init

app.py

@@ -1,67 +1,327 @@
+import os
+import time
+
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-from transformers import LlamaForCausalLM
-model = LlamaForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
-print(model)
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+import argparse
+try:
+    from ..model.ea_model import EaModel
+except:
+    from eagle.model.ea_model import EaModel
+import torch
+from fastchat.model import get_conversation_template
+import re
+
+
+def truncate_list(lst, num):
+    if num not in lst:
+        return lst
+
+
+    first_index = lst.index(num)
+
+
+    return lst[:first_index + 1]
+
+
+
+
+
+def find_list_markers(text):
+
+    pattern = re.compile(r'(?m)(^\d+\.\s|\n)')
+    matches = pattern.finditer(text)
+
+
+    return [(match.start(), match.end()) for match in matches]
+
+
+def checkin(pointer,start,marker):
+    for b,e in marker:
+        if b<=pointer<e:
+            return True
+        if b<=start<e:
+            return True
+    return False
+
+def highlight_text(text, text_list,color="black"):
+
+    pointer = 0
+    result = ""
+    markers=find_list_markers(text)
+
+
+    for sub_text in text_list:
+
+        start = text.find(sub_text, pointer)
+        if start==-1:
+            continue
+        end = start + len(sub_text)
+
+
+        if checkin(pointer,start,markers):
+            result += text[pointer:start]
+        else:
+            result += f"<span style='color: {color};'>{text[pointer:start]}</span>"
+
+        result += sub_text
+
+        pointer = end
+
+    if pointer < len(text):
+        result += f"<span style='color: {color};'>{text[pointer:]}</span>"
+
+    return result
+
+
+def warmup(model):
+    conv = get_conversation_template(args.model_type)
+
+    if args.model_type == "llama-2-chat":
+        sys_p = "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.\n\nIf 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."
+        conv.system_message = sys_p
+    elif args.model_type == "mixtral":
+        conv = get_conversation_template("llama-2-chat")
+        conv.system_message = ''
+        conv.sep2 = "</s>"
+    conv.append_message(conv.roles[0], "Hello")
+    conv.append_message(conv.roles[1], None)
+    prompt = conv.get_prompt()
+    if args.model_type == "llama-2-chat":
+        prompt += " "
+    input_ids = model.tokenizer([prompt]).input_ids
+    input_ids = torch.as_tensor(input_ids).cuda()
+    for output_ids in model.ea_generate(input_ids):
+        ol=output_ids.shape[1]
+
+def bot(history, temperature, top_p, use_EaInfer, highlight_EaInfer,session_state,):
+    if not history:
+        return history, "0.00 tokens/s", "0.00", session_state
+    pure_history = session_state.get("pure_history", [])
+    assert args.model_type == "llama-2-chat" or "vicuna"
+    conv = get_conversation_template(args.model_type)
+
+    if args.model_type == "llama-2-chat":
+        sys_p = "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.\n\nIf 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."
+        conv.system_message = sys_p
+    elif args.model_type == "mixtral":
+        conv = get_conversation_template("llama-2-chat")
+        conv.system_message = ''
+        conv.sep2 = "</s>"
+    elif args.model_type == "llama-3-instruct":
+        messages = [
+            {"role": "system",
+             "content": "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.\n\nIf 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."},
+        ]
+
+    for query, response in pure_history:
+        if args.model_type == "llama-3-instruct":
+            messages.append({
+                "role": "user",
+                "content": query
+            })
+            if response!=None:
+                messages.append({
+                    "role": "assistant",
+                    "content": response
+                })
+        else:
+            conv.append_message(conv.roles[0], query)
+            if args.model_type == "llama-2-chat" and response:
+                response = " " + response
+            conv.append_message(conv.roles[1], response)
+
+    if args.model_type == "llama-3-instruct":
+        prompt = model.tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+    else:
+        prompt = conv.get_prompt()
+
+    if args.model_type == "llama-2-chat":
+        prompt += " "
+
+    input_ids = model.tokenizer([prompt]).input_ids
+    input_ids = torch.as_tensor(input_ids).cuda()
+    input_len = input_ids.shape[1]
+    naive_text = []
+    cu_len = input_len
+    totaltime=0
+    start_time=time.time()
+    total_ids=0
+    if use_EaInfer:
+
+        for output_ids in model.ea_generate(input_ids, temperature=temperature, top_p=top_p,
+                                            max_new_tokens=args.max_new_token,is_llama3=args.model_type=="llama-3-instruct"):
+            totaltime+=(time.time()-start_time)
+            total_ids+=1
+            decode_ids = output_ids[0, input_len:].tolist()
+            decode_ids = truncate_list(decode_ids, model.tokenizer.eos_token_id)
+            if args.model_type == "llama-3-instruct":
+                decode_ids = truncate_list(decode_ids, model.tokenizer.convert_tokens_to_ids("<|eot_id|>"))
+            text = model.tokenizer.decode(decode_ids, skip_special_tokens=True, spaces_between_special_tokens=False,
+                                          clean_up_tokenization_spaces=True, )
+
+            naive_text.append(model.tokenizer.decode(output_ids[0, cu_len], skip_special_tokens=True,
+                                                     spaces_between_special_tokens=False,
+                                                     clean_up_tokenization_spaces=True, ))
+
+            cu_len = output_ids.shape[1]
+            colored_text = highlight_text(text, naive_text, "orange")
+            if highlight_EaInfer:
+                history[-1][1] = colored_text
+            else:
+                history[-1][1] = text
+            pure_history[-1][1] = text
+            session_state["pure_history"] = pure_history
+            new_tokens = cu_len-input_len
+            yield history,f"{new_tokens/totaltime:.2f} tokens/s",f"{new_tokens/total_ids:.2f}",session_state
+            start_time = time.time()
+
+
+    else:
+        for output_ids in model.naive_generate(input_ids, temperature=temperature, top_p=top_p,
+                                            max_new_tokens=args.max_new_token,is_llama3=args.model_type=="llama-3-instruct"):
+            totaltime += (time.time() - start_time)
+            total_ids+=1
+            decode_ids = output_ids[0, input_len:].tolist()
+            decode_ids = truncate_list(decode_ids, model.tokenizer.eos_token_id)
+            text = model.tokenizer.decode(decode_ids, skip_special_tokens=True, spaces_between_special_tokens=False,
+                                          clean_up_tokenization_spaces=True, )
+            naive_text.append(model.tokenizer.decode(output_ids[0, cu_len], skip_special_tokens=True,
+                                                     spaces_between_special_tokens=False,
+                                                     clean_up_tokenization_spaces=True, ))
+            cu_len = output_ids.shape[1]
+            colored_text = highlight_text(text, naive_text, "orange")
+            if highlight_EaInfer and use_EaInfer:
+                history[-1][1] = colored_text
+            else:
+                history[-1][1] = text
+            history[-1][1] = text
+            pure_history[-1][1] = text
+            new_tokens = cu_len - input_len
+            yield history,f"{new_tokens/totaltime:.2f} tokens/s",f"{new_tokens/total_ids:.2f}",session_state
+            start_time = time.time()
+
+
+def user(user_message, history,session_state):
+    if history==None:
+        history=[]
+    pure_history = session_state.get("pure_history", [])
+    pure_history += [[user_message, None]]
+    session_state["pure_history"] = pure_history
+    return "", history + [[user_message, None]],session_state
+
+
+def regenerate(history,session_state):
+    if not history:
+        return history, None,"0.00 tokens/s","0.00",session_state
+    pure_history = session_state.get("pure_history", [])
+    pure_history[-1][-1] = None
+    session_state["pure_history"]=pure_history
+    if len(history) > 1:  # Check if there's more than one entry in history (i.e., at least one bot response)
+        new_history = history[:-1]  # Remove the last bot response
+        last_user_message = history[-1][0]  # Get the last user message
+        return new_history + [[last_user_message, None]], None,"0.00 tokens/s","0.00",session_state
+    history[-1][1] = None
+    return history, None,"0.00 tokens/s","0.00",session_state
+
+
+def clear(history,session_state):
+    pure_history = session_state.get("pure_history", [])
+    pure_history = []
+    session_state["pure_history"] = pure_history
+    return [],"0.00 tokens/s","0.00",session_state
+
+
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--ea-model-path",
+    type=str,
+    default="yuhuili/EAGLE-LLaMA3-Instruct-8B",
+    help="The path to the weights. This can be a local folder or a Hugging Face repo ID.",
+)
+parser.add_argument("--base-model-path", type=str, default="meta-llama/Meta-Llama-3-8B-Instruct",
+                    help="path of basemodel, huggingface project or local path")
+parser.add_argument(
+    "--load-in-8bit", action="store_true", help="Use 8-bit quantization"
+)
+parser.add_argument(
+    "--load-in-4bit", action="store_true", help="Use 4-bit quantization"
+)
+parser.add_argument("--model-type", type=str, default="llama-3-instruct",choices=["llama-2-chat","vicuna","mixtral","llama-3-instruct"])
+parser.add_argument(
+    "--total-token",
+    type=int,
+    default=59,
+    help="The maximum number of new generated tokens.",
+)
+parser.add_argument(
+    "--max-new-token",
+    type=int,
+    default=512,
+    help="The maximum number of new generated tokens.",
 )
+args = parser.parse_args()
+
+model = EaModel.from_pretrained(
+    base_model_path=args.base_model_path,
+    ea_model_path=args.ea_model_path,
+    total_token=args.total_token,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
+    load_in_4bit=args.load_in_4bit,
+    load_in_8bit=args.load_in_8bit,
+    device_map="auto",
+)
+model.eval()
+warmup(model)
+
+custom_css = """
+#speed textarea {
+    color: red;   
+    font-size: 30px; 
+}"""
+
+with gr.Blocks(css=custom_css) as demo:
+    gs = gr.State({"pure_history": []})
+    gr.Markdown('''## EAGLE-2 Chatbot''')
+    with gr.Row():
+        speed_box = gr.Textbox(label="Speed", elem_id="speed", interactive=False, value="0.00 tokens/s")
+        compression_box = gr.Textbox(label="Compression Ratio", elem_id="speed", interactive=False, value="0.00")
+    with gr.Row():
+        with gr.Column():
+            use_EaInfer = gr.Checkbox(label="Use EAGLE-2", value=True)
+            highlight_EaInfer = gr.Checkbox(label="Highlight the tokens generated by EAGLE-2", value=True)
+        temperature = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="temperature", value=0.5)
+        top_p = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="top_p", value=0.9)
+    note=gr.Markdown(show_label=False,value='''The original LLM is LLaMA3-Instruct 8B, running on a single RTX 3090. The Compression Ratio is defined as the number of generated tokens divided by the number of forward passes in the original LLM. If "Highlight the tokens generated by EAGLE-2" is checked, the tokens correctly guessed by EAGLE-2 
+    will be displayed in orange. Note: Checking this option may cause special formatting rendering issues in a few cases, especially when generating code''')
+
+
+    chatbot = gr.Chatbot(height=600,show_label=False)
+
 
+    msg = gr.Textbox(label="Your input")
+    with gr.Row():
+        send_button = gr.Button("Send")
+        stop_button = gr.Button("Stop")
+        regenerate_button = gr.Button("Regenerate")
+        clear_button = gr.Button("Clear")
+    enter_event=msg.submit(user, [msg, chatbot,gs], [msg, chatbot,gs], queue=True).then(
+        bot, [chatbot, temperature, top_p, use_EaInfer, highlight_EaInfer,gs], [chatbot,speed_box,compression_box,gs]
+    )
+    clear_button.click(clear, [chatbot,gs], [chatbot,speed_box,compression_box,gs], queue=True)
 
-if __name__ == "__main__":
-    demo.launch()
+    send_event=send_button.click(user, [msg, chatbot,gs], [msg, chatbot,gs],queue=True).then(
+        bot, [chatbot, temperature, top_p, use_EaInfer, highlight_EaInfer,gs], [chatbot,speed_box,compression_box,gs]
+    )
+    regenerate_event=regenerate_button.click(regenerate, [chatbot,gs], [chatbot, msg,speed_box,compression_box,gs],queue=True).then(
+        bot, [chatbot, temperature, top_p, use_EaInfer, highlight_EaInfer,gs], [chatbot,speed_box,compression_box,gs]
+    )
+    stop_button.click(fn=None, inputs=None, outputs=None, cancels=[send_event,regenerate_event,enter_event])
+demo.queue()
+demo.launch(share=True)

model/cnets.py

@@ -0,0 +1,923 @@
+# 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 copy
+import os
+# os.environ["CUDA_VISIBLE_DEVICES"] = "5"
+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.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, \
+    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,
+    logging,
+    replace_return_docstrings,
+)
+
+try:
+    from .configs import EConfig
+    from .choices import *
+except:
+    from configs import EConfig
+    from choices import *
+    from utils import prepare_logits_processor
+
+import time
+
+
+class Timer:
+    def __init__(self, name):
+        self.name = name
+
+    def __enter__(self):
+        torch.cuda.synchronize()
