Last Updated on 2024-11-13 by Clay
在自推測性解碼（Self-Speculative Decoding）中，由於我們的 draft model 是由 target model 的部份網路擔任，所以找到一個好的『跳層策略』（Layer Skip Strategy）是非常重要的事情 —— 我們不僅要跳得夠多層讓加速真正意義上實現、也需要讓 draft model 的推測解碼程度足夠好且不容易被 target model 驗證時拒絕。
所以今天的實作，就是靠貝氏優化框架 Optuna 來優化我之前的實現的 LayerSkip 模型，決定到底要跳哪幾層。
背景回顧
寫系列文的一個痛點就是我可能永遠都是從中途開始，只能或多或少補充點之前的筆記來增添背景細節。
如果是想要看推測性解碼的細節解釋，可以參考：[論文閱讀] Fast Inference from Transformers via Speculative Decoding 和 推測性解碼（Speculative Decoding）實作筆記（附簡易實驗結果）
如果是想要看貝氏定理的簡單介紹，也可以參考：貝氏定理（Bayes’ Theorem）筆記
而自推測解碼（Self-Speculative Decoding）的 LayerSkip 模型，我的實現放在：Self-Speculative Decoding 實現: 跳層 Transformer 模型實作筆記
當然，也歡迎去我的 GitHub，我會一直把加速推理技巧的實現更新在上面：https://github.com/ccs96307/fast-llm-inference
實作說明
Optuna 是 Python 中的一個知名套件，通常我會使用都是用來做模型訓練的最佳超參數的搜尋。簡單來說，它的背後是靠貝氏優化去自動檢索我們定義的超參數組合，並且也同樣是在我們定義的目標函數上評估到底哪個超參數組合是最佳的。
更白話一點，就是當我們真正的目標是無法求導的，我們中間的處理過程是不明的一個黑盒子，但是我們可以決定最初的輸入並得到最後的結果，我們就可以嘗試透過貝氏優化去檢索出較佳的排列組合 —— 之所以說是較佳，是因為我們終究不能窮舉，以此保證得到最佳。
那 Layer Skip 不能窮舉來判斷哪個才是最好嗎？假設我們有 20 層，每一層又分為 Attention 和 MLP，我們的排列組合數量為 2 ^ 40 = 1,099,511,627,776。
以下我會分成幾個函式去寫：
- calculate_continuous_acceptance(): 計算 target model 對於 draft model 推測的 Tokens 的接受率（要連續接受才算接受）
- drafter_speculative_decoding(): draft model 的解碼函式，需要包含連續多個 probs 的輸出
- target_speculative_decoding(): target model 的解碼函式，需要傳入 draft model probs 去進行驗證
- objective(): Optuna 的搜索位置
當然，我知道原本論文中寫的優化方向是測試速度，但我這邊還沒準備好 GPU 跟測試的資料… 所以我只用了一句話跟一個 2B 的 Gemma 當作測試，並且優化目標定義為接受率，真正的測試結果未來應該會更新在 GitHub 跟本篇文章。
完整實作
import optuna
from typing import Dict, Optional, Tuple
import os
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
import copy
import time
import torch
from torch.nn.utils.rnn import pad_sequence
from transformers import AutoTokenizer, PreTrainedTokenizerBase
from layerskip_modeling.modeling_layerskip_gemma2 import LayerSkipGemma2ForCausalLM
from sampling.sampling import sample_next_token
def calculate_continuous_acceptance(acceptance_mask: torch.BoolTensor) -> int:
continuous_acceptance = 0
for accepted in acceptance_mask.long().squeeze(0):
if accepted == 1:
continuous_acceptance += 1
else:
break
return continuous_acceptance
def drafter_speculative_decode(
draft_model: torch.nn.Module,
draft_tokenizer: PreTrainedTokenizerBase,
inputs: Dict[str, torch.Tensor],
gamma: int = 10,
temperature: float = 1.0,
top_k: Optional[int] = 0, # Default is 0, it means do not select top-k tokens
top_p: Optional[float] = 1.0,
repetition_penalty: Optional[float] = 1.0,
draft_mode: bool = True
) -> Tuple[Dict[str, torch.Tensor], torch.FloatTensor]:
draft_model.set_draft_mode(draft_mode)
draft_probs = []
for idx in range(gamma):
with torch.no_grad():
outputs = draft_model(**inputs)
next_tokens, probs = sample_next_token(
logits=outputs.logits,
prefix_token_ids=inputs["input_ids"],
temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
)
draft_probs.append(probs)
input_ids = torch.cat([inputs["input_ids"], next_tokens[:, -1:]], dim=-1)
