76 +0,0 @@
-import torch
-import torch.nn as nn
-
-from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
-
-
-class CLIPVisionTower(nn.Module):
-    def __init__(self, vision_tower, args, delay_load=False):
-        super().__init__()
-
-        self.is_loaded = False
-
-        self.vision_tower_name = vision_tower
-        self.select_layer = -2
-
-        if not delay_load:
-            self.load_model()
-        else:
-            self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
-
-    def load_model(self):
-        self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
-        self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)
-        self.vision_tower.requires_grad_(False)
-
-        self.is_loaded = True
-
-    def feature_select(self, image_forward_outs):
-        image_features = image_forward_outs.hidden_states[self.select_layer]
-
-        image_features = image_features[:, 1:]
-
-        return image_features
-
-    @torch.no_grad()
-    def forward(self, images):
-        if type(images) is list:
-            image_features = []
-            for image in images:
-                image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0),
-                                                      output_hidden_states=True)
-                image_feature = self.feature_select(image_forward_out).to(image.dtype)
-                image_features.append(image_feature)
-        else:
-            image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype),
-                                                   output_hidden_states=True)
-            image_features = self.feature_select(image_forward_outs).to(images.dtype)
-
-        return image_features
-
-    @property
-    def dummy_feature(self):
-        return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
-
-    @property
-    def dtype(self):
-        return self.vision_tower.dtype
-
-    @property
-    def device(self):
-        return self.vision_tower.device
-
-    @property
-    def config(self):
-        if self.is_loaded:
-            return self.vision_tower.config
-        else:
-            return self.cfg_only
-
-    @property
-    def hidden_size(self):
-        return self.config.hidden_size
-
-    @property
-    def num_patches(self):
-        return (self.config.image_size // self.config.patch_size) ** 2

bunny/model/multimodal_encoder/eva_clip/eva_clip_encoder.py

@@ -1,63 +0,0 @@
-import torch
-import torch.nn as nn
-
-from .eva_clip_processors import EvaClipImageTrainProcessor
-from .eva_vit import Eva2LargePlusEncoder
-
-
-class EvaClipVisionTower(nn.Module):
-    def __init__(self, vision_tower, args, delay_load=False):
-        super().__init__()
-
-        self.is_loaded = False
-
-        self.vision_tower_path = vision_tower
-        self.config = VisionTowerConfig()
-
-        if not delay_load:
-            self.load_model()
-        else:
-            self.cfg_only = self.config
-
-    def load_model(self):
-        self.image_processor = EvaClipImageTrainProcessor(self.config.image_size)
-        self.vision_tower = Eva2LargePlusEncoder(self.vision_tower_path)
-        self.vision_tower.requires_grad_(False)
-
-        self.is_loaded = True
-
-    @torch.no_grad()
-    def forward(self, images):
-        if type(images) is list:
-            image_features = []
-            for image in images:
-                image_feature = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0)).to(
-                    image.dtype)
-                image_features.append(image_feature)
-        else:
-            image_features = self.vision_tower(images.to(device=self.device, dtype=self.dtype)).to(images.dtype)
-
-        return image_features
-
-    @property
-    def dtype(self):
-        return self.vision_tower.dtype
-
-    @property
-    def device(self):
-        return self.vision_tower.device
-
-    @property
-    def hidden_size(self):
-        return self.config.hidden_size
-
-    @property
-    def num_patches(self):
-        return (self.config.image_size // self.config.patch_size) ** 2
-
-
-class VisionTowerConfig():
-    def __init__(self):
-        self.image_size = 336
-        self.patch_size = 14
-        self.hidden_size = 1024

bunny/model/multimodal_encoder/eva_clip/eva_clip_processors.py

@@ -1,68 +0,0 @@
-'''
-# Adapted from https://github.com/baaivision/EVA/tree/master/EVA-CLIP
-'''
-
-from torchvision import transforms
-from torchvision.transforms.functional import InterpolationMode
-from transformers.image_processing_utils import BatchFeature
-from PIL import Image
-from transformers.image_transforms import convert_to_rgb
-
-
-class BaseProcessor:
-    def __init__(self):
-        self.transform = lambda x: x
-        return
-
-    def __call__(self, item):
-        return self.transform(item)
-
-
-class EvaClipImageBaseProcessor(BaseProcessor):
-    def __init__(self, mean=None, std=None):
-        self.mean = (0.48145466, 0.4578275, 0.40821073) if mean is None else mean
-        self.std = (0.26862954, 0.26130258, 0.27577711) if std is None else std
-
-        self.normalize = transforms.Normalize(self.mean, self.std)
-
-    @property
-    def image_mean(self):
-        return self.mean
-
-
-class EvaClipImageTrainProcessor(EvaClipImageBaseProcessor):