+        self.start = time.perf_counter()
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        torch.cuda.synchronize()
+        elapsed = time.perf_counter() - self.start
+        print(f'{self.name} took {elapsed} seconds')
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+        input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+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)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2:]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class LlamaRotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+    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 LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
+class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                    (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
+class LlamaAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            if hasattr(self.config, "rope_theta"):
+                self.rotary_emb = LlamaRotaryEmbedding(self.head_dim,
+                                                       max_position_embeddings=self.max_position_embeddings,
+                                                       base=self.config.rope_theta)
+            else:
+                self.rotary_emb = LlamaRotaryEmbedding(self.head_dim,
+                                                       max_position_embeddings=self.max_position_embeddings)
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
+                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Tuple[torch.Tensor]] = None,
+            output_attentions: bool = False,
+            use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        if self.config.pretraining_tp > 1:
+            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
+            query_slices = self.q_proj.weight.split(
+                (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
+            )
+            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+            query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
+            query_states = torch.cat(query_states, dim=-1)
+
+            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
+            key_states = torch.cat(key_states, dim=-1)
+
+            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
+            value_states = torch.cat(value_states, dim=-1)
+
+        else:
+            query_states = self.q_proj(hidden_states)
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        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()}"
+            )
+
+        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 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
+
+
+class LlamaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        LlamaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class LlamaDecoderLayer(nn.Module):
+    def __init__(self, config, index):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = LlamaAttention(config=config)
+        self.mlp = LlamaMLP(config)
+        self.index = index
+        if self.index != 0:
+            self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Tuple[torch.Tensor]] = None,
+            output_attentions: Optional[bool] = False,
+            use_cache: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+
+        residual = hidden_states
+
+        if self.index != 0:
+            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 I(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.dummy = nn.Parameter(torch.ones(1, dtype=torch.float32))
+
+    def forward(self, x):
+        return x + self.dummy - self.dummy  # (also tried x+self.dummy)
+
+
+def len_list(x, n):
+    return [i for i in x if len(i) <= n]
+
+
+class Model(nn.Module):
+    def __init__(self, config, load_emb=False, path=None, bias=True, total_tokens=63, depth=5, top_k=8, threshold=1.0):
+        super().__init__()
+
+        self.gradient_checkpointing = True
+        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)
+        if load_emb:
+            from safetensors import safe_open
+            import json
+            try:
+                with open(os.path.join(path, "model.safetensors.index.json"), "r") as f:
+                    index_json = json.loads(f.read())
+                    emb_path = index_json["weight_map"]["model.embed_tokens.weight"]
+                with safe_open(os.path.join(path, emb_path),
+                               framework="pt",
+                               device="cpu") as f:
+                    tensor_slice = f.get_slice("model.embed_tokens.weight")
+                    vocab_size, hidden_dim = tensor_slice.get_shape()
+                    tensor = tensor_slice[:, :hidden_dim].float()
+            except:
+                with open(os.path.join(path, "pytorch_model.bin.index.json"), "r") as f:
+                    index_json = json.loads(f.read())
+                    emb_path = index_json["weight_map"]["model.embed_tokens.weight"]
+                weights = torch.load(os.path.join(path, emb_path))
+                tensor = weights["model.embed_tokens.weight"].float()
+            self.embed_tokens.weight.data = tensor
+
+        self.top_k = top_k
+        self.total_tokens = total_tokens - 1
+        self.depth = depth
+        self.threshold = math.log(threshold)
+        # print("total_tokens",total_tokens)
+        # print("depth",depth)
+        # print("top_k",top_k)
+        # print("threshold",threshold)
+
+        self.layers = nn.ModuleList([LlamaDecoderLayer(config, index) for index in range(config.num_hidden_layers)])
+        self.fc = nn.Linear(2 * config.hidden_size, config.hidden_size, bias=bias)
+        self.act = ACT2FN[config.hidden_act]
+        self.logsoftmax = nn.LogSoftmax(dim=-1)
+        for param in self.embed_tokens.parameters():
+            param.requires_grad = False
+
+    def init_tree(self):
+        self.tree_mask_init = torch.eye(self.top_k, device=self.embed_tokens.weight.device)[None, None]
+        self.position_ids = torch.zeros(self.top_k, device=self.embed_tokens.weight.device, dtype=torch.long)
+        self.tree_mask_init = self.tree_mask_init.to(self.embed_tokens.weight.device)
+
+    def reset(self):
+        self.tree_mask = None
+
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                # inputs_embeds.dtype,
+                torch.float32,  # [MODIFIED] force to cast to float32
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, torch.float32, 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
+            )
+
+        # [MODIFIED] add tree mask
+        if hasattr(self, "tree_mask") and self.tree_mask is not None:
+            tree_mask = self.tree_mask
+            _, _, tree_shape0, tree_shape1 = tree_mask.shape
+            combined_attention_mask[:, :, -tree_shape0:, -tree_shape1:][
+                tree_mask == 0
+                ] = torch.finfo(torch.float32).min
+
+        return combined_attention_mask
+
+    def forward(
+            self,
+            hidden_states,
+            input_ids,
+            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,
+            std=None
+    ):
+        batch_size, seq_length, _ = hidden_states.shape
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        with torch.no_grad():
+            inputs_embeds = self.embed_tokens(input_ids)
+            # inputs_embeds = inputs_embeds.detach()
+
+        # if std is not None:
+        #     noise = torch.randn(inputs_embeds.size(),device=inputs_embeds.device) * std
+        #     inputs_embeds=inputs_embeds+noise
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+        if position_ids is None:
+            device = hidden_states.device if hidden_states 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 attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past), dtype=torch.bool, device=hidden_states.device
+            )
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask, (batch_size, seq_length), hidden_states, past_key_values_length
+        )
+
+        # if self.gradient_checkpointing and self.training:
+        #    if use_cache:
+        #        use_cache = False
+
+        # hidden_states=self.act(self.fc(torch.cat((inputs_embeds,hidden_states),dim=-1)))
+        inputs_embeds = inputs_embeds.to(hidden_states.dtype)
+        hidden_states = self.fc(torch.cat((inputs_embeds, hidden_states), dim=-1))
+
+        all_hidden_states = () if output_hidden_states 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 use_cache:
+            return hidden_states, next_decoder_cache
+
+        return hidden_states
+
+    def reset_kv(self):
+        self.stable_kv = None
+
+    @torch.no_grad()
+    def topK_genrate(self, hidden_states, input_ids, head, logits_processor):
+
+        input_ids = input_ids.to(hidden_states.device)
+        total_tokens = self.total_tokens
+        depth = self.depth
+        top_k = self.top_k
+
+        sample_token = input_ids[:, -1]
+
+        scores_list = []
+        parents_list = []
+        ss_token = []
+
+        input_ids = input_ids[:, 1:]
+        input_ids = input_ids.to(hidden_states.device)
+
+        len_posi = input_ids.shape[1]
+        self.reset()
+
+        # with Timer("draft many"):
+        if hasattr(self, "stable_kv") and self.stable_kv is not None:
+            kv_len = self.stable_kv[0][0].shape[2]
+            out_hidden, past_key_values = self(hidden_states, input_ids=input_ids[:, kv_len:],
+                                               past_key_values=self.stable_kv, use_cache=True)
+        else:
+            out_hidden, past_key_values = self(hidden_states, input_ids=input_ids, use_cache=True)
+        self.stable_kv = past_key_values
+        last_hidden = out_hidden[:, -1]
+
+        last_headout = head(last_hidden)
+
+        last_p = self.logsoftmax(last_headout)
+        top = torch.topk(last_p, top_k, dim=-1)
+        topk_index, topk_p = top.indices, top.values
+        scores = topk_p[0]
+        scores_list.append(scores[None])
+        parents_list.append(torch.zeros(1, dtype=torch.long, device=scores.device))
+        ss_token.append(topk_index)
+        input_ids = topk_index
+        input_hidden = last_hidden[None].repeat(1, top_k, 1)
+        tree_mask = self.tree_mask_init
+        topk_cs_index = torch.arange(top_k, device=self.embed_tokens.weight.device)
+
+        # 4
+        for i in range(depth):
+            self.tree_mask = tree_mask
+            position_ids = len_posi + self.position_ids
+            # with Timer("draft one"):
+            out_hidden, past_key_values = self(input_hidden, input_ids=input_ids, past_key_values=past_key_values,
+                                               position_ids=position_ids, use_cache=True)
+            len_posi += 1
+
+            # with Timer("sort1"):
+            bias1 = top_k if i > 0 else 0
+            bias2 = max(0, i - 1)
+            bias = 1 + top_k ** 2 * bias2 + bias1
+            parents = (topk_cs_index + bias)
+            parents_list.append(parents)
+
+            last_headout = head(out_hidden[0])
+            last_p = self.logsoftmax(last_headout)
+
+            top = torch.topk(last_p, top_k, dim=-1)
+            topk_index, topk_p = top.indices, top.values
+
+            cu_scores = topk_p + scores[:, None]
+
+            topk_cs = torch.topk(cu_scores.view(-1), top_k, dim=-1)
+            topk_cs_index, topk_cs_p = topk_cs.indices, topk_cs.values
+            scores = topk_cs_p
+
+            out_ids = topk_cs_index // top_k
+            input_hidden = out_hidden[:, out_ids]
+            # with Timer("2index"):
+            #     in_ids = topk_cs_index % top_k
+            #     input_ids = topk_index[out_ids, in_ids][None]
+            # with Timer("1index"):
+            input_ids = topk_index.view(-1)[topk_cs_index][None]
+            # print(input_ids.equal(input_ids0))
+
+            ss_token.append(topk_index)
+            scores_list.append(cu_scores)
+            tree_mask = torch.cat((tree_mask[:, :, out_ids], self.tree_mask_init), dim=3)
+
+            # if self.threshold < 0 and cu_scores.max() < self.threshold:
+            #     break
+
+        # del parents_list,scores_list,ss_token
+        # return draft_tokens, mask_index,tree_mask,tree_position_ids
+
+        # with Timer("post"):
+
+        scores_list = torch.cat(scores_list, dim=0).view(-1)
+        ss_token_list = torch.cat(ss_token, dim=0).view(-1)
+        top_scores = torch.topk(scores_list, total_tokens, dim=-1)
+        top_scores_index = top_scores.indices
+        top_scores_index = torch.sort(top_scores_index).values
+
+        draft_tokens = ss_token_list[top_scores_index]
+        draft_tokens = torch.cat((sample_token, draft_tokens), dim=0)
+
+        draft_parents = torch.cat(parents_list, dim=0)[top_scores_index // top_k].long()
+        mask_index = torch.searchsorted(top_scores_index, draft_parents - 1, right=False)
+        # mask_index[(top_scores_index[mask_index]!=draft_parents - 1)]=-1
+        mask_index[draft_parents == 0] = -1
+        mask_index = mask_index + 1
+        mask_index_list = mask_index.tolist()
+        # with Timer("mask"):
+        tree_mask = torch.eye(total_tokens + 1).bool()
+        tree_mask[:, 0] = True
+        for i in range(total_tokens):
+            tree_mask[i + 1].add_(tree_mask[mask_index_list[i]])
+
+        # with Timer("mask1"):
+        #     tree_mask0 = [[False for _ in range(total_tokens + 1)] for _ in range(total_tokens + 1)]
+        #     tree_mask0[0][0] = True
+        #     for i in range(total_tokens):
+        #         #tree_mask0[i + 1][0]=True
+        #         tree_mask0[i + 1][i + 1] = True
+        #         p=mask_index_list[i]
+        #         tree_mask0[i + 1][p] = True
+        #         while p:
+        #             p=mask_index_list[p-1]
+        #             tree_mask0[i + 1][p] = True
+        #     tree_mask0 = torch.tensor(tree_mask0, dtype=torch.bool)
+        #
+        # print(tree_mask0.equal(tree_mask))
+        tree_position_ids = torch.sum(tree_mask, dim=1) - 1
+
+        tree_mask = tree_mask.float()[None, None]
+        draft_tokens = draft_tokens[None]
+
+        del parents_list, scores_list, ss_token, ss_token_list, draft_parents
+
+        # with Timer("retrieve"):
+
+        max_depth = torch.max(tree_position_ids) + 1
+        noleaf_index = torch.unique(mask_index).tolist()
+        noleaf_num = len(noleaf_index) - 1
+        leaf_num = total_tokens - noleaf_num
+
+        retrieve_indices = torch.zeros(leaf_num, max_depth.item(), dtype=torch.long) - 1
+        retrieve_indices = retrieve_indices.tolist()
+
+        rid = 0
+        position_ids_list = tree_position_ids.tolist()
+
+        for i in range(total_tokens + 1):
+            if i not in noleaf_index:
+                cid = i
+                depth = position_ids_list[i]
+                for j in reversed(range(depth + 1)):
+                    retrieve_indices[rid][j] = cid
+                    cid = mask_index_list[cid - 1]
+                rid += 1
+
+        if logits_processor is not None:
+            maxitem = total_tokens + 5
+
+            def custom_sort(lst):
+                # sort_keys=[len(list)]
+                sort_keys = []
+                for i in range(len(lst)):
+                    sort_keys.append(lst[i] if lst[i] >= 0 else maxitem)
+                return sort_keys
+
+            retrieve_indices = sorted(retrieve_indices, key=custom_sort)
+
+        retrieve_indices = torch.tensor(retrieve_indices, dtype=torch.long)
+        del mask_index, mask_index_list, noleaf_index, noleaf_num, leaf_num, max_depth, rid
+        tree_position_ids = tree_position_ids.to(hidden_states.device)
+
+        return draft_tokens, retrieve_indices, tree_mask, tree_position_ids
+
+    @torch.no_grad()
+    def acc(self, data, head, max_length=5):
+        hidden_states = data["hidden_states"]
+        input_ids = data["input_ids"]
+        # attention_mask=data["attention_mask"]
+        loss_mask = data["loss_mask"]
+        sample_mask = data["sample_mask"]
+        target = data["target"]
+        total = [0 for _ in range(max_length)]
+        correct = [0 for _ in range(max_length)]
+        bs, sl = hidden_states.shape[0], hidden_states.shape[1]
+        target_headout = head(target)
+        hidden_states_headout = head(hidden_states)
+
+        for i in range(bs):
+            for j in range(sl):
+                if loss_mask[i, j] == 0:
+                    continue
+                single_hidden_states = hidden_states[i, :j]
+                single_input_ids = input_ids[i, :j]
+
+                single_hidden_states = single_hidden_states[None, :, :]
+                single_input_ids = single_input_ids[None, :]
+                for k in range(max_length):
+                    tmp_in_target_headout = hidden_states_headout[i, single_hidden_states.shape[1] - 1]
+                    tmp_out_target_headout = target_headout[i, single_hidden_states.shape[1] - 1]
+                    target_in_token = torch.argmax(tmp_in_target_headout)
+                    target_out_token = torch.argmax(tmp_out_target_headout)
+                    tmp_token = input_ids[i, single_hidden_states.shape[1] - 1]
+                    tmp_sample_mask = sample_mask[i, single_hidden_states.shape[1] - 1]
+                    if not (target_in_token == tmp_token):
+                        break
+                    out_hidden = self(single_hidden_states, input_ids=single_input_ids)
+                    last_hidden = out_hidden[:, -1]
+                    last_headout = head(last_hidden)
+                    token = torch.argmax(last_headout)
+                    total[k] += 1
+                    if token == target_out_token:
+                        correct[k] += 1
+                    else:
+                        for kk in range(k, max_length):
+                            total[kk] += 1
+                        break
+
+                    single_hidden_states = torch.cat((single_hidden_states, out_hidden[:, -1:]), dim=1)
+                    single_input_ids = torch.cat(
+                        (single_input_ids, torch.tensor([[token]]).to(single_input_ids.device)), dim=1)
+
+        acc = [correct[i] / total[i] for i in range(len(correct))]
+        return acc
+
+
+class Vhead(nn.Module):
+    def __init__(self, ins=6566, outs=32000):
+        super().__init__()
+        self.fc = nn.Linear(ins, outs, bias=False)
+
+    def forward(self, x):
+        return self.fc(x)
+
+
+import torch
+
+
+def count_parameters(model):
+    return sum(p.numel() for p in model.parameters())
+
+
+if __name__ == "__main__":
+    config = EConfig.from_pretrained('config.json')
+    model = Model(config, load_emb=True, path="/home/lyh/weights/hf/vicuna_v13/7B/")
+    print(model)

model/configs.py

@@ -0,0 +1,145 @@
+from transformers.configuration_utils import PretrainedConfig
+class EConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`LlamaModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        pretraining_tp (`int`, *optional*, defaults to `1`):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports 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/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+
+        Example:
+
+    ```python
+    >>> from transformers import LlamaModel, LlamaConfig
+
+    >>> # Initializing a LLaMA llama-7b style configuration
+    >>> configuration = LlamaConfig()
+
+    >>> # Initializing a model from the llama-7b style configuration
+    >>> model = LlamaModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "llama"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_scaling=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s name 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 an float > 1, got {rope_scaling_factor}")

model/ea_model.py

@@ -0,0 +1,562 @@
+import copy
+import json
+import time
+
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, PretrainedConfig,AutoConfig
+from .modeling_llama_kv import LlamaForCausalLM as KVLlamaForCausalLM
+from .modeling_mixtral_kv import MixtralForCausalLM as KVMixtralForCausalLM
+from .utils import *
+from .kv_cache import initialize_past_key_values
+from .choices import mc_sim_7b_63
+from transformers import AutoTokenizer
+import os
+from huggingface_hub import hf_hub_download
+from .cnets import Model
+from .configs import EConfig
+from huggingface_hub import hf_hub_download
+
+
+
+
+class EaModel(nn.Module):
+
+    def __init__(
+            self,
+            base_model,
+            base_model_name_or_path,
+            ea_model_path,
+            total_token,
+            depth,
+            top_k,
+            threshold,
+            ea_layer_state_dict
+    ):
+
+        super().__init__()
+        self.base_model = base_model
+        self.config = base_model.config
+        self.hidden_size = base_model.lm_head.weight.shape[-1]
+        self.vocab_size = base_model.lm_head.weight.shape[0]
+        self.base_model_name_or_path = base_model_name_or_path
+        self.tokenizer = AutoTokenizer.from_pretrained(self.base_model_name_or_path,use_fast=False)
+        config = EConfig.from_pretrained(ea_model_path)
+        with open(ea_model_path,"r") as f:
+            con=json.loads(f.read())
+        try:
+            bias=con["bias"]
+        except:
+            bias=True
+        self.ea_layer = Model(config,bias=bias,total_tokens=total_token,depth=depth,top_k=top_k,threshold=threshold)
+
+        low_memory=False
+
+        device = base_model.model.layers[-1].self_attn.q_proj.weight.device
+        if device!=base_model.lm_head.weight.device:
+            self.ea_layer.diff_device = True
+            if not low_memory:
+                # self.ea_layer.head=nn.Linear(base_model.lm_head.in_features,base_model.lm_head.out_features,bias=False)
+                # self.ea_layer.head.weight=copy.deepcopy(base_model.lm_head.weight)
+                # self.ea_layer.head.to(device)
+                self.ea_layer.headweight = base_model.lm_head.weight.clone().to(device)
+            else:
+                self.ea_layer.layer_device = device
+
+        else:
+            self.ea_layer.diff_device = False
+        self.ea_layer.load_state_dict(ea_layer_state_dict, strict=True)
+        self.ea_layer.to(self.base_model.dtype).to(device)
+        self.ea_layer.init_tree()
+
+    def get_tokenizer(self):
+        """Get the tokenizer of the base model.
+
+        Returns:
+            Tokenizer: The tokenizer of the base model.
+        """
+        return self.tokenizer
+
+    @classmethod
+    def from_pretrained(
+            cls,
+            Type="LLaMA",
+            base_model_path=None,
+            ea_model_path=None,
+            total_token=59,
+            depth=5,
+            top_k=10,
+            threshold=1.0,
+            **kwargs,
+    ):
+        #assert Type=="LLaMA" or "Mixtral"
+        Type=AutoConfig.from_pretrained(base_model_path).architectures[0]
+        if Type=='LlamaForCausalLM':
+            base_model = KVLlamaForCausalLM.from_pretrained(
+                base_model_path, **kwargs
+            )
+        else:
+            base_model = KVMixtralForCausalLM.from_pretrained(
+                base_model_path, **kwargs
+            )
+
+        configpath=os.path.join(ea_model_path,"config.json")
+        if not os.path.exists(configpath):
+            configpath = hf_hub_download(ea_model_path, "config.json")
+        load_model_path=os.path.join(ea_model_path, "pytorch_model.bin")
+        if not os.path.exists(load_model_path):
+            load_model_path=hf_hub_download(ea_model_path, "pytorch_model.bin")
+        ea_layer_state_dict = torch.load(load_model_path,
+                                         map_location="cpu")
+        model = cls(
+            base_model,
+            base_model_path,
+            configpath,
+            total_token,
+            depth,
+            top_k,
+            threshold,
+            ea_layer_state_dict
+        )
+
+
+
+        if total_token==-1:
+            device = model.base_model.model.layers[0].self_attn.q_proj.weight.device
+            cans=[40,48,50,56,60]
+            x=[1,1.05,1.07,1.1,1.13]
+            times=[]
+
+            for i in range(len(cans)):
+                length = cans[i]
+                input_ids = torch.randint(0, model.config.vocab_size - 200, (1, length)).to(device)
+                torch.cuda.synchronize()
+                start_time = time.time()
+                for _ in range(20):
+                    torch.cuda.synchronize()
+                    with torch.no_grad():
+                        outputs = model.base_model(input_ids)
+                    torch.cuda.synchronize()
+                torch.cuda.synchronize()
+                end_time = time.time()
+                times.append((end_time - start_time) / x[i])
+            total_token=cans[times.index(min(times))]
+            model.ea_layer.total_tokens=total_token-1
+
+
+
+
+        return model
+
+    def forward(
+            self,
+            input_ids=None,
+            attention_mask=None,
+            past_key_values=None,
+            output_orig=False,
+            position_ids=None,
+    ):
+
+        with torch.inference_mode():
+            # Pass input through the base model
+            outputs = self.base_model.model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                past_key_values=past_key_values,
+                position_ids=position_ids,
+            )
+            if output_orig:
+                orig = self.base_model.lm_head(outputs[0])
+            hidden_states = outputs[0]
+        # if init:
+        #     if logits_processor is not None:
+        #         logits = orig[:, -1]
+        #         logits = logits_processor(None, logits)
+        #         probabilities = torch.nn.functional.softmax(logits, dim=1)
+        #         token = torch.multinomial(probabilities, 1)
+        #     else:
+        #         token = torch.argmax(orig[:, -1])
+        #         token = token[None, None]
+        #     input_ids = torch.cat((input_ids, token.to(input_ids.device)), dim=1)
+        #     # Clone the output hidden states
+        #
+        #     draft_tokens, retrieve_indices,tree_mask,tree_position_ids = self.ea_layer.topK_genrate(hidden_states, input_ids, self.base_model.lm_head)
+        #     if output_orig:
+        #         return draft_tokens, retrieve_indices,tree_mask,tree_position_ids, outputs, orig, hidden_states, token
+        #     return draft_tokens, retrieve_indices,tree_mask,tree_position_ids, hidden_states, token
+        # else:
+        if output_orig:
+            return outputs, orig, hidden_states
+        else:
+            return outputs, hidden_states
+
+    @torch.no_grad()
+    def eagenerate(
+            self,
+            input_ids,
+            temperature=0.0,
+            top_p=0.0,
+            top_k=0.0,
+            max_new_tokens=512,
+            max_length=2048,
+            log=False,
+            is_llama3=False,
+
+    ):
+        if is_llama3:
+            stop_token_id = self.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+        max_length=max_length-self.ea_layer.total_tokens-10
+
+        if temperature > 1e-5:
+            logits_processor = prepare_logits_processor(temperature=temperature, top_p=top_p, top_k=top_k)
+        else:
+            logits_processor = None
+        #assert input_ids.shape[0] == 1, "Only support batch size 1 for now!!"
+        # Avoid modifying the input_ids in-place
+
+        padding=(torch.zeros(1,1,dtype=torch.long)-1).to(input_ids.device)
+        input_ids = input_ids.clone()
+        self.ea_layer.reset_kv()
+
+
+
+        # Initialize the past key and value states
+        if hasattr(self, "past_key_values"):
+            past_key_values = self.past_key_values
+            past_key_values_data = self.past_key_values_data
+            current_length_data = self.current_length_data
+            # Reset the past key and value states
+            current_length_data.zero_()
+        else:
+            (
+                past_key_values,
+                past_key_values_data,
+                current_length_data,
+            ) = initialize_past_key_values(self.base_model)
+            self.past_key_values = past_key_values
+            self.past_key_values_data = past_key_values_data
+            self.current_length_data = current_length_data
+
+        input_len = input_ids.shape[1]
+        reset_tree_mode(self)
+        draft_tokens, retrieve_indices,tree_mask,tree_position_ids, logits, hidden_state, sample_token = initialize_tree(
+            input_ids, self, past_key_values, logits_processor
+        )
+        new_token = 0
+
+        for idx in range(max_length):
+            #with Timer("all"):
+            self.base_model.model.tree_mask = tree_mask
+
+            draft_tokens=draft_tokens.to(input_ids.device)
+            #with Timer("tree_decoding"):
+            logits, hidden_state_new, outputs = tree_decoding(
+                self,
+                draft_tokens,
+                past_key_values,
+                tree_position_ids,
+                input_ids,
+                retrieve_indices,
+            )
+            #retrieve_indices=tree_buffers["retrieve_indices"]
+            #logits = logits[0, retrieve_indices]
+            draft_tokens=torch.cat((draft_tokens,padding),dim=1)
+            candidates=draft_tokens[0,retrieve_indices]
+            best_candidate, accept_length, sample_p = evaluate_posterior(
+                logits, candidates, logits_processor
+            )
+            # print(accept_length)
+            #with Timer("update_inference_inputs"):
+            input_ids, draft_tokens, retrieve_indices,tree_mask,tree_position_ids, new_token, hidden_state, sample_token = update_inference_inputs(
+                input_ids,
+                candidates,
+                best_candidate,
+                accept_length,
+                retrieve_indices,
+                logits_processor,
+                new_token,
+                past_key_values_data,
+                current_length_data,
+                self,
+                hidden_state_new,
+                sample_p
+            )
+
+            if is_llama3:
+                if stop_token_id in input_ids[0, input_len:].tolist():
+                    break
+
+            if self.tokenizer.eos_token_id in input_ids[0, input_len:].tolist():
+                break
+            if new_token > max_new_tokens:
+                break
+            if input_ids.shape[1] > max_length:
+                break
+        if not log:
+            return input_ids
+        else:
+            return input_ids, new_token, idx
+
+
+    @torch.no_grad()
+    def naivegenerate(
+            self,
+            input_ids,
+            temperature=0.0,
+            top_p=0.0,
+            top_k=0.0,
+            max_new_tokens=512,
+            max_length=2048,
+            log=False,
+            is_llama3=False,
+
+    ):
+        if is_llama3:
+            stop_token_id = self.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+        max_length = max_length - self.ea_layer.total_tokens - 10
+
+        if temperature > 1e-5:
+            logits_processor = prepare_logits_processor(temperature=temperature, top_p=top_p, top_k=top_k)
+        else:
+            logits_processor = None
+        # assert input_ids.shape[0] == 1, "Only support batch size 1 for now!!"
+        # Avoid modifying the input_ids in-place
+
+        padding = (torch.zeros(1, 1, dtype=torch.long) - 1).to(input_ids.device)
+        input_ids = input_ids.clone()
+        self.ea_layer.reset_kv()
+
+
+
+        # Initialize the past key and value states
+        if hasattr(self, "past_key_values"):
+            past_key_values = self.past_key_values
+            past_key_values_data = self.past_key_values_data
+            current_length_data = self.current_length_data
+            # Reset the past key and value states
+            current_length_data.zero_()
+        else:
+            (
+                past_key_values,
+                past_key_values_data,
+                current_length_data,
+            ) = initialize_past_key_values(self.base_model)
+            self.past_key_values = past_key_values
+            self.past_key_values_data = past_key_values_data
+            self.current_length_data = current_length_data
+
+        input_len = input_ids.shape[1]
+        reset_tree_mode(self)
+        outputs = self.base_model(input_ids, past_key_values=past_key_values, use_cache=True)
+        new_token = 0
+
+        for idx in range(max_length):
+            if logits_processor is not None:
+                logits = outputs.logits[:, -1]
+                logits = logits_processor(None, logits)
+                probabilities = torch.nn.functional.softmax(logits, dim=-1)
+                input_id = torch.multinomial(probabilities, 1)
+            else:
+                input_id = outputs.logits[:, -1:].argmax(dim=-1)
+            outputs = self.base_model(input_id, use_cache=True, past_key_values=past_key_values)
+            input_ids = torch.cat([input_ids, input_id], dim=-1)
+            new_token+=1
+
+            if is_llama3:
+                if stop_token_id in input_ids[0, input_len:].tolist():
+                    break
+
+            if self.tokenizer.eos_token_id in input_ids[0, input_len:].tolist():
+                break
+            if new_token > max_new_tokens:
+                break
+            if input_ids.shape[1] > max_length:
+                break
+        if not log:
+            return input_ids
+        else:
+            return input_ids, new_token, idx
+
+    @torch.no_grad()
+    def ea_generate(
+            self,
+            input_ids,
+            temperature=0.0,
+            top_p=0.0,
+            top_k=0.0,
+            max_new_tokens=512,
+            max_length=2048,
+            log=False,
+            is_llama3=False,
+
+    ):
+        if is_llama3:
+            stop_token_id = self.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+        max_length=max_length-self.ea_layer.total_tokens-10
+
+        if temperature > 1e-5:
+            logits_processor = prepare_logits_processor(temperature=temperature, top_p=top_p, top_k=top_k)
+        else:
+            logits_processor = None
+        #assert input_ids.shape[0] == 1, "Only support batch size 1 for now!!"
+        # Avoid modifying the input_ids in-place
+
+        padding=(torch.zeros(1,1,dtype=torch.long)-1).to(input_ids.device)
+        input_ids = input_ids.clone()
+        self.ea_layer.reset_kv()
+
+
+
+        # Initialize the past key and value states
+        if hasattr(self, "past_key_values"):
+            past_key_values = self.past_key_values
+            past_key_values_data = self.past_key_values_data
+            current_length_data = self.current_length_data
+            # Reset the past key and value states
+            current_length_data.zero_()
+        else:
+            (
+                past_key_values,
+                past_key_values_data,
+                current_length_data,
+            ) = initialize_past_key_values(self.base_model)
+            self.past_key_values = past_key_values
+            self.past_key_values_data = past_key_values_data
+            self.current_length_data = current_length_data
+
+        input_len = input_ids.shape[1]
+        reset_tree_mode(self)
+        draft_tokens, retrieve_indices,tree_mask,tree_position_ids, logits, hidden_state, sample_token = initialize_tree(
+            input_ids, self, past_key_values, logits_processor
+        )
+        new_token = 0
+
+        for idx in range(max_length):
+            #with Timer("all"):
+            self.base_model.model.tree_mask = tree_mask
+
+            draft_tokens=draft_tokens.to(input_ids.device)
+            #with Timer("tree_decoding"):
+            logits, hidden_state_new, outputs = tree_decoding(
+                self,
+                draft_tokens,
+                past_key_values,
+                tree_position_ids,
+                input_ids,
+                retrieve_indices,
+            )
+            #retrieve_indices=tree_buffers["retrieve_indices"]
+            #logits = logits[0, retrieve_indices]
+            draft_tokens=torch.cat((draft_tokens,padding),dim=1)
+            candidates=draft_tokens[0,retrieve_indices]
+            best_candidate, accept_length, sample_p = evaluate_posterior(
+                logits, candidates, logits_processor
+            )
+            # print(accept_length)
+            #with Timer("update_inference_inputs"):
+            input_ids, draft_tokens, retrieve_indices,tree_mask,tree_position_ids, new_token, hidden_state, sample_token = update_inference_inputs(
+                input_ids,
+                candidates,
+                best_candidate,
+                accept_length,
+                retrieve_indices,
+                logits_processor,
+                new_token,
+                past_key_values_data,
+                current_length_data,
+                self,
+                hidden_state_new,
+                sample_p
+            )
+
+            yield input_ids
+
+            if is_llama3:
+                if stop_token_id in input_ids[0, input_len:].tolist():
+                    break
+
+            if self.tokenizer.eos_token_id in input_ids[0, input_len:].tolist():
+                break
+            if new_token > max_new_tokens:
+                break
+            if input_ids.shape[1] > max_length:
+                break
+
+
+    @torch.no_grad()
+    def naive_generate(
+            self,
+            input_ids,
+            temperature=0.0,
+            top_p=0.0,
+            top_k=0.0,
+            max_new_tokens=512,
+            max_length=2048,
+            log=False,
+            is_llama3=False,
+
+    ):
+        if is_llama3:
+            stop_token_id = self.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+        max_length = max_length - self.ea_layer.total_tokens - 10
+
+        if temperature > 1e-5:
+            logits_processor = prepare_logits_processor(temperature=temperature, top_p=top_p, top_k=top_k)
+        else:
+            logits_processor = None
+        # assert input_ids.shape[0] == 1, "Only support batch size 1 for now!!"
+        # Avoid modifying the input_ids in-place
+
+        padding = (torch.zeros(1, 1, dtype=torch.long) - 1).to(input_ids.device)
+        input_ids = input_ids.clone()
+        self.ea_layer.reset_kv()
+
+        # Initialize the past key and value states
+        if hasattr(self, "past_key_values"):
+            past_key_values = self.past_key_values
+            past_key_values_data = self.past_key_values_data
+            current_length_data = self.current_length_data
+            # Reset the past key and value states
+            current_length_data.zero_()
+        else:
+            (
+                past_key_values,
+                past_key_values_data,
+                current_length_data,
+            ) = initialize_past_key_values(self.base_model)
+            self.past_key_values = past_key_values
+            self.past_key_values_data = past_key_values_data
+            self.current_length_data = current_length_data
+
+        input_len = input_ids.shape[1]
+        reset_tree_mode(self)
+        outputs = self.base_model(input_ids, past_key_values=past_key_values, use_cache=True)
+        new_token = 0
+
+        for idx in range(max_length):
+            if logits_processor is not None:
+                logits = outputs.logits[:, -1]
+                logits = logits_processor(None, logits)
+                probabilities = torch.nn.functional.softmax(logits, dim=-1)
+                input_id = torch.multinomial(probabilities, 1)
+            else:
+                input_id = outputs.logits[:, -1:].argmax(dim=-1)
+            outputs = self.base_model(input_id, use_cache=True, past_key_values=past_key_values)
+            input_ids = torch.cat([input_ids, input_id], dim=-1)
+            new_token += 1
+
+            yield input_ids
+
+            if is_llama3:
+                if stop_token_id in input_ids[0, input_len:].tolist():
+                    break
+
+            if self.tokenizer.eos_token_id in input_ids[0, input_len:].tolist():
+                break
+            if new_token > max_new_tokens:
+                break
+            if input_ids.shape[1] > max_length:
+                break
+
+
+

model/kv_cache.py

@@ -0,0 +1,157 @@
+import torch
+
+
+class KVCache:
+    """
+    A key-value cache for the model.
+
+    This class provides a mechanism to maintain a growing cache of keys and values,
+    particularly useful for models that benefit from caching previous states,
+    like transformers during autoregressive decoding.
+
+    Attributes:
+        data (torch.Tensor): The tensor storing keys and values.
+        current_length (int): Current length of the data being stored.
+    """
+
+    def __init__(self, data, current_length):
+        """
+        Initialize the KVCache.
+
+        Args:
+            data (torch.Tensor): Initial tensor to store the keys and values.
+            current_length (int): Initial length of the data.
+        """
+        self.data = data
+        self.current_length = current_length
+
+    @property
+    def shape(self):
+        """Return the shape of the data tensor with updated length."""
+        return (
+            self.data.shape[0],
+            self.data.shape[1],
+            self.current_length.item(),
+            self.data.shape[3],
+        )
+
+    def copy(self, indices: torch.Tensor, prev_length: int, dim: int = 2):
+        """
+        Copy values from the current data at specified indices to a new location.
+
+        Args:
+            indices (torch.Tensor): Indices of the data tensor to be copied.
+            prev_length (int): Previous length before adding new data.
+            dim (int, optional): Dimension along which copying should be performed. Default is 2.
+        """
+        tgt = self.data.index_select(dim, indices)
+        dst = self.data.narrow(dim, prev_length, tgt.shape[dim])
+        dst.copy_(tgt, non_blocking=True)
+        self.current_length.fill_(prev_length + tgt.shape[dim])
+
+    def cat(self, tensor: torch.Tensor, dim: int = 2):
+        """
+        Concatenate the given tensor with the current data.
+
+        Args:
+            tensor (torch.Tensor): The tensor to be concatenated.
+            dim (int, optional): The dimension along which concatenation should be done. Default is 2.
+
+        Returns:
+            torch.Tensor: The data tensor after concatenation up to the current length.
+        """
+        dst = self.data.narrow(dim, self.current_length, tensor.shape[dim])
+        dst.copy_(tensor)
+        self.current_length.add_(tensor.shape[dim])
+        return torch.narrow(self.data, 2, 0, self.current_length)
+
+
+def initialize_past_key_values(model):
+    """
+    Initialize past key and value states for a given transformer model.
+
+    This function prepares key-value cache structures for the model, allowing it to store and reuse
+    past key and value states during autoregressive decoding, which can improve efficiency.
+
+    Args:
+        model (nn.Module): The transformer model for which past key-value states need to be initialized.
+
+    Returns:
+        tuple:
+            - past_key_values (list): A list of KVCache objects for each layer in the model.
+            - past_key_values_data (torch.Tensor): The tensor that will store all keys and values.
+            - current_length_data (torch.Tensor): A tensor tracking the current length of keys/values in the cache.
+    """
+    # Extracting configuration from the model
+    config = model.config
+    # Initializing the batch size to 1, this can be modified if different batch sizes are required
+    batch_size = 1
+    # Initializing a tensor to store past keys and values for all layers
+
+    devices=[]
+    for i in range(config.num_hidden_layers):
+        try:
+            device = model.model.layers[i].self_attn.q_proj.weight.device
+        except:
+            device=model.layers[i].self_attn.q_proj.weight.device
+        devices.append(device)
+    past_key_values_data_list=[]
+    startnum=0
+    startdevice=devices[0]
+    for id,i in enumerate(devices):
+        if startdevice!=i:
+            past_key_values_data = torch.zeros(
+                startnum * 2,
+                batch_size,
+                config.num_key_value_heads,
+                config.max_position_embeddings,
+                config.hidden_size // config.num_attention_heads,
+                device=startdevice,
+                dtype=model.dtype,
+            )
+            past_key_values_data_list.append(past_key_values_data)
+            startdevice = i
+            startnum=0
+        startnum += 1
+    past_key_values_data = torch.zeros(
+        startnum * 2,
+        batch_size,
+        config.num_key_value_heads,
+        config.max_position_embeddings,
+        config.hidden_size // config.num_attention_heads,
+        device=startdevice,
+        dtype=model.dtype,
+    )
+    past_key_values_data_list.append(past_key_values_data)
+    # Initialize tensor to store the current length of the cached data for all layers.
+    # [IMPORTANT] It needs to be kept on CPU for quick access and updates.
+    current_length_data = torch.zeros(
+        config.num_hidden_layers * 2, dtype=torch.long, device="cpu"
+    )
+    # Creating a KVCache for each pair of key and value in all layers
+    past_key_values = [] * config.num_hidden_layers
+
+    bias=0
+    start_data_m=devices[0].index
+    for i in range(config.num_hidden_layers):
+        data_m=devices[i].index
+        if data_m!=start_data_m:
+            bias=0
+            start_data_m=data_m
+        try:
+            past_key_values.append(
+                [
+                    KVCache(past_key_values_data_list[data_m-devices[0].index][2*bias + j], current_length_data[i * 2 + j])
+                    for j in range(2)
+                ]
+            )
+        except:
+            past_key_values.append(
+                [
+                    KVCache(past_key_values_data_list[0][2 * bias + j],
+                            current_length_data[i * 2 + j])
+                    for j in range(2)
+                ]
+            )
+        bias+=1
+    return past_key_values, past_key_values_data_list, current_length_data

model/modeling_llama_kv.py

@@ -0,0 +1,1398 @@
+# Source: https://github.com/huggingface/transformers/blob/v4.31-release/src/transformers/models/llama/modeling_llama.py
+# Modifications are denoted by the symbol: [MODIFIED]
+
+
+""" PyTorch LLaMA 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
+
+# [MODIFIED] Import from transformer library
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from transformers import LlamaConfig
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LlamaConfig"
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+        input_ids_shape: torch.Size,
+        dtype: torch.dtype,
+        device: torch.device,
+        past_key_values_length: int = 0,
+):
+    """
+    Create a causal mask for bi-directional self-attention.
+
+    Args:
+        input_ids_shape (torch.Size): The shape of input_ids tensor, typically (batch_size, tgt_len).
+        dtype (torch.dtype): The data type of the mask.
+        device (torch.device): The device on which the mask will be placed.
+        past_key_values_length (int, optional): The length of past key values. Default is 0.
+
+    Returns:
+        torch.Tensor: The causal mask tensor.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat(
+            [
+                torch.zeros(
+                    tgt_len, past_key_values_length, dtype=dtype, device=device
+                ),
+                mask,
+            ],
+            dim=-1,
+        )
+    return mask[None, None, :, :].expand(
+        bsz, 1, tgt_len, tgt_len + past_key_values_length
+    )
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expand attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+
+    Args:
+        mask (torch.Tensor): The attention mask tensor of shape `[bsz, seq_len]`.
+        dtype (torch.dtype): The data type of the mask.
+        tgt_len (Optional[int], optional): The target sequence length. If None, it defaults to the source sequence length.
+
+    Returns:
+        torch.Tensor: The expanded mask tensor.
+    """
+    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
+    )
+
+
+import torch.nn as nn
+import torch
+
+
+class LlamaRMSNorm(nn.Module):
+    """
+    LlamaRMSNorm is equivalent to T5LayerNorm.
+
+    Args:
+        hidden_size (int): The size of the hidden states.
+        eps (float, optional): A small value to prevent division by zero. Default is 1e-6.
+    """
+
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        """
+        Apply LlamaRMSNorm to the input hidden states.
+
+        Args:
+            hidden_states (torch.Tensor): Input hidden states.
+
+        Returns:
+            torch.Tensor: The normalized and scaled hidden states.
+        """
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+class LlamaRotaryEmbedding(nn.Module):
+    """
+    Llama Rotary Positional Embedding Module.
+
+    Args:
+        dim (int): The dimension of the embedding.
+        max_position_embeddings (int, optional): The maximum position for embeddings. Default is 2048.
+        base (int, optional): The base value for rotational encoding. Default is 10000.
+        device (str, optional): The device on which the computation will be performed. Default is None.
+    """
+
+    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)
+
+        # 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):
+        """
+        Set the cosine and sine cache for positional embeddings.
+
+        Args:
+            seq_len (int): The sequence length.
+            device (str): The device on which the cache tensors will be stored.
+            dtype: The data type of the cache tensors.
+        """
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
+        )
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer(
+            "cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False
+        )
+        self.register_buffer(
+            "sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
+        )
+
+    def forward(self, x, seq_len=None):
+        """
+        Forward pass of the LlamaRotaryEmbedding module.
+
+        Args:
+            x (torch.Tensor): Input tensor of shape [bs, num_attention_heads, seq_len, head_size].
+            seq_len (int): The sequence length. If greater than the cached length, the cache will be updated.
+
+        Returns:
+            tuple: A tuple containing two tensors, the cosine and sine embeddings, both of shape [1, 1, seq_len, dim].
+        """
+        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 LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """
+    LlamaRotaryEmbedding extended with linear scaling.
+
+    This class adds linear scaling to LlamaRotaryEmbedding. Credits to the Reddit user /u/kaiokendev.
+
+    Args:
+        dim (int): The dimension of the embedding.
+        max_position_embeddings (int, optional): The maximum number of position embeddings. Default is 2048.
+        base (int, optional): The base value for the rotational embeddings. Default is 10000.
+        device (str or torch.device, optional): The device where the embeddings should be stored. Default is None.
+        scaling_factor (float, optional): The scaling factor for the embeddings. Default is 1.0.
+    """
+
+    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):
+        """
+        Set the cosine and sine cache for the rotary embeddings.
+
+        Args:
+            seq_len (int): The sequence length.
+            device (str or torch.device): The device where the cache should be stored.
+            dtype: The data type for the cache.
+        """
+        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.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer(
+            "cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False
+        )
+        self.register_buffer(
+            "sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
+        )
+
+
+class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """
+    LlamaRotaryEmbedding extended with Dynamic NTK scaling.
+
+    Credits to the Reddit users /u/bloc97 and /u/emozilla.
+    """
+
+    def __init__(
+            self,
+            dim,
+            max_position_embeddings=2048,
+            base=10000,
+            device=None,
+            scaling_factor=1.0,
+    ):
+        """
+        Initialize the LlamaDynamicNTKScalingRotaryEmbedding.
+
+        Args:
+            dim (int): The dimensionality of the embedding.
+            max_position_embeddings (int, optional): Maximum number of position embeddings. Default is 2048.
+            base (int, optional): Base value for scaling calculations. Default is 10000.
+            device: The device to place tensors on. If None, uses the default device.
+            scaling_factor (float, optional): Scaling factor for NTK scaling. Default is 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):
+        """
+        Set the cached values for cosine and sine.
+
+        Args:
+            seq_len (int): The sequence length.
+            device: The device to place tensors on.
+            dtype: The data type of tensors.
+        """
+        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)
+
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
+        )
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        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 rotate_half(x):
+    """
+    Rotates half the hidden dimensions of the input.
+
+    Args:
+        x (torch.Tensor): Input tensor.
+
+    Returns:
+        torch.Tensor: Tensor with half of its hidden dimensions rotated.
+    """
+    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):
+    """
+    Apply rotary position embeddings to query and key tensors.
+
+    Args:
+        q (torch.Tensor): Query tensor.
+        k (torch.Tensor): Key tensor.
+        cos (torch.Tensor): Cosine values.
+        sin (torch.Tensor): Sine values.
+        position_ids (torch.Tensor): Position IDs.
+
+    Returns:
+        torch.Tensor: Query and key tensors with rotary position embeddings applied.
+    """
+    cos = cos.squeeze(1).squeeze(0)
+    sin = sin.squeeze(1).squeeze(0)
+    cos = cos[position_ids].unsqueeze(1)
+    sin = sin[position_ids].unsqueeze(1)
+    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):
+    """
+    LlamaMLP is a multi-layer perceptron module used in the Llama model.
+
+    Args:
+        config: The configuration for the MLP.
+
+    Attributes:
+        pretraining_tp (int): The pretraining time periods.
+        hidden_size (int): The size of the hidden layer.
+        intermediate_size (int): The size of the intermediate layer.
+        gate_proj (nn.Linear): The linear projection for gating.
+        up_proj (nn.Linear): The linear projection for the up projection.
+        down_proj (nn.Linear): The linear projection for the down projection.
+        act_fn: The activation function.
+
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.pretraining_tp = config.pretraining_tp
+        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):
+        """
+        Forward pass of the MLP.
+
+        Args:
+            x: Input tensor.
+
+        Returns:
+            torch.Tensor: Output tensor.
+        """
+        if self.pretraining_tp > 1:
+            slice = self.intermediate_size // self.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.pretraining_tp)],
+                dim=-1,
+            )
+            up_proj = torch.cat(
+                [F.linear(x, up_proj_slices[i]) for i in range(self.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.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:
+    """
+    Repeat key and value tensors n times along the specified dimension.
+
+    Args:
+        hidden_states (torch.Tensor): Input tensor with shape (batch, num_key_value_heads, seqlen, head_dim).
+        n_rep (int): Number of times to repeat.
+
+    Returns:
+        torch.Tensor: Repeated tensor with shape (batch, num_key_value_heads * n_rep, 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):
+    """
+    LlamaAttention is a multi-headed attention module based on the 'Attention Is All You Need' paper.
+
+    Args:
+        config (LlamaConfig): Configuration for the attention module.
+
+    Attributes:
+        config (LlamaConfig): Configuration for the attention module.
+        hidden_size (int): The size of the hidden layer.
+        num_heads (int): The number of attention heads.
+        head_dim (int): The dimension of each attention head.
+        num_key_value_heads (int): The number of key-value attention heads.
+        num_key_value_groups (int): The number of key-value groups.
+        pretraining_tp (int): The pretraining time periods.
+        max_position_embeddings (int): The maximum position embeddings.
+
+    """
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.pretraining_tp = config.pretraining_tp
+        self.max_position_embeddings = config.max_position_embeddings
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(
+            self.hidden_size, self.num_heads * self.head_dim, bias=False
+        )
+        self.k_proj = nn.Linear(
+            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
+        )
+        self.v_proj = nn.Linear(
+            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
+        )
+        self.o_proj = nn.Linear(
+            self.num_heads * self.head_dim, self.hidden_size, bias=False
+        )
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = LlamaRotaryEmbedding(
+                self.head_dim, max_position_embeddings=self.max_position_embeddings,base=self.config.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,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return (
+            tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
+            .transpose(1, 2)
+            .contiguous()
+        )
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Tuple[torch.Tensor]] = None,
+            output_attentions: bool = False,
+            use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        if self.pretraining_tp > 1:
+            key_value_slicing = (
+                                        self.num_key_value_heads * self.head_dim
+                                ) // self.pretraining_tp
+            query_slices = self.q_proj.weight.split(
+                (self.num_heads * self.head_dim) // self.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.pretraining_tp)
+            ]
+            query_states = torch.cat(query_states, dim=-1)
+
+            key_states = [
+                F.linear(hidden_states, key_slices[i])
+                for i in range(self.pretraining_tp)
+            ]
+            key_states = torch.cat(key_states, dim=-1)
+
+            value_states = [
+                F.linear(hidden_states, value_slices[i])
+                for i in range(self.pretraining_tp)
+            ]
+            value_states = torch.cat(value_states, dim=-1)
+
+        else:
+            query_states = self.q_proj(hidden_states)
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin, position_ids
+        )
+
+        # [MODIFIED] Using KVCache mechanism for preallocated GPU memory optimization
+        # past_key_value is utilized to leverage previously computed key and value states.
+        # If past_key_value is available, reuse the states for k, v, and self_attention.
+        if past_key_value is not None:
+            key_states = past_key_value[0].cat(key_states, dim=2)
+            value_states = past_key_value[1].cat(value_states, dim=2)
+        # Reset past_key_value to avoid return past_key_value.
+        past_key_value = 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()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        if self.pretraining_tp > 1:
+            attn_output = attn_output.split(
+                self.hidden_size // self.pretraining_tp, dim=2
+            )
+            o_proj_slices = self.o_proj.weight.split(
+                self.hidden_size // self.pretraining_tp, dim=1
+            )
+            attn_output = sum(
+                [
+                    F.linear(attn_output[i], o_proj_slices[i])
+                    for i in range(self.pretraining_tp)
+                ]
+            )
+        else:
+            attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class LlamaDecoderLayer(nn.Module):
+    """
+    LlamaDecoderLayer represents a single layer of the Llama decoder.
+
+    Args:
+        config (LlamaConfig): Configuration for the decoder layer.
+
+    Attributes:
+        hidden_size (int): The size of the hidden layer.
+        self_attn (LlamaAttention): Multi-headed self-attention module.
+        mlp (LlamaMLP): Multi-layer perceptron module.
+        input_layernorm (LlamaRMSNorm): Layer normalization for input.
+        post_attention_layernorm (LlamaRMSNorm): Layer normalization after self-attention.
+    """
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = LlamaAttention(config=config)
+        self.mlp = LlamaMLP(config)
+        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LlamaRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Tuple[torch.Tensor]] = None,
+            output_attentions: Optional[bool] = False,
+            use_cache: Optional[bool] = False,
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        """
+        Forward pass for the LlamaDecoderLayer.
+
+        Args:
+            hidden_states (torch.FloatTensor): Input tensor 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, optional): Positional IDs tensor.
+            past_key_value (Tuple[torch.FloatTensor], optional): Cached past key and value projection states.
+            output_attentions (bool, optional): Whether or not to return the attentions tensors of all attention layers.
+            use_cache (bool, optional): If set to `True`, `past_key_values` key-value states are returned and can be
+                used to speed up decoding.
+
+        Returns:
+            Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: Tuple containing:
+                - hidden_states (torch.FloatTensor): Output tensor.
+                - self_attn_weights (Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]): Self-attention weights if
+                  `output_attentions` is `True`.
+                - present_key_value (Optional[Tuple[torch.FloatTensor]]): Cached key and value projection states if
+                  `use_cache` is `True`.
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+LLAMA_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`LlamaConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class LlamaPreTrainedModel(PreTrainedModel):
+    config_class = LlamaConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["LlamaDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, LlamaModel):
+            module.gradient_checkpointing = value
+
+
+LLAMA_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class LlamaModel(LlamaPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
+
+    Args:
+        config: LlamaConfig
+    """
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(
+            config.vocab_size, config.hidden_size, self.padding_idx
+        )
+        self.layers = nn.ModuleList(
+            [LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(
+            self, attention_mask, input_shape, inputs_embeds, past_key_values_length
+    ):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                # inputs_embeds.dtype,
+                torch.float32,  # [MODIFIED] force to cast to float32
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(
+                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+            ).to(inputs_embeds.device)
+            combined_attention_mask = (
+                expanded_attn_mask
+                if combined_attention_mask is None
+                else expanded_attn_mask + combined_attention_mask
+            )
+
+
+        if hasattr(self, "tree_mask") and self.tree_mask is not None:
+            tree_mask = self.tree_mask
+            tree_len = tree_mask.size(-1)
+            combined_attention_mask[:, :, -tree_len:, -tree_len:][
+                tree_mask == 0
+                ] = combined_attention_mask.min()
+
+        return combined_attention_mask
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values=None,  # [MODIFIED] past_key_value is KVCache class
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError(
+                "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+            )
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length,
+                seq_length + past_key_values_length,
+                dtype=torch.long,
+                device=device,
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        # embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past),
+                dtype=torch.bool,
+                device=inputs_embeds.device,
+            )
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask,
+            (batch_size, seq_length),
+            inputs_embeds,
+            past_key_values_length,
+        )
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            # if idx==16:
+            #     print(idx)
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = (
+                past_key_values[idx] if past_key_values is not None else None
+            )
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class LlamaForCausalLM(LlamaPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = LlamaModel(config)
+        self.pretraining_tp = config.pretraining_tp
+        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=None,  # [MODIFIED] past_key_value is KVCache class
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, LlamaForCausalLM
+
+        >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        if self.pretraining_tp > 1:
+            lm_head_slices = self.lm_head.weight.split(
+                self.vocab_size // self.pretraining_tp, dim=0
+            )
+            logits = [
+                F.linear(hidden_states, lm_head_slices[i])
+                for i in range(self.pretraining_tp)
+            ]
+            logits = torch.cat(logits, dim=-1)
+        else:
+            logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+            self,
+            input_ids,
+            past_key_values=None,
+            attention_mask=None,
+            inputs_embeds=None,
+            **kwargs,
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(
+                    past_state.index_select(0, beam_idx.to(past_state.device))
+                    for past_state in layer_past
+                ),
+            )
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The LLaMa Model transformer with a sequence classification head on top (linear layer).
+
+    [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    LLAMA_START_DOCSTRING,
+)
+class LlamaForSequenceClassification(LlamaPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = LlamaModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError(
+                "Cannot handle batch sizes > 1 if no padding token is defined."
+            )
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (
+                        torch.ne(input_ids, self.config.pad_token_id).sum(-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,
+        )

model/modeling_mixtral_kv.py

@@ -0,0 +1,1199 @@
+# coding=utf-8
+# Copyright 2023 Mistral AI 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 Mixtral model."""
+import inspect
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+from .kv_cache import KVCache
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+# [MODIFIED] Import from transformer library
+from transformers.activations import ACT2FN
+
+from transformers.modeling_outputs import (
+    MoeCausalLMOutputWithPast,
+    MoeModelOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from transformers import MixtralConfig
+
+
+
+
+# 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.
+
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "MixtralConfig"
+
+
+def _make_causal_mask(
+        input_ids_shape: torch.Size,
+        dtype: torch.dtype,
+        device: torch.device,
+        past_key_values_length: int = 0,
+):
+    """
+    Create a causal mask for bi-directional self-attention.
+
+    Args:
+        input_ids_shape (torch.Size): The shape of input_ids tensor, typically (batch_size, tgt_len).
+        dtype (torch.dtype): The data type of the mask.
+        device (torch.device): The device on which the mask will be placed.
+        past_key_values_length (int, optional): The length of past key values. Default is 0.
+
+    Returns:
+        torch.Tensor: The causal mask tensor.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat(
+            [
+                torch.zeros(
+                    tgt_len, past_key_values_length, dtype=dtype, device=device
+                ),
+                mask,
+            ],
+            dim=-1,
+        )
+    return mask[None, None, :, :].expand(
+        bsz, 1, tgt_len, tgt_len + past_key_values_length
+    )
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expand attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+
+    Args:
+        mask (torch.Tensor): The attention mask tensor of shape `[bsz, seq_len]`.
+        dtype (torch.dtype): The data type of the mask.
+        tgt_len (Optional[int], optional): The target sequence length. If None, it defaults to the source sequence length.
+
+    Returns:
+        torch.Tensor: The expanded mask tensor.
+    """
+    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
+    )
+
+
+def load_balancing_loss_func(gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2) -> float:
+    r"""
+    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+    See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
+    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+    experts is too unbalanced.
+
+    Args:
+        gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
+            Logits from the `gate`, should be a tuple of tensors. Shape: [batch_size, seqeunce_length, num_experts].
+        num_experts (`int`, *optional*):
+            Number of experts
+
+    Returns:
+        The auxiliary loss.
+    """
+    if gate_logits is None:
+        return 0
+
+    if isinstance(gate_logits, tuple):
+        # cat along the layers?
+        compute_device = gate_logits[0].device
+        gate_logits = torch.cat([gate.to(compute_device) for gate in gate_logits], dim=0)
+
+    routing_weights, selected_experts = torch.topk(gate_logits, top_k, dim=-1)
+    routing_weights = routing_weights.softmax(dim=-1)
+
+    # cast the expert indices to int64, otherwise one-hot encoding will fail
+    if selected_experts.dtype != torch.int64:
+        selected_experts = selected_experts.to(torch.int64)
+
+    if len(selected_experts.shape) == 2:
+        selected_experts = selected_experts.unsqueeze(2)
+
+    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+    # For a given token, determine if it was routed to a given expert.
+    expert_mask = torch.max(expert_mask, axis=-2).values
+
+    # cast to float32 otherwise mean will fail
+    expert_mask = expert_mask.to(torch.float32)
+    tokens_per_group_and_expert = torch.mean(expert_mask, axis=-2)
+
+    router_prob_per_group_and_expert = torch.mean(routing_weights, axis=-1)
+    return torch.mean(tokens_per_group_and_expert * router_prob_per_group_and_expert.unsqueeze(-1)) * (num_experts**2)
+
+
+# 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.llama.modeling_llama.LlamaRMSNorm with Llama->Mixtral
+class MixtralRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        MixtralRMSNorm 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.LlamaRotaryEmbedding with Llama->Mixtral
+class MixtralRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        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.llama.modeling_llama.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.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)
+
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralAttention with Mistral->Mixtral
+class MixtralAttention(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: MixtralConfig, 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=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.rotary_emb = MixtralRotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[KVCache]] = 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[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            key_states = past_key_value[0].cat(key_states, dim=2)
+            value_states = past_key_value[1].cat(value_states, dim=2)
+
+        # 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
+
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->Mixtral
+
+
+
+class MixtralBLockSparseTop2MLP(nn.Module):
+    def __init__(self, config: MixtralConfig):
+        super().__init__()
+        self.ffn_dim = config.intermediate_size
+        self.hidden_dim = config.hidden_size
+
+        self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+        self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
+        self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_states, routing_weights):
+        current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
+        current_hidden_states = self.w2(current_hidden_states)
+        return routing_weights * current_hidden_states
+
+
+MISTRAL_ATTENTION_CLASSES = {
+    "eager": MixtralAttention,
+}
+
+
+class MixtralSparseMoeBlock(nn.Module):
+    """
+    This implementation is
+    strictly equivalent to standard MoE with full capacity (no
+    dropped tokens). It's faster since it formulates MoE operations
+    in terms of block-sparse operations to accomodate imbalanced
+    assignments of tokens to experts, whereas standard MoE either
+    (1) drop tokens at the cost of reduced performance or (2) set
+    capacity factor to number of experts and thus waste computation
+    and memory on padding.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_dim = config.hidden_size
+        self.ffn_dim = config.intermediate_size
+        self.num_experts = config.num_local_experts
+        self.top_k = config.num_experts_per_tok
+
+        # gating
+        self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
+
+        self.experts = nn.ModuleList([MixtralBLockSparseTop2MLP(config) for _ in range(self.num_experts)])
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """ """
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        # router_logits: (batch * sequence_length, n_experts)
+        router_logits = self.gate(hidden_states)
+
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+        # we cast back to the input dtype
+        routing_weights = routing_weights.to(hidden_states.dtype)
+
+        final_hidden_states = torch.zeros(
+            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+        )
+
+        # One hot encode the selected experts to create an expert mask
+        # this will be used to easily index which expert is going to be sollicitated
+        expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
+
+        # Loop over all available experts in the model and perform the computation on each expert
+        for expert_idx in range(self.num_experts):
+            expert_layer = self.experts[expert_idx]
+            idx, top_x = torch.where(expert_mask[expert_idx])
+
+            if top_x.shape[0] == 0:
+                continue
+
+            # in torch it is faster to index using lists than torch tensors
+            top_x_list = top_x.tolist()
+            idx_list = idx.tolist()
+
+            # Index the correct hidden states and compute the expert hidden state for
+            # the current expert. We need to make sure to multiply the output hidden
+            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+            current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
+            current_hidden_states = expert_layer(current_state, routing_weights[top_x_list, idx_list, None])
+
+            # However `index_add_` only support torch tensors for indexing so we'll use
+            # the `top_x` tensor here.
+            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+        return final_hidden_states, router_logits
+
+
+class MixtralDecoderLayer(nn.Module):
+    def __init__(self, config: MixtralConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = MISTRAL_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
+
+        self.block_sparse_moe = MixtralSparseMoeBlock(config)
+        self.input_layernorm = MixtralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = MixtralRMSNorm(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[KVCache]] = None,
+        output_attentions: Optional[bool] = False,
+        output_router_logits: 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.
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            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_router_logits (`bool`, *optional*):
+                Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+                should not be returned during inference.
+            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`).
+        """
+
+        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, router_logits = self.block_sparse_moe(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        if output_router_logits:
+            outputs += (router_logits,)
+
+        return outputs
+
+
+MIXTRAL_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 ([`MixtralConfig`]):
+            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 Mixtral Model outputting raw hidden-states without any specific head on top.",
+    MIXTRAL_START_DOCSTRING,
+)
+# Copied from transformers.models.mistral.modeling_mistral.MistralPreTrainedModel with Mistral->Mixtral
+class MixtralPreTrainedModel(PreTrainedModel):
+    config_class = MixtralConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["MixtralDecoderLayer"]
+    _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_()
+
+
+MIXTRAL_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        output_router_logits (`bool`, *optional*):
+            Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+            should not be returned during inference.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare Mixtral Model outputting raw hidden-states without any specific head on top.",
+    MIXTRAL_START_DOCSTRING,
+)
+# Copied from transformers.models.mistral.modeling_mistral.MistralModel with MISTRAL->MIXTRAL,Mistral->Mixtral
+class MixtralModel(MixtralPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MixtralDecoderLayer`]
+
+    Args:
+        config: MixtralConfig
+    """
+
+    def __init__(self, config: MixtralConfig):
+        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(
+            [MixtralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+        self.norm = MixtralRMSNorm(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
+
+    def _prepare_decoder_attention_mask(
+            self, attention_mask, input_shape, inputs_embeds, past_key_values_length
+    ):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                # inputs_embeds.dtype,
+                torch.float32,  # [MODIFIED] force to cast to float32
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(
+                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+            ).to(inputs_embeds.device)
+            combined_attention_mask = (
+                expanded_attn_mask
+                if combined_attention_mask is None
+                else expanded_attn_mask + combined_attention_mask
+            )
+
+
+        if hasattr(self, "tree_mask") and self.tree_mask is not None:
+            tree_mask = self.tree_mask
+            tree_len = tree_mask.size(-1)
+            combined_attention_mask[:, :, -tree_len:, -tree_len:][
+                tree_mask == 0
+                ] = combined_attention_mask.min()
+
+        return combined_attention_mask
+
+    # Ignore copy
+    @add_start_docstrings_to_model_forward(MIXTRAL_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[Tuple[KVCache]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_router_logits: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MoeModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+        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")
+
+        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 past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+
+        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._use_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 Mixtral. Make sure to "
+                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
+                )
+
+        # 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 = self._prepare_decoder_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
+        all_router_logits = () if output_router_logits else None
+        next_decoder_cache = 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:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_value,
+                    output_attentions,
+                    output_router_logits,
+                    use_cache,
+                )
+            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,
+                    output_router_logits=output_router_logits,
+                    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],)
+
+            if output_router_logits:
+                all_router_logits += (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 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, all_router_logits]
+                if v is not None
+            )
+        return MoeModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            router_logits=all_router_logits,
+        )
+
+
+class MixtralForCausalLM(MixtralPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MixtralModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.router_aux_loss_coef = config.router_aux_loss_coef
+        self.num_experts = config.num_local_experts
+        self.num_experts_per_tok = config.num_experts_per_tok
+        # 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(MIXTRAL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    # Ignore copy
+    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,
+        output_router_logits: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
+        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, MixtralForCausalLM
+
+        >>> model = MixtralForCausalLM.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_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+
+        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,
+            output_router_logits=output_router_logits,
+            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)
+
+        aux_loss = None
+        if output_router_logits:
+            aux_loss = load_balancing_loss_func(
+                outputs.router_logits if return_dict else outputs[-1], self.num_experts, self.num_experts_per_tok
+            )
+            if labels is not None:
+                loss += self.router_aux_loss_coef * aux_loss
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            if output_router_logits:
+                output = (aux_loss,) + output
+            return (loss,) + output if loss is not None else output
+
+        return MoeCausalLMOutputWithPast(
+            loss=loss,
+            aux_loss=aux_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            router_logits=outputs.router_logits,
+        )
+
+
+
+
+
+
+@add_start_docstrings(
+    """
+    The Mixtral Model transformer with a sequence classification head on top (linear layer).
+
+    [`MixtralForSequenceClassification`] 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).
+    """,
+    MIXTRAL_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Mixtral, LLAMA->MIXTRAL
+class MixtralForSequenceClassification(MixtralPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = MixtralModel(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(MIXTRAL_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,
+        )

model/utils.py

@@ -0,0 +1,469 @@
+import copy
+import random
+
+# typing 
+from typing import List, Tuple
+import time
+import torch
+
+# TODO
+# from transformers import LlamaTokenizer
+# tokenizer=LlamaTokenizer.from_pretrained("/home/lyh/weights/hf/vicuna_v13/7B/")
+
+TOPK = 10  # topk for sparse tree
+
+from transformers.generation.logits_process import (
+    LogitsProcessorList,
+    RepetitionPenaltyLogitsProcessor,
+    TemperatureLogitsWarper,
+    TopKLogitsWarper,
+    TopPLogitsWarper,
+)
+
+
+class Timer:
+    def __init__(self,name):
+        self.name = name
+    def __enter__(self):
+        torch.cuda.synchronize()
+        self.start = time.perf_counter()
+
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        torch.cuda.synchronize()
+        elapsed = time.perf_counter() - self.start
+        print(f'{self.name} took {elapsed} seconds')
+
+
+def prepare_logits_processor(
+        temperature: float = 0.0,
+        repetition_penalty: float = 0.0,
+        top_p: float = 0.0,
+        top_k: int = 0
+) -> LogitsProcessorList:
+    processor_list = LogitsProcessorList()
+    if temperature > 1e-5:
+        if temperature >= 1e-5 and temperature != 1.0:
+            processor_list.append(TemperatureLogitsWarper(temperature))
+        if repetition_penalty > 1.0:
+            processor_list.append(RepetitionPenaltyLogitsProcessor(repetition_penalty))
+        if 1e-8 <= top_p < 1.0:
+            processor_list.append(TopPLogitsWarper(top_p))
+        if top_k > 0:
+            processor_list.append(TopKLogitsWarper(top_k))
+        return processor_list
+
+
+# test_processor = prepare_logits_processor(
+#         0.0, 0.0, -1, 1
+#     )
+
+
+def pad_path(path: List[int], length: int, pad_value: int = -2) -> List[int]:
+    """
+    Pad the given path list with a specific value up to a specified length.
+
+    Parameters:
+    - path (list): The original list that needs padding.
+    - length (int): The desired length of the padded list.
+    - pad_value (optional, default=-2): The value to use for padding.
+
+    Returns:
+    - list: A new list based on the original path but padded to the desired length.
+
+    Example:
+    >>> pad_path([1,2,3], 5)
+    [1, 2, 3, -2, -2]
+
+    Note:
+    If the given path is already longer than the specified length,
+    then no padding occurs, and the original path is returned.
+    """
+
+    # Calculate the number of padding values needed by subtracting the length
+    # of the path from the desired length.
+    # Append the padding values to the original path and return the new list.
+    return path + [pad_value] * (length - len(path))
+
+
+def generate_tree_buffers(tree_choices, device="cuda"):
+    def custom_sort(lst):
+        # sort_keys=[len(list)]
+        sort_keys = []
+        for i in range(len(lst)):
+            sort_keys.append(lst[i] if lst[i] >= 0 else maxitem)
+        return sort_keys
+    with Timer("sort"):
+
+        sorted_tree_choices = sorted(tree_choices, key=lambda x: (len(x), x))
+        tree_len = len(sorted_tree_choices) + 1
+
+    # Initialize depth_counts to keep track of how many choices have a particular depth
+        depth_counts = []
+        prev_depth = 0
+        for path in sorted_tree_choices:
+            depth = len(path)
+            if depth != prev_depth:
+                depth_counts.append(0)
+            depth_counts[depth - 1] += 1
+            prev_depth = depth
+
+        tree_attn_mask = torch.eye(tree_len, tree_len)
+        tree_attn_mask[:, 0] = 1
+        start = 0
+        for i in range(len(depth_counts)):
+            for j in range(depth_counts[i]):
+                cur_tree_choice = sorted_tree_choices[start + j]
+                # retrieve ancestor position
+                if len(cur_tree_choice) == 1:
+                    continue
+                ancestor_idx = []
+                for c in range(len(cur_tree_choice) - 1):
+                    ancestor_idx.append(sorted_tree_choices.index(cur_tree_choice[:c + 1]) + 1)
+                tree_attn_mask[j + start + 1, ancestor_idx] = 1
+            start += depth_counts[i]
+
+        tree_indices = torch.zeros(tree_len, dtype=torch.long)
+        p_indices = [0 for _ in range(tree_len - 1)]
+        b_indices = [[] for _ in range(tree_len - 1)]
+        tree_indices[0] = 0
+        start = 0
+        bias = 0
+        for i in range(len(depth_counts)):
+            inlayer_bias = 0
+            b = []
+            for j in range(depth_counts[i]):
+                cur_tree_choice = sorted_tree_choices[start + j]
+                cur_parent = cur_tree_choice[:-1]
+                if j != 0:
+                    if cur_parent != parent:
+                        bias += 1
+                        inlayer_bias += 1
+                        parent = cur_parent
+                        b = []
+                else:
+                    parent = cur_parent
+                tree_indices[start + j + 1] = cur_tree_choice[-1] + TOPK * (i + bias) + 1
+                p_indices[start + j] = inlayer_bias
+                if len(b) > 0:
+                    b_indices[start + j] = copy.deepcopy(b)
+                else:
+                    b_indices[start + j] = []
+                b.append(cur_tree_choice[-1] + TOPK * (i + bias) + 1)
+            start += depth_counts[i]
+
+        p_indices = [-1] + p_indices
+        tree_position_ids = torch.zeros(tree_len, dtype=torch.long)
+        start = 0
+        for i in range(len(depth_counts)):
+            tree_position_ids[start + 1: start + depth_counts[i] + 1] = i + 1
+            start += depth_counts[i]
+
+        retrieve_indices_nest = []
+        retrieve_paths = []
+        for i in range(len(sorted_tree_choices)):
+            cur_tree_choice = sorted_tree_choices[-i - 1]
+            retrieve_indice = []
+            if cur_tree_choice in retrieve_paths:
+                continue
+            else:
+                for c in range(len(cur_tree_choice)):
+                    retrieve_indice.append(sorted_tree_choices.index(cur_tree_choice[:c + 1]))
+                    retrieve_paths.append(cur_tree_choice[:c + 1])
+            retrieve_indices_nest.append(retrieve_indice)
+        max_length = max([len(x) for x in retrieve_indices_nest])
+        retrieve_indices = [pad_path(path, max_length) for path in retrieve_indices_nest]
+        retrieve_indices = torch.tensor(retrieve_indices, dtype=torch.long)
+        retrieve_indices = retrieve_indices + 1
+        retrieve_indices = torch.cat([torch.zeros((retrieve_indices.shape[0], 1), dtype=torch.long), retrieve_indices],
+                                     dim=1)
+
+        maxitem = retrieve_indices.max().item() + 5
+
+
+
+        retrieve_indices = retrieve_indices.tolist()
+        retrieve_indices = sorted(retrieve_indices, key=custom_sort)
+        retrieve_indices = torch.tensor(retrieve_indices, dtype=torch.long)
+
+
+
+    # Aggregate the generated buffers into a dictionary
+    tree_buffers = {
+        "tree_attn_mask": tree_attn_mask.unsqueeze(0).unsqueeze(0),
+        "tree_indices": tree_indices,
+        "tree_position_ids": tree_position_ids,
+        "retrieve_indices": retrieve_indices,
+    }
+
+    # Move the tensors in the dictionary to the specified device
+    tree_buffers = {
+        k: v.clone().to(device)
+        if isinstance(v, torch.Tensor)
+        else torch.tensor(v, device=device)
+        for k, v in tree_buffers.items()
+    }
+
+    return tree_buffers
+
+
+def initialize_tree0(input_ids, model, past_key_values, logits_processor):
+    draft_tokens, retrieve_indices,tree_mask,tree_position_ids, outputs, logits, hidden_state, sample_token = model(
+        input_ids, past_key_values=past_key_values, output_orig=True, logits_processor=logits_processor
+    )
+
+    #     if logits_processor is not None:
+    #         logits = orig[:, -1]
+    #         logits = logits_processor(None, logits)
+    #         probabilities = torch.nn.functional.softmax(logits, dim=1)
+    #         token = torch.multinomial(probabilities, 1)
+    #     else:
+    #         token = torch.argmax(orig[:, -1])
+    #         token = token[None, None]
+    #     input_ids = torch.cat((input_ids, token.to(input_ids.device)), dim=1)
+    #     # Clone the output hidden states
+    #
+    #     draft_tokens, retrieve_indices,tree_mask,tree_position_ids = self.ea_layer.topK_genrate(hidden_states, input_ids, self.base_model.lm_head)
+    #     if output_orig:
+    #         return draft_tokens, retrieve_indices,tree_mask,tree_position_ids, outputs, orig, hidden_states, token
+    #     return draft_tokens, retrieve_indices,tree_mask,tree_position_ids, hidden_states, token
+    return draft_tokens, retrieve_indices,tree_mask,tree_position_ids, logits, hidden_state, sample_token
+
+def initialize_tree(input_ids, model, past_key_values, logits_processor):
+    outputs, orig, hidden_states = model(
+        input_ids, past_key_values=past_key_values, output_orig=True
+    )
+
+    if logits_processor is not None:
+        logits = orig[:, -1]
+        logits = logits_processor(None, logits)
+        probabilities = torch.nn.functional.softmax(logits, dim=1)
+        token = torch.multinomial(probabilities, 1)
+    else:
+        token = torch.argmax(orig[:, -1])
+        token = token[None, None]
+    input_ids = torch.cat((input_ids, token.to(input_ids.device)), dim=1)
+    # Clone the output hidden states
+
+    draft_tokens, retrieve_indices,tree_mask,tree_position_ids = model.ea_layer.topK_genrate(hidden_states, input_ids, model.base_model.lm_head,logits_processor)
+    return draft_tokens, retrieve_indices,tree_mask,tree_position_ids, orig, hidden_states, token
+
+
+def reset_tree_mode(
+        model,
+):
+    model.base_model.model.tree_mask = None
+    model.base_model.model.tree_mode = None
+
+
+def reset_past_key_values(passed_key_values: List[torch.Tensor]) -> List[torch.Tensor]:
+    """
+    Resets the current lengths in the passed key-values to zero.
+
+    This function is designed to be used during the evaluation of a baseline model.
+    It iterates through each layer's key-values and sets their current lengths to zero,
+    effectively resetting their state.
+
+    Args:
+    - passed_key_values (list of torch.Tensor): Contains past hidden states and past attention values for each layer.
+
+    Returns:
+    - passed_key_values (list of torch.Tensor): Updated past hidden states and past attention values with reset lengths.
+    """
+    for i in range(len(passed_key_values)):
+        for j in range(2):
+            passed_key_values[i][j].current_length.fill_(0)
+    return passed_key_values
+
+
+def generate_candidates(tree_logits, tree_indices, retrieve_indices, sample_token, logits_processor):
+    sample_token = sample_token.to(tree_indices.device)
+
+    candidates_logit = sample_token[0]
+
+    candidates_tree_logits = tree_logits
+
+    candidates = torch.cat([candidates_logit, candidates_tree_logits.view(-1)], dim=-1)
+
+    tree_candidates = candidates[tree_indices]
+
+    tree_candidates_ext = torch.cat(
+        [tree_candidates, torch.zeros((1), dtype=torch.long, device=tree_candidates.device) - 1], dim=0)
+
+    cart_candidates = tree_candidates_ext[retrieve_indices]
+
+
+    # Unsqueeze the tree candidates for dimension consistency.
+    tree_candidates = tree_candidates.unsqueeze(0)
+    return cart_candidates,  tree_candidates
+
+
+def tree_decoding(
+        model,
+        tree_candidates,
+        past_key_values,
+        tree_position_ids,
+        input_ids,
+        retrieve_indices,
+):
+    position_ids = tree_position_ids + input_ids.shape[1]
+
+    outputs, tree_logits, hidden_state = model(
+        tree_candidates,
+        output_orig=True,
+        past_key_values=past_key_values,
+        position_ids=position_ids,
+    )
+
+
+    logits = tree_logits[0, retrieve_indices]
+    return logits, hidden_state, outputs
+
+
+
+
+
+def evaluate_posterior(
+        logits: torch.Tensor,
+        candidates: torch.Tensor,
+        logits_processor,
+):
+    """
+    Evaluate the posterior probabilities of the candidates based on the provided logits and choose the best candidate.
+
+    Depending on the temperature value, the function either uses greedy decoding or evaluates posterior
+    probabilities to select the best candidate.
+
+    Args:
+    - logits (torch.Tensor): Predicted logits of shape (batch_size, sequence_length, vocab_size).
+    - candidates (torch.Tensor): Candidate token sequences.
+    - temperature (float): Softmax temperature for probability scaling. A value of 0 indicates greedy decoding.
+    - posterior_threshold (float): Threshold for posterior probability.
+    - posterior_alpha (float): Scaling factor for the threshold.
+
+    Returns:
+    - best_candidate (torch.Tensor): Index of the chosen best candidate.
+    - accept_length (int): Length of the accepted candidate sequence.
+    """
+    # Greedy decoding based on temperature value
+    if logits_processor is None:
+        # Find the tokens that match the maximum logits for each position in the sequence
+        posterior_mask = (
+                candidates[:, 1:].to(logits.device) == torch.argmax(logits[:, :-1], dim=-1)
+        ).int()
+        candidates_accept_length = (torch.cumprod(posterior_mask, dim=1)).sum(dim=1)
+        accept_length = candidates_accept_length.max()
+        # Choose the best candidate
+        if accept_length == 0:
+            # Default to the first candidate if none are accepted
+            best_candidate = torch.tensor(0, dtype=torch.long, device=candidates.device)
+        else:
+            best_candidate = torch.argmax(candidates_accept_length).to(torch.long)
+        return best_candidate, accept_length, logits[best_candidate, accept_length]
+
+    else:
+        accept_length = 1
+        accept_cand = candidates[0][:1]
+        best_candidate = 0
+        for i in range(1, candidates.shape[1]):
+            if i != accept_length:
+                break
+            adjustflag = False
+            is_eq = (candidates[:, :accept_length] == accept_cand).all(dim=1)
+            fi = torch.nonzero(is_eq, as_tuple=True)[0][0]
+            gt_logits = logits[fi, i - 1][None]
+            gt_logits = logits_processor(None, gt_logits)[0]
+            gtp = torch.softmax(gt_logits, dim=0)
+            candidates_set = []
+            for j in range(candidates.shape[0]):
+                if is_eq[j]:
+                    x = candidates[j, i]
+                    xi = x.item()
+                    if xi in candidates_set or xi == -1:
+                        continue
+                    candidates_set.append(xi)
+                    r = random.random()
+                    px = gtp[xi]
+                    qx = 1.0
+                    acp = px / qx
+                    if r <= acp:
+                        accept_cand = torch.cat((accept_cand, x[None]), dim=0)
+                        accept_length += 1
+                        best_candidate = j
+                        break
+                    else:
+                        gtp[xi] = 0
+                        gtp = gtp / gtp.sum()
+                        adjustflag = True
+        if adjustflag and accept_length != candidates.shape[1]:
+            sample_p = gtp
+        else:
+            gt_logits = logits[best_candidate, accept_length - 1]
+            sample_p = torch.softmax(gt_logits, dim=0)
+        return torch.tensor(best_candidate), accept_length - 1, sample_p
+
+
+@torch.no_grad()
+def update_inference_inputs(
+        input_ids,
+        candidates,
+        best_candidate,
+        accept_length,
+        retrieve_indices,
+        logits_processor,
+        new_token,
+        past_key_values_data_list,
+        current_length_data,
+        model,
+        hidden_state_new,
+        sample_p
+):
+    prev_input_len = input_ids.shape[1]
+    # Map the best candidate indices to the original indices in the sequence
+    select_indices = (
+            retrieve_indices[best_candidate, : accept_length + 1] + prev_input_len
+    )
+    # Append the tokens from the best candidate to the input sequence
+    input_ids = torch.cat(
+        [input_ids, candidates[None, best_candidate, : accept_length + 1].to(input_ids.device)], dim=-1
+    )
+    # Update the past key values based on the selected tokens
+    # Source tensor that contains relevant past information based on the selected candidate
+    for past_key_values_data in past_key_values_data_list:
+        tgt = past_key_values_data[..., select_indices.to(past_key_values_data.device), :]
+        # Destination tensor where the relevant past information will be stored
+        dst = past_key_values_data[..., prev_input_len: prev_input_len + tgt.shape[-2], :]
+        # Copy relevant past information from the source to the destination
+        dst.copy_(tgt, non_blocking=True)
+
+    # Update the current length tensor (currently only support batch size is 1)
+    current_length_data.fill_(prev_input_len + tgt.shape[-2])
+
+    retrieve_hidden_state_new = hidden_state_new[:, retrieve_indices]
+    accept_hidden_state_new = retrieve_hidden_state_new[:, best_candidate, : accept_length + 1]
+    # token=model.base_model.lm_head(accept_hidden_state_new[:,-1]).argmax()
+    # token=token[None,None]
+    prob = sample_p
+    if logits_processor is not None:
+        token = torch.multinomial(prob, 1)
+        token = token[None]
+    else:
+        token = torch.argmax(prob)
+        token = token[None, None]
+    # hidden_state = torch.cat((hidden_state, accept_hidden_state_new), dim=1)
+    draft_tokens, retrieve_indices,tree_mask,tree_position_ids = model.ea_layer.topK_genrate(accept_hidden_state_new,
+                                              input_ids=torch.cat((input_ids, token.to(input_ids.device)), dim=1),
+                                              head=model.base_model.lm_head,logits_processor=logits_processor)
+
+
+    new_token += accept_length + 1
+
+    return input_ids, draft_tokens, retrieve_indices,tree_mask,tree_position_ids, new_token, None, token
+
+
+if __name__ == "__main__":
+    logits = torch.randn(1, 5)
+    tp = prepare_logits_processor(0.9, 0, 0.9, 0)
+    l = tp(None, logits)
+    if tp is None:
+        print(tp)