attention_mask = torch.cat([inputs["attention_mask"], torch.ones(inputs["attention_mask"].shape[0], 1).to(inputs["input_ids"].device)], dim=-1)
inputs["input_ids"] = input_ids
inputs["attention_mask"] = attention_mask
draft_model.set_draft_mode(True)
return inputs, torch.cat(draft_probs, dim=1)
def target_speculative_decode(
target_model: torch.nn.Module,
target_tokenizer: PreTrainedTokenizerBase,
inputs: Dict[str, torch.Tensor],
draft_probs: torch.FloatTensor,
temperature: float = 1.0,
top_k: Optional[int] = 0, # Default is 0, it means do not select top-k tokens
top_p: Optional[float] = 1.0,
repetition_penalty: Optional[float] = 1.0,
) -> Tuple[Dict[str, torch.Tensor], bool, int]:
target_model.set_draft_mode(False)
with torch.no_grad():
outputs = target_model(**inputs)
next_tokens, target_probs = sample_next_token(
logits=outputs.logits,
prefix_token_ids=inputs["input_ids"],
temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
probs_num=draft_probs.shape[1] + 1,
)
next_token = next_tokens[:, -1:]
# Evaluation
indices = inputs["input_ids"][:, -draft_probs.shape[1]:]
eval_probs = target_probs[:, :-1, :]
expanded_indices = indices.unsqueeze(-1)
selected_draft_probs = torch.gather(draft_probs, dim=-1, index=expanded_indices)
selected_draft_probs = selected_draft_probs.squeeze(-1)
selected_eval_probs = torch.gather(eval_probs, dim=-1, index=expanded_indices)
selected_eval_probs = selected_eval_probs.squeeze(-1)
# Compare draft_prob and eval_prob, and check the reject_mask
mask_to_reject = selected_draft_probs > selected_eval_probs
# Calculate reject probabilty 1 - (eval_prob / draft_prob)
rejection_probs = 1 - (selected_eval_probs / selected_draft_probs)
# Generate random values to determined accept or reject
random_values = torch.rand_like(rejection_probs)
rejection_decisions = random_values < rejection_probs
# Get the final reject masks
rejection_masks = mask_to_reject & rejection_decisions
acceptance_mask = torch.ones_like(selected_draft_probs, dtype=torch.bool)
acceptance_mask[rejection_masks] = False
is_end = False
# Concat `input_ids`
if torch.all(acceptance_mask):
input_ids = torch.cat([inputs["input_ids"], next_token], dim=-1)
attention_mask = torch.cat([inputs["attention_mask"], torch.ones(inputs["attention_mask"].shape[0], 1).to(inputs["input_ids"].device)], dim=-1)
else:
new_input_ids = []
new_attention_mask = []
for batch_idx in range(next_tokens.shape[0]):
gamma = next_tokens.shape[1] - 1
start_idx = inputs["input_ids"].shape[1] - gamma
for pos_idx in range(acceptance_mask[batch_idx].shape[0]):
if (acceptance_mask[batch_idx][pos_idx] and inputs["input_ids"][batch_idx][start_idx+pos_idx].item() == target_tokenizer.eos_token_id) or not acceptance_mask[batch_idx][pos_idx]:
inputs["input_ids"][batch_idx][start_idx+pos_idx] = next_tokens[batch_idx][pos_idx]
new_input_ids.append(inputs["input_ids"][batch_idx][:start_idx+pos_idx+1])