-    def __init__(self, image_size=224, mean=None, std=None, min_scale=0.5, max_scale=1.0):
-        super().__init__(mean=mean, std=std)
-
-        self.transform = transforms.Compose(
-            [
-                convert_to_rgb,
-                transforms.Resize(
-                    image_size,
-                    interpolation=InterpolationMode.BICUBIC,
-                ),
-                transforms.CenterCrop(image_size),
-                transforms.ToTensor(),
-                self.normalize,
-            ]
-        )
-
-        self.image_size = image_size
-
-    def preprocess(self, images, return_tensors):
-        if isinstance(images, Image.Image):
-            images = [images]
-        else:
-            assert isinstance(images, list)
-
-        transformed_images = [self.transform(image).numpy() for image in images]
-        data = {"pixel_values": transformed_images}
-
-        return BatchFeature(data=data, tensor_type=return_tensors)
-
-    def __call__(self, item):
-        return self.transform(item)
-
-    @property
-    def crop_size(self):
-        return {'height': self.image_size, 'width': self.image_size}

bunny/model/multimodal_encoder/eva_clip/eva_vit.py

@@ -1,851 +0,0 @@
-'''
-# Adapted from https://github.com/baaivision/EVA/tree/master/EVA-CLIP
-'''
-
-from math import pi
-import torch
-from torch import nn
-from einops import rearrange, repeat
-import logging
-
-
-def broadcat(tensors, dim=-1):
-    num_tensors = len(tensors)
-    shape_lens = set(list(map(lambda t: len(t.shape), tensors)))
-    assert len(shape_lens) == 1, 'tensors must all have the same number of dimensions'
-    shape_len = list(shape_lens)[0]
-    dim = (dim + shape_len) if dim < 0 else dim
-    dims = list(zip(*map(lambda t: list(t.shape), tensors)))
-    expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim]
-    assert all(
-        [*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]), 'invalid dimensions for broadcastable concatentation'
-    max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims))
-    expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims))
-    expanded_dims.insert(dim, (dim, dims[dim]))
-    expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims)))
-    tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes)))
-    return torch.cat(tensors, dim=dim)
-
-
-def rotate_half(x):
-    x = rearrange(x, '... (d r) -> ... d r', r=2)
-    x1, x2 = x.unbind(dim=-1)
-    x = torch.stack((-x2, x1), dim=-1)
-    return rearrange(x, '... d r -> ... (d r)')
-
-
-class VisionRotaryEmbeddingFast(nn.Module):
-    def __init__(
-            self,
-            dim,
-            pt_seq_len,
-            ft_seq_len=None,
-            custom_freqs=None,
-            freqs_for='lang',
-            theta=10000,
-            max_freq=10,
-            num_freqs=1,
-            patch_dropout=0.
-    ):
-        super().__init__()
-        if custom_freqs:
-            freqs = custom_freqs
-        elif freqs_for == 'lang':
-            freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim))
-        elif freqs_for == 'pixel':
-            freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi
-        elif freqs_for == 'constant':
-            freqs = torch.ones(num_freqs).float()
-        else:
-            raise ValueError(f'unknown modality {freqs_for}')
-
-        if ft_seq_len is None: ft_seq_len = pt_seq_len
-        t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len
-
-        freqs = torch.einsum('..., f -> ... f', t, freqs)
-        freqs = repeat(freqs, '... n -> ... (n r)', r=2)
-        freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim=-1)
-
-        freqs_cos = freqs.cos().view(-1, freqs.shape[-1])
-        freqs_sin = freqs.sin().view(-1, freqs.shape[-1])
-
-        self.patch_dropout = patch_dropout
-
-        self.register_buffer("freqs_cos", freqs_cos)
-        self.register_buffer("freqs_sin", freqs_sin)
-
-        logging.info(f'Shape of rope freq: {self.freqs_cos.shape}')
-
-    def forward(self, t, patch_indices_keep=None):
-        if patch_indices_keep is not None:
-            batch = t.size()[0]
-            batch_indices = torch.arange(batch)
-            batch_indices = batch_indices[..., None]
-
-            freqs_cos = repeat(self.freqs_cos, 'i j -> n i m j', n=t.shape[0], m=t.shape[1])
-            freqs_sin = repeat(self.freqs_sin, 'i j -> n i m j', n=t.shape[0], m=t.shape[1])
-
-            freqs_cos = freqs_cos[batch_indices, patch_indices_keep]
-            freqs_cos = rearrange(freqs_cos, 'n i m j -> n m i j')
-            freqs_sin = freqs_sin[batch_indices, patch_indices_keep]
-            freqs_sin = rearrange(freqs_sin, 'n i m j -> n m i j')
-
-            return t * freqs_cos + rotate_half(t) * freqs_sin
-
-        return t * self.freqs_cos + rotate_half(t) * self.freqs_sin
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm (with cast back to input dtype)."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
-        return x.to(orig_type)
-
-
-class PatchDropout(nn.Module):
-    """
-    https://arxiv.org/abs/2212.00794
-    """
-
-    def __init__(self, prob, exclude_first_token=True):
-        super().__init__()
-        assert 0 <= prob < 1.
-        self.prob = prob
-        self.exclude_first_token = exclude_first_token  # exclude CLS token
-        logging.info(f"os.getenv('RoPE')={os.getenv('RoPE')}")
-
-    def forward(self, x):
-        if not self.training or self.prob == 0.:
-            return x
-
-        if self.exclude_first_token:
-            cls_tokens, x = x[:, :1], x[:, 1:]
-        else:
-            cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1])
-
-        batch = x.size()[0]
-        num_tokens = x.size()[1]
-
-        batch_indices = torch.arange(batch)
-        batch_indices = batch_indices[..., None]
-
-        keep_prob = 1 - self.prob
-        num_patches_keep = max(1, int(num_tokens * keep_prob))
-
-        rand = torch.randn(batch, num_tokens)
-        patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices
-
-        x = x[batch_indices, patch_indices_keep]
-
-        if self.exclude_first_token:
-            x = torch.cat((cls_tokens, x), dim=1)
-
-        if self.training and os.getenv('RoPE') == '1':
-            return x, patch_indices_keep
-
-        return x
-
-
-# --------------------------------------------------------
-# Adapted from  https://github.com/microsoft/unilm/tree/master/beit
-# --------------------------------------------------------
-import math
-import os
-from functools import partial
-import torch.nn as nn
-import torch.nn.functional as F
-
-try:
-    from timm.models.layers import drop_path, to_2tuple, trunc_normal_
-except:
-    from timm.layers import drop_path, to_2tuple, trunc_normal_
-
-if os.getenv('ENV_TYPE') == 'deepspeed':
-    try:
-        from deepspeed.runtime.activation_checkpointing.checkpointing import checkpoint
-    except:
-        from torch.utils.checkpoint import checkpoint
-else:
-    from torch.utils.checkpoint import checkpoint
-
-import xformers.ops as xops
-
-
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
-    """
-
-    def __init__(self, drop_prob=None):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training)
-
-    def extra_repr(self) -> str:
-        return 'p={}'.format(self.drop_prob)
-
-
-class Mlp(nn.Module):
-    def __init__(
-            self,
-            in_features,
-            hidden_features=None,
-            out_features=None,
-            act_layer=nn.GELU,
-            norm_layer=nn.LayerNorm,
-            drop=0.,
-            subln=False,
-
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-
-        self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity()
-
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        # x = self.drop(x)
-        # commit this for the orignal BERT implement 
-        x = self.ffn_ln(x)
-
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class SwiGLU(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.,
-                 norm_layer=nn.LayerNorm, subln=False):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-
-        self.w1 = nn.Linear(in_features, hidden_features)
-        self.w2 = nn.Linear(in_features, hidden_features)
-
-        self.act = act_layer()
-        self.ffn_ln = norm_layer(hidden_features) if subln else nn.Identity()
-        self.w3 = nn.Linear(hidden_features, out_features)
-
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x1 = self.w1(x)
-        x2 = self.w2(x)
-        hidden = self.act(x1) * x2
-        x = self.ffn_ln(hidden)
-        x = self.w3(x)
-        x = self.drop(x)
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(
-            self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
-            proj_drop=0., window_size=None, attn_head_dim=None, xattn=False, rope=None, subln=False,
-            norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        if attn_head_dim is not None:
-            head_dim = attn_head_dim
-        all_head_dim = head_dim * self.num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        self.subln = subln
-        if self.subln:
-            self.q_proj = nn.Linear(dim, all_head_dim, bias=False)
-            self.k_proj = nn.Linear(dim, all_head_dim, bias=False)
-            self.v_proj = nn.Linear(dim, all_head_dim, bias=False)
-        else:
-            self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
-
-        if qkv_bias:
-            self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
-            self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
-        else:
-            self.q_bias = None
-            self.v_bias = None
-
-        if window_size:
-            self.window_size = window_size
-            self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
-            self.relative_position_bias_table = nn.Parameter(
-                torch.zeros(self.num_relative_distance, num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-            # cls to token & token 2 cls & cls to cls
-
-            # get pair-wise relative position index for each token inside the window
-            coords_h = torch.arange(window_size[0])
-            coords_w = torch.arange(window_size[1])
-            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-            coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-            relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-            relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
-            relative_coords[:, :, 1] += window_size[1] - 1
-            relative_coords[:, :, 0] *= 2 * window_size[1] - 1
-            relative_position_index = \
-                torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
-            relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-            relative_position_index[0, 0:] = self.num_relative_distance - 3
-            relative_position_index[0:, 0] = self.num_relative_distance - 2
-            relative_position_index[0, 0] = self.num_relative_distance - 1
-
-            self.register_buffer("relative_position_index", relative_position_index)
-        else:
-            self.window_size = None
-            self.relative_position_bias_table = None
-            self.relative_position_index = None
-
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.inner_attn_ln = norm_layer(all_head_dim) if subln else nn.Identity()
-        # self.proj = nn.Linear(all_head_dim, all_head_dim)
-        self.proj = nn.Linear(all_head_dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.xattn = xattn
-        self.xattn_drop = attn_drop
-
-        self.rope = rope
-
-    def forward(self, x, rel_pos_bias=None, attn_mask=None):
-        B, N, C = x.shape
-        if self.subln:
-            q = F.linear(input=x, weight=self.q_proj.weight, bias=self.q_bias)
-            k = F.linear(input=x, weight=self.k_proj.weight, bias=None)
-            v = F.linear(input=x, weight=self.v_proj.weight, bias=self.v_bias)
-
-            q = q.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)  # B, num_heads, N, C
-            k = k.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
-            v = v.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
-        else:
-
-            qkv_bias = None
-            if self.q_bias is not None:
-                qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
-
-            qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
-            qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)  # 3, B, num_heads, N, C
-            q, k, v = qkv[0], qkv[1], qkv[2]
-
-        if self.rope:
-            # slightly fast impl
-            q_t = q[:, :, 1:, :]
-            ro_q_t = self.rope(q_t)
-            q = torch.cat((q[:, :, :1, :], ro_q_t), -2).type_as(v)
-
-            k_t = k[:, :, 1:, :]
-            ro_k_t = self.rope(k_t)
-            k = torch.cat((k[:, :, :1, :], ro_k_t), -2).type_as(v)
-
-        if self.xattn:
-            q = q.permute(0, 2, 1, 3)  # B, num_heads, N, C -> B, N, num_heads, C
-            k = k.permute(0, 2, 1, 3)
-            v = v.permute(0, 2, 1, 3)
-
-            x = xops.memory_efficient_attention(
-                q, k, v,
-                p=self.xattn_drop,
-                scale=self.scale,
-            )
-            x = x.reshape(B, N, -1)
-            x = self.inner_attn_ln(x)
-            x = self.proj(x)
-            x = self.proj_drop(x)
-        else:
-            q = q * self.scale
-            attn = (q @ k.transpose(-2, -1))
-
-            if self.relative_position_bias_table is not None:
-                relative_position_bias = \
-                    self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-                        self.window_size[0] * self.window_size[1] + 1,
-                        self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH
-                relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-                attn = attn + relative_position_bias.unsqueeze(0).type_as(attn)
-
-            if rel_pos_bias is not None:
-                attn = attn + rel_pos_bias.type_as(attn)
-
-            if attn_mask is not None:
-                attn_mask = attn_mask.bool()
-                attn = attn.masked_fill(~attn_mask[:, None, None, :], float("-inf"))
-
-            attn = attn.softmax(dim=-1)
-            attn = self.attn_drop(attn)
-
-            x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
-            x = self.inner_attn_ln(x)
-            x = self.proj(x)
-            x = self.proj_drop(x)
-        return x
-
-
-class Block(nn.Module):
-
-    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
-                 window_size=None, attn_head_dim=None, xattn=False, rope=None, postnorm=False,
-                 subln=False, naiveswiglu=False):
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
-            attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim,
-            xattn=xattn, rope=rope, subln=subln, norm_layer=norm_layer)
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-
-        if naiveswiglu:
-            self.mlp = SwiGLU(
-                in_features=dim,
-                hidden_features=mlp_hidden_dim,
-                subln=subln,
-                norm_layer=norm_layer,
-            )
-        else:
-            self.mlp = Mlp(
-                in_features=dim,
-                hidden_features=mlp_hidden_dim,
-                act_layer=act_layer,
-                subln=subln,
-                drop=drop
-            )
-
-        if init_values is not None and init_values > 0:
-            self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
-            self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
-        else:
-            self.gamma_1, self.gamma_2 = None, None
-
-        self.postnorm = postnorm
-
-    def forward(self, x, rel_pos_bias=None, attn_mask=None):
-        if self.gamma_1 is None:
-            if self.postnorm:
-                x = x + self.drop_path(self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)))
-                x = x + self.drop_path(self.norm2(self.mlp(x)))
-            else:
-                x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))
-                x = x + self.drop_path(self.mlp(self.norm2(x)))
-        else:
-            if self.postnorm:
-                x = x + self.drop_path(
-                    self.gamma_1 * self.norm1(self.attn(x, rel_pos_bias=rel_pos_bias, attn_mask=attn_mask)))
-                x = x + self.drop_path(self.gamma_2 * self.norm2(self.mlp(x)))
-            else:
-                x = x + self.drop_path(
-                    self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, attn_mask=attn_mask))
-                x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
-        return x
-
-
-class PatchEmbed(nn.Module):
-    """ Image to Patch Embedding
-    """
-
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
-        self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.num_patches = num_patches
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-
-    def forward(self, x, **kwargs):
-        B, C, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        assert H == self.img_size[0] and W == self.img_size[1], \
-            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x).flatten(2).transpose(1, 2)
-        return x
-
-
-class RelativePositionBias(nn.Module):
-
-    def __init__(self, window_size, num_heads):
-        super().__init__()
-        self.window_size = window_size
-        self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros(self.num_relative_distance, num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-        # cls to token & token 2 cls & cls to cls
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(window_size[0])
-        coords_w = torch.arange(window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * window_size[1] - 1
-        relative_position_index = \
-            torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
-        relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        relative_position_index[0, 0:] = self.num_relative_distance - 3
-        relative_position_index[0:, 0] = self.num_relative_distance - 2
-        relative_position_index[0, 0] = self.num_relative_distance - 1
-
-        self.register_buffer("relative_position_index", relative_position_index)
-
-    def forward(self):
-        relative_position_bias = \
-            self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-                self.window_size[0] * self.window_size[1] + 1,
-                self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH
-        return relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-
-
-class EVAVisionTransformer(nn.Module):
-    """ Vision Transformer with support for patch or hybrid CNN input stage
-    """
-
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
-                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
-                 drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None, patch_dropout=0.,
-                 use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, rope=False,
-                 use_mean_pooling=True, init_scale=0.001, grad_checkpointing=False, xattn=False, postnorm=False,
-                 pt_hw_seq_len=16, intp_freq=False, naiveswiglu=False, subln=False):
-        super().__init__()
-        self.image_size = img_size
-        self.num_classes = num_classes
-        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
-        num_patches = self.patch_embed.num_patches
-
-        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
-        # self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
-        if use_abs_pos_emb:
-            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
-        else:
-            self.pos_embed = None
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        if use_shared_rel_pos_bias:
-            self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads)
-        else:
-            self.rel_pos_bias = None
-
-        if rope:
-            half_head_dim = embed_dim // num_heads // 2
-            hw_seq_len = img_size // patch_size
-            self.rope = VisionRotaryEmbeddingFast(
-                dim=half_head_dim,
-                pt_seq_len=pt_hw_seq_len,
-                ft_seq_len=hw_seq_len if intp_freq else None,
-                # patch_dropout=patch_dropout
-            )
-        else:
-            self.rope = None
-
-        self.naiveswiglu = naiveswiglu
-
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
-        self.use_rel_pos_bias = use_rel_pos_bias
-        self.blocks = nn.ModuleList([
-            Block(
-                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
-                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
-                init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None,
-                xattn=xattn, rope=self.rope, postnorm=postnorm, subln=subln, naiveswiglu=naiveswiglu)
-            for i in range(depth)])
-        self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
-        self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
-        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
-        if self.pos_embed is not None:
-            trunc_normal_(self.pos_embed, std=.02)
-
-        trunc_normal_(self.cls_token, std=.02)
-        # trunc_normal_(self.mask_token, std=.02)
-
-        self.apply(self._init_weights)
-        self.fix_init_weight()
-
-        if isinstance(self.head, nn.Linear):
-            trunc_normal_(self.head.weight, std=.02)
-            self.head.weight.data.mul_(init_scale)
-            self.head.bias.data.mul_(init_scale)
-
-        # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn
-        self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity()
-
-        self.grad_checkpointing = grad_checkpointing
-
-    def fix_init_weight(self):
-        def rescale(param, layer_id):
-            param.div_(math.sqrt(2.0 * layer_id))
-
-        for layer_id, layer in enumerate(self.blocks):
-            rescale(layer.attn.proj.weight.data, layer_id + 1)
-            if self.naiveswiglu:
-                rescale(layer.mlp.w3.weight.data, layer_id + 1)
-            else:
-                rescale(layer.mlp.fc2.weight.data, layer_id + 1)
-
-    def get_cast_dtype(self) -> torch.dtype:
-        return self.blocks[0].mlp.fc2.weight.dtype
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    def get_num_layers(self):
-        return len(self.blocks)
-
-    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
-        assert unlocked_groups == 0, 'partial locking not currently supported for this model'
-        for param in self.parameters():
-            param.requires_grad = False
-
-    @torch.jit.ignore
-    def set_grad_checkpointing(self, enable=True):
-        self.grad_checkpointing = enable
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'pos_embed', 'cls_token'}
-
-    def get_classifier(self):
-        return self.head
-
-    def reset_classifier(self, num_classes, global_pool=''):
-        self.num_classes = num_classes
-        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
-    def forward_features(self, x, return_all_features=False):
-
-        x = self.patch_embed(x)
-        batch_size, seq_len, _ = x.size()
-
-        cls_tokens = self.cls_token.expand(batch_size, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
-        x = torch.cat((cls_tokens, x), dim=1)
-        if self.pos_embed is not None:
-            x = x + self.pos_embed
-        x = self.pos_drop(x)
-
-        # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in
-        if os.getenv('RoPE') == '1':
-            if self.training and not isinstance(self.patch_dropout, nn.Identity):
-                x, patch_indices_keep = self.patch_dropout(x)
-                self.rope.forward = partial(self.rope.forward, patch_indices_keep=patch_indices_keep)
-            else:
-                self.rope.forward = partial(self.rope.forward, patch_indices_keep=None)
-                x = self.patch_dropout(x)
-        else:
-            x = self.patch_dropout(x)
-
-        rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
-        for i, blk in enumerate(self.blocks):
-            if i == len(self.blocks) - 1:
-                continue
-            if self.grad_checkpointing:
-                x = checkpoint(blk, x, (rel_pos_bias,))
-            else:
-                x = blk(x, rel_pos_bias=rel_pos_bias)
-
-        if not return_all_features:
-            x = self.norm(x)
-            if self.fc_norm is not None:
-                return self.fc_norm(x.mean(1))
-            else:
-                return x[:, 0]
-        return x
-
-    def forward(self, x, return_all_features=False):
-        if return_all_features:
-            return self.forward_features(x, return_all_features)
-        x = self.forward_features(x)
-        x = self.head(x)
-        return x
-
-
-def load_state_dict(checkpoint_path: str, map_location: str = 'cpu', model_key: str = 'model|module|state_dict',
-                    is_openai: bool = False, skip_list: list = []):
-    if is_openai:
-        model = torch.jit.load(checkpoint_path, map_location="cpu").eval()
-        state_dict = model.state_dict()
-        for key in ["input_resolution", "context_length", "vocab_size"]:
-            state_dict.pop(key, None)
-    else:
-        checkpoint = torch.load(checkpoint_path, map_location=map_location)
-        for mk in model_key.split('|'):
-            if isinstance(checkpoint, dict) and mk in checkpoint:
-                state_dict = checkpoint[mk]
-                break
-            else:
-                state_dict = checkpoint
-        if next(iter(state_dict.items()))[0].startswith('module'):
-            state_dict = {k[7:]: v for k, v in state_dict.items()}
-
-    for k in skip_list:
-        if k in list(state_dict.keys()):
-            logging.info(f"Removing key {k} from pretrained checkpoint")
-            del state_dict[k]
-
-    if os.getenv('RoPE') == '1':
-        for k in list(state_dict.keys()):
-            if 'freqs_cos' in k or 'freqs_sin' in k:
-                del state_dict[k]
-    return state_dict
-
-
-def load_clip_visual_state_dict(checkpoint_path: str, map_location: str = 'cpu', is_openai: bool = False,
-                                skip_list: list = []):
-    state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list)
-
-    for k in list(state_dict.keys()):
-        if not k.startswith('visual.'):
-            del state_dict[k]
-    for k in list(state_dict.keys()):
-        if k.startswith('visual.'):
-            new_k = k[7:]
-            state_dict[new_k] = state_dict[k]
-            del state_dict[k]
-    return state_dict
-
-
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union
-
-try:
-    from apex.normalization import FusedLayerNorm
-except:
-    FusedLayerNorm = LayerNorm
-    print(
-        "Please build and install Nvidia apex package with option '--cuda_ext' according to https://github.com/NVIDIA/apex#from-source .")