new_attention_mask.append(inputs["attention_mask"][batch_idx][:start_idx+pos_idx+1])
is_end = inputs["input_ids"][batch_idx][start_idx+pos_idx].item() == target_tokenizer.eos_token_id
break
input_ids = pad_sequence(new_input_ids, batch_first=True, padding_value=target_tokenizer.pad_token_id)
attention_mask = pad_sequence(new_attention_mask, batch_first=True, padding_value=0)
inputs["input_ids"] = input_ids
inputs["attention_mask"] = attention_mask
return inputs, is_end, calculate_continuous_acceptance(acceptance_mask)
def objective(trial):
# Define search space, sssume we can skip up to six layers
total_layers = 26
# Determine skip or not for `attn`
skip_attn_layers = []
for i in range(total_layers):
skip = trial.suggest_int(f'skip_attn_layer_{i}', 0, 1)
if skip == 1:
skip_attn_layers.append(i)
# Determine skip or not for `mlp`
skip_mlp_layers = []
for i in range(total_layers):
skip = trial.suggest_int(f'skip_mlp_layer_{i}', 0, 1)
if skip == 1:
skip_mlp_layers.append(i)
# Disable set to 0 both
if len(skip_attn_layers) == 0 and len(skip_mlp_layers) == 0:
raise optuna.TrialPruned()
skip_layer_ids = {
"attn": skip_attn_layers,
"mlp": skip_mlp_layers,
}
# Set the skip strategy
model.set_skip_layer_ids(skip_layer_ids=skip_layer_ids)
messages = [
[
{
"role": "user",
"content": "What is the capital of Taiwan. And why?",
},
],
]
input_text = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(
input_text,
return_tensors="pt",
max_length=512,
truncation=True,
padding=True,
).to(device)
is_end = False
# Record
raw_inputs = copy.deepcopy(inputs)
raw_token_num = raw_inputs["input_ids"].shape[1]
total_draft_tokens = 0
total_accept_tokens = 0
gamma = 5
max_new_tokens = 100
while not is_end:
# Draft model
target_inputs, draft_probs = drafter_speculative_decode(
draft_model=model,
draft_tokenizer=tokenizer,
inputs=inputs,
gamma=gamma,
)
total_draft_tokens += gamma
# Target model
outputs, is_end, accept_tokens = target_speculative_decode(
target_model=model,
target_tokenizer=tokenizer,
inputs=target_inputs,
draft_probs=draft_probs,
)
total_accept_tokens += accept_tokens
inputs = outputs
if inputs["input_ids"].shape[1] - raw_token_num >= max_new_tokens:
break
# Compute acceptance rate
accept_rate = total_accept_tokens / total_draft_tokens
print(f"attn_skip: {skip_attn_layers}, mlp_skip: {skip_mlp_layers}, Accept Rate: {accept_rate}")
# Assume we want to maximize `accept_rate`
return accept_rate
if __name__ == "__main__":
pretrained_model_name_or_path = "../models/google--gemma-2-2b-it/"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)
model = LayerSkipGemma2ForCausalLM.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.bfloat16).to(device)
# Init
skip_layer_ids = {
"attn": [],
"mlp": [],
}
model.set_skip_layer_ids(skip_layer_ids=skip_layer_ids)
# Create
study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=50)
print("The best params:", study.best_params)