-
-
-@dataclass
-class CLIPVisionCfg:
-    layers: Union[Tuple[int, int, int, int], int] = 12
-    width: int = 768
-    head_width: int = 64
-    mlp_ratio: float = 4.0
-    patch_size: int = 16
-    image_size: Union[Tuple[int, int], int] = 224
-    ls_init_value: Optional[float] = None  # layer scale initial value
-    patch_dropout: float = 0.  # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results
-    global_average_pool: bool = False  # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580)
-    drop_path_rate: Optional[float] = None  # drop path rate
-    timm_model_name: str = None  # a valid model name overrides layers, width, patch_size
-    timm_model_pretrained: bool = False  # use (imagenet) pretrained weights for named model
-    timm_pool: str = 'avg'  # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
-    timm_proj: str = 'linear'  # linear projection for timm model output ('linear', 'mlp', '')
-    timm_proj_bias: bool = False  # enable bias final projection
-    eva_model_name: str = None  # a valid eva model name overrides layers, width, patch_size
-    qkv_bias: bool = True
-    fusedLN: bool = False
-    xattn: bool = False
-    postnorm: bool = False
-    rope: bool = False
-    pt_hw_seq_len: int = 16  # 224/14
-    intp_freq: bool = False
-    naiveswiglu: bool = False
-    subln: bool = False
-
-
-def _build_vision_tower(
-        vision_tower_path: str,
-        embed_dim: int,
-        vision_cfg: CLIPVisionCfg
-):
-    if isinstance(vision_cfg, dict):
-        vision_cfg = CLIPVisionCfg(**vision_cfg)
-
-    if vision_cfg.eva_model_name:
-        vision_heads = vision_cfg.width // vision_cfg.head_width
-        norm_layer = LayerNorm
-
-        visual = EVAVisionTransformer(
-            img_size=vision_cfg.image_size,
-            patch_size=vision_cfg.patch_size,
-            num_classes=embed_dim,
-            use_mean_pooling=vision_cfg.global_average_pool,  # False
-            init_values=vision_cfg.ls_init_value,
-            patch_dropout=vision_cfg.patch_dropout,
-            embed_dim=vision_cfg.width,
-            depth=vision_cfg.layers,
-            num_heads=vision_heads,
-            mlp_ratio=vision_cfg.mlp_ratio,
-            qkv_bias=vision_cfg.qkv_bias,
-            drop_path_rate=vision_cfg.drop_path_rate,
-            norm_layer=partial(FusedLayerNorm, eps=1e-6) if vision_cfg.fusedLN else partial(norm_layer, eps=1e-6),
-            xattn=vision_cfg.xattn,
-            rope=vision_cfg.rope,
-            postnorm=vision_cfg.postnorm,
-            pt_hw_seq_len=vision_cfg.pt_hw_seq_len,  # 224/14
-            intp_freq=vision_cfg.intp_freq,
-            naiveswiglu=vision_cfg.naiveswiglu,
-            subln=vision_cfg.subln
-        )
-
-        state_dict = load_clip_visual_state_dict(vision_tower_path)
-        incompatible_keys = visual.load_state_dict(state_dict, strict=False)
-        print('EVA-CLIP incompatible_keys:', incompatible_keys)
-
-    return visual
-
-
-class Eva2LargePlusEncoder(nn.Module):
-    def __init__(self, vision_tower_path):
-        super(Eva2LargePlusEncoder, self).__init__()
-        self.config = {
-            "embed_dim": 768,
-            "vision_cfg": {
-                "image_size": 336,
-                "layers": 24,
-                "width": 1024,
-                "drop_path_rate": 0,
-                "head_width": 64,
-                "mlp_ratio": 2.6667,
-                "patch_size": 14,
-                "eva_model_name": "eva-clip-l-14-336",
-                "xattn": True,
-                "fusedLN": True,
-                "rope": True,
-                "pt_hw_seq_len": 16,
-                "intp_freq": True,
-                "naiveswiglu": True,
-                "subln": True
-            }
-        }
-
-        self.config['vision_tower_path'] = vision_tower_path
-        self.model = _build_vision_tower(**self.config)
-
-    def forward(self, image, **kwargs):
-        encode = self.model(image, return_all_features=True)[:, 1:, :]
-        return encode
-
-    @property
-    def dtype(self):
-        return list(self.parameters())[-1].dtype
-
-    @property
-    def device(self):
-        return list(self.parameters())[-1].device

bunny/model/multimodal_encoder/siglip/siglip_encoder.py

@@ -1,129 +0,0 @@
-import torch
-import torch.nn as nn
-
-from transformers import SiglipVisionModel, SiglipImageProcessor, SiglipVisionConfig
-from bunny.util.s2wrapper import forward as multiscale_forward
-
-
-class SiglipVisionTower(nn.Module):
-    def __init__(self, vision_tower, args, delay_load=False):
-        super().__init__()
-
-        self.is_loaded = False
-
-        self.vision_tower_name = vision_tower
-        self.select_layer = -2
-
-        if not delay_load:
-            self.load_model()
-        else:
-            self.cfg_only = SiglipVisionConfig.from_pretrained(self.vision_tower_name)
-
-    def load_model(self):
-        self.image_processor = SiglipImageProcessor.from_pretrained(self.vision_tower_name)
-        self.image_processor.crop_size = self.image_processor.size
-        self.vision_tower = SiglipVisionModel.from_pretrained(self.vision_tower_name)
-        self.vision_tower.requires_grad_(False)
-
-        self.is_loaded = True
-
-    def feature_select(self, image_forward_outs):
-        image_features = image_forward_outs.hidden_states[self.select_layer]
-
-        return image_features
-
-    @torch.no_grad()
-    def forward(self, images):
-        if type(images) is list:
-            image_features = []
-            for image in images:
-                image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0),
-                                                      output_hidden_states=True)
-                image_feature = self.feature_select(image_forward_out).to(image.dtype)
-                image_features.append(image_feature)
-        else:
-            image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype),
-                                                   output_hidden_states=True)
-            image_features = self.feature_select(image_forward_outs).to(images.dtype)
-
-        return image_features
-
-    @property
-    def dummy_feature(self):
-        return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
-
-    @property
-    def dtype(self):
-        return self.vision_tower.dtype
-
-    @property
-    def device(self):
-        return self.vision_tower.device
-
-    @property
-    def config(self):
-        if self.is_loaded:
-            return self.vision_tower.config
-        else:
-            return self.cfg_only
-
-    @property
-    def hidden_size(self):
-        return self.config.hidden_size
-
-    @property
-    def num_patches(self):
-        return (self.config.image_size // self.config.patch_size) ** 2
-
-
-class SiglipVisionTowerS2(SiglipVisionTower):
-    def __init__(self, vision_tower, args, delay_load=False):
-        self.s2_scales = getattr(args, 's2_scales', '384,768,1152')
-        self.s2_scales = list(map(int, self.s2_scales.split(',')))
-        self.s2_scales.sort()
-        self.s2_split_size = self.s2_scales[0]
-        self.s2_image_size = self.s2_scales[-1]
-
-        super().__init__(vision_tower, args, delay_load)
-
-        self.multiscale_forward = multiscale_forward
-
-        if not delay_load:
-            self.image_processor.size['height'] = self.image_processor.size['width'] = self.s2_image_size
-            self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.s2_image_size
-
-    def load_model(self):
-        self.image_processor = SiglipImageProcessor.from_pretrained(self.vision_tower_name)
-        self.image_processor.crop_size = self.image_processor.size
-        self.vision_tower = SiglipVisionModel.from_pretrained(self.vision_tower_name)
-        self.vision_tower.requires_grad_(False)
-
-        self.image_processor.size['height'] = self.image_processor.size['width'] = self.s2_image_size
-        self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.s2_image_size
-
-        self.is_loaded = True
-
-    @torch.no_grad()
-    def forward_feature(self, images):
-        image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype),
-                                               output_hidden_states=True)
-        image_features = self.feature_select(image_forward_outs).to(images.dtype)
-        return image_features
-
-    @torch.no_grad()
-    def forward(self, images):
-        if type(images) is list:
-            image_features = []
-            for image in images:
-                image_feature = self.multiscale_forward(self.forward_feature, image.unsqueeze(0),
-                                                        img_sizes=self.s2_scales, max_split_size=self.s2_split_size)
-                image_features.append(image_feature)
-        else:
-            image_features = self.multiscale_forward(self.forward_feature, images, img_sizes=self.s2_scales,
-                                                     max_split_size=self.s2_split_size)
-
-        return image_features
-
-    @property
-    def hidden_size(self):
-        return self.config.hidden_size * len(self.s2_scales)