print("The best accept_rate:", study.best_value)
Output:
[I 2024-11-13 13:26:42,633] Trial 49 finished with value: 0.04390243902439024 and parameters: {'skip_attn_layer_0': 1, 'skip_attn_layer_1': 0, 'skip_attn_layer_2': 1, 'skip_attn_layer_3': 1, 'skip_attn_layer_4': 0, 'skip_attn_layer_5': 0, 'skip_attn_layer_6': 1, 'skip_attn_layer_7': 0, 'skip_attn_layer_8': 1, 'skip_attn_layer_9': 1, 'skip_attn_layer_10': 0, 'skip_attn_layer_11': 0, 'skip_attn_layer_12': 0, 'skip_attn_layer_13': 0, 'skip_attn_layer_14': 1, 'skip_attn_layer_15': 1, 'skip_attn_layer_16': 1, 'skip_attn_layer_17': 0, 'skip_attn_layer_18': 0, 'skip_attn_layer_19': 1, 'skip_attn_layer_20': 1, 'skip_attn_layer_21': 0, 'skip_attn_layer_22': 0, 'skip_attn_layer_23': 0, 'skip_attn_layer_24': 0, 'skip_attn_layer_25': 1, 'skip_mlp_layer_0': 0, 'skip_mlp_layer_1': 0, 'skip_mlp_layer_2': 0, 'skip_mlp_layer_3': 0, 'skip_mlp_layer_4': 1, 'skip_mlp_layer_5': 0, 'skip_mlp_layer_6': 0, 'skip_mlp_layer_7': 1, 'skip_mlp_layer_8': 1, 'skip_mlp_layer_9': 1, 'skip_mlp_layer_10': 1, 'skip_mlp_layer_11': 0, 'skip_mlp_layer_12': 0, 'skip_mlp_layer_13': 0, 'skip_mlp_layer_14': 1, 'skip_mlp_layer_15': 0, 'skip_mlp_layer_16': 1, 'skip_mlp_layer_17': 0, 'skip_mlp_layer_18': 0, 'skip_mlp_layer_19': 1, 'skip_mlp_layer_20': 1, 'skip_mlp_layer_21': 1, 'skip_mlp_layer_22': 0, 'skip_mlp_layer_23': 0, 'skip_mlp_layer_24': 0, 'skip_mlp_layer_25': 1}. Best is trial 24 with value: 0.15.
The best params: {'skip_attn_layer_0': 0, 'skip_attn_layer_1': 0, 'skip_attn_layer_2': 0, 'skip_attn_layer_3': 0, 'skip_attn_layer_4': 1, 'skip_attn_layer_5': 0, 'skip_attn_layer_6': 0, 'skip_attn_layer_7': 0, 'skip_attn_layer_8': 0, 'skip_attn_layer_9': 1, 'skip_attn_layer_10': 0, 'skip_attn_layer_11': 0, 'skip_attn_layer_12': 0, 'skip_attn_layer_13': 0, 'skip_attn_layer_14': 0, 'skip_attn_layer_15': 1, 'skip_attn_layer_16': 0, 'skip_attn_layer_17': 1, 'skip_attn_layer_18': 1, 'skip_attn_layer_19': 1, 'skip_attn_layer_20': 1, 'skip_attn_layer_21': 0, 'skip_attn_layer_22': 0, 'skip_attn_layer_23': 0, 'skip_attn_layer_24': 0, 'skip_attn_layer_25': 1, 'skip_mlp_layer_0': 0, 'skip_mlp_layer_1': 0, 'skip_mlp_layer_2': 1, 'skip_mlp_layer_3': 0, 'skip_mlp_layer_4': 0, 'skip_mlp_layer_5': 0, 'skip_mlp_layer_6': 0, 'skip_mlp_layer_7': 1, 'skip_mlp_layer_8': 1, 'skip_mlp_layer_9': 1, 'skip_mlp_layer_10': 1, 'skip_mlp_layer_11': 0, 'skip_mlp_layer_12': 0, 'skip_mlp_layer_13': 0, 'skip_mlp_layer_14': 1, 'skip_mlp_layer_15': 0, 'skip_mlp_layer_16': 1, 'skip_mlp_layer_17': 0, 'skip_mlp_layer_18': 0, 'skip_mlp_layer_19': 1, 'skip_mlp_layer_20': 1, 'skip_mlp_layer_21': 1, 'skip_mlp_layer_22': 0, 'skip_mlp_layer_23': 0, 'skip_mlp_layer_24': 0, 'skip_mlp_layer_25': 1}
The best accept_rate: 0.15
最後測試的結果挺慘的，大概只有 0.15。不過根據我初步的測試，其實用大一點的模型比較能接受跳過幾層。
References
- dilab-zju/self-speculative-decoding: Code associated with …
- Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding