LLaMA 3
2024年4月18日，Meta 重磅推出了Meta Llama 3，Llama 3是Meta最先进开源大型语言模型的下一代，包括具有80亿和700亿参数的预训练和指令微调的语言模型，能够支持广泛的应用场景。这一代Llama在一系列行业标准基准测试中展示了最先进的性能，并提供了新的功能，包括改进的推理能力。
版本和性能
新的 8B 和 70B 参数 Llama 3 模型是 Llama 2 的重大飞跃，并为这些规模的 LLM 模型建立了新的最先进技术。由于预训练和训练后的改进，模型是当今 8B 和 70B 参数规模的最佳模型。我训练后程序的改进大大降低了错误拒绝率，改善了一致性并增加了模型响应的多样性。我们还看到了推理、代码生成和指令跟踪等功能的极大改进，使 Llama 3 更加易于操控。
模型架构
从模型架构上看，LLaMA 3和LLaMA 2基本没有区别，同样使用了Transformer的Decoder-only架构，加入RMSNorm预归一化，使用 SwiGLU 激活函数和旋转位置嵌入，使用了改进的注意力机制GQA，增加了上下文长度。故本文不具体解释。
上述具体的技术和方法可以查看LLaMA 2的博客：点击此处
模型代码如下，代码来自LLaMA 3：https://github.com/meta-llama/llama3
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
import math
from dataclasses import dataclass
from typing import Optional, Tuple
import fairscale.nn.model_parallel.initialize as fs_init
import torch
import torch.nn.functional as F
from fairscale.nn.model_parallel.layers import (
ColumnParallelLinear,
RowParallelLinear,
VocabParallelEmbedding,
)
from torch import nn
@dataclass
class ModelArgs:
dim: int = 4096
n_layers: int = 32
n_heads: int = 32
n_kv_heads: Optional[int] = None
vocab_size: int = -1
multiple_of: int = 256 # make SwiGLU hidden layer size multiple of large power of 2
ffn_dim_multiplier: Optional[float] = None
norm_eps: float = 1e-5
rope_theta: float = 500000
max_batch_size: int = 32
max_seq_len: int = 2048
class RMSNorm(torch.nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
output = self._norm(x.float()).type_as(x)
return output * self.weight
def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
t = torch.arange(end, device=freqs.device, dtype=torch.float32)
freqs = torch.outer(t, freqs)
freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
return freqs_cis
def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
ndim = x.ndim
assert 0 <= 1 < ndim
assert freqs_cis.shape == (x.shape[1], x.shape[-1])
shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
return freqs_cis.view(*shape)
def apply_rotary_emb(
xq: torch.Tensor,
xk: torch.Tensor,
freqs_cis: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
return xq_out.type_as(xq), xk_out.type_as(xk)
def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
"""torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
bs, slen, n_kv_heads, head_dim = x.shape
if n_rep == 1:
return x
return (
x[:, :, :, None, :]
.expand(bs, slen, n_kv_heads, n_rep, head_dim)
.reshape(bs, slen, n_kv_heads * n_rep, head_dim)
)
class Attention(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
model_parallel_size = fs_init.get_model_parallel_world_size()
self.n_local_heads = args.n_heads // model_parallel_size
self.n_local_kv_heads = self.n_kv_heads // model_parallel_size
self.n_rep = self.n_local_heads // self.n_local_kv_heads
self.head_dim = args.dim // args.n_heads
self.wq = ColumnParallelLinear(
args.dim,
args.n_heads * self.head_dim,
bias=False,
gather_output=False,
init_method=lambda x: x,
)
self.wk = ColumnParallelLinear(
args.dim,
self.n_kv_heads * self.head_dim,
bias=False,
gather_output=False,
init_method=lambda x: x,
)
self.wv = ColumnParallelLinear(
args.dim,
self.n_kv_heads * self.head_dim,
bias=False,
gather_output=False,
init_method=lambda x: x,
)
self.wo = RowParallelLinear(
args.n_heads * self.head_dim,
args.dim,
bias=False,
input_is_parallel=True,
init_method=lambda x: x,
)
self.cache_k = torch.zeros(
(
args.max_batch_size,
args.max_seq_len,
self.n_local_kv_heads,
self.head_dim,
)
).cuda()
self.cache_v = torch.zeros(
(
args.max_batch_size,
args.max_seq_len,
self.n_local_kv_heads,
self.head_dim,
)
).cuda()
def forward(
self,
x: torch.Tensor,
start_pos: int,
freqs_cis: torch.Tensor,
mask: Optional[torch.Tensor],
):
bsz, seqlen, _ = x.shape
xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis)
self.cache_k = self.cache_k.to(xq)
self.cache_v = self.cache_v.to(xq)
self.cache_k[:bsz, start_pos : start_pos + seqlen] = xk
self.cache_v[:bsz, start_pos : start_pos + seqlen] = xv
keys = self.cache_k[:bsz, : start_pos + seqlen]
values = self.cache_v[:bsz, : start_pos + seqlen]
# repeat k/v heads if n_kv_heads < n_heads
keys = repeat_kv(
keys, self.n_rep
) # (bs, cache_len + seqlen, n_local_heads, head_dim)
values = repeat_kv(
values, self.n_rep
) # (bs, cache_len + seqlen, n_local_heads, head_dim)
xq = xq.transpose(1, 2) # (bs, n_local_heads, seqlen, head_dim)
keys = keys.transpose(1, 2) # (bs, n_local_heads, cache_len + seqlen, head_dim)
values = values.transpose(
1, 2
) # (bs, n_local_heads, cache_len + seqlen, head_dim)
scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim)
if mask is not None:
scores = scores + mask # (bs, n_local_heads, seqlen, cache_len + seqlen)
scores = F.softmax(scores.float(), dim=-1).type_as(xq)
output = torch.matmul(scores, values) # (bs, n_local_heads, seqlen, head_dim)
output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
return self.wo(output)
class FeedForward(nn.Module):
def __init__(
self,
dim: int,
hidden_dim: int,
multiple_of: int,
ffn_dim_multiplier: Optional[float],
):
super().__init__()
hidden_dim = int(2 * hidden_dim / 3)
# custom dim factor multiplier
if ffn_dim_multiplier is not None:
hidden_dim = int(ffn_dim_multiplier * hidden_dim)
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
self.w1 = ColumnParallelLinear(
dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x
)
self.w2 = RowParallelLinear(
hidden_dim, dim, bias=False, input_is_parallel=True, init_method=lambda x: x
)
self.w3 = ColumnParallelLinear(
dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x
)
def forward(self, x):
return self.w2(F.silu(self.w1(x)) * self.w3(x))
class TransformerBlock(nn.Module):
def __init__(self, layer_id: int, args: ModelArgs):
super().__init__()
self.n_heads = args.n_heads
self.dim = args.dim
self.head_dim = args.dim // args.n_heads
self.attention = Attention(args)
self.feed_forward = FeedForward(
dim=args.dim,
hidden_dim=4 * args.dim,
multiple_of=args.multiple_of,
ffn_dim_multiplier=args.ffn_dim_multiplier,
)
self.layer_id = layer_id
self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
def forward(
self,
x: torch.Tensor,
start_pos: int,
freqs_cis: torch.Tensor,
mask: Optional[torch.Tensor],
):
h = x + self.attention(self.attention_norm(x), start_pos, freqs_cis, mask)
out = h + self.feed_forward(self.ffn_norm(h))
return out
class Transformer(nn.Module):
def __init__(self, params: ModelArgs):
super().__init__()
self.params = params
self.vocab_size = params.vocab_size
self.n_layers = params.n_layers
self.tok_embeddings = VocabParallelEmbedding(
params.vocab_size, params.dim, init_method=lambda x: x
)
self.layers = torch.nn.ModuleList()
for layer_id in range(params.n_layers):
self.layers.append(TransformerBlock(layer_id, params))
self.norm = RMSNorm(params.dim, eps=params.norm_eps)
self.output = ColumnParallelLinear(
params.dim, params.vocab_size, bias=False, init_method=lambda x: x
)
self.freqs_cis = precompute_freqs_cis(
params.dim // params.n_heads,
params.max_seq_len * 2,
params.rope_theta,
)
@torch.inference_mode()
def forward(self, tokens: torch.Tensor, start_pos: int):
_bsz, seqlen = tokens.shape
h = self.tok_embeddings(tokens)
self.freqs_cis = self.freqs_cis.to(h.device)
freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen]
mask = None
if seqlen > 1:
mask = torch.full((seqlen, seqlen), float("-inf"), device=tokens.device)
mask = torch.triu(mask, diagonal=1)
# When performing key-value caching, we compute the attention scores
# only for the new sequence. Thus, the matrix of scores is of size # (seqlen, cache_len + seqlen), and the only masked entries are (i, j) for # j > cache_len + i, since row i corresponds to token cache_len + i. mask = torch.hstack(
[torch.zeros((seqlen, start_pos), device=tokens.device), mask]
).type_as(h)
for layer in self.layers:
h = layer(h, start_pos, freqs_cis, mask)
h = self.norm(h)
output = self.output(h).float()
return output
Tokenizer
LLaMA3 改进了Tokenizer，使得对长文本的处理更快。
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
import os
from logging import getLogger
from pathlib import Path
from typing import (
AbstractSet,
cast,
Collection,
Dict,
Iterator,
List,
Literal,
Sequence,
TypedDict,
Union,
)
import tiktoken
from tiktoken.load import load_tiktoken_bpe
logger = getLogger(__name__)
Role = Literal["system", "user", "assistant"]
class Message(TypedDict):
role: Role
content: str
Dialog = Sequence[Message]
class Tokenizer:
"""
Tokenizing and encoding/decoding text using the Tiktoken tokenizer. """
special_tokens: Dict[str, int]
num_reserved_special_tokens = 256
pat_str = r"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+" # noqa: E501
def __init__(self, model_path: str):
"""
Initializes the Tokenizer with a Tiktoken model.
Args: model_path (str): The path to the Tiktoken model file. """ assert os.path.isfile(model_path), model_path
mergeable_ranks = load_tiktoken_bpe(model_path)
num_base_tokens = len(mergeable_ranks)
special_tokens = [
"<|begin_of_text|>",
"<|end_of_text|>",
"<|reserved_special_token_0|>",
"<|reserved_special_token_1|>",
"<|reserved_special_token_2|>",
"<|reserved_special_token_3|>",
"<|start_header_id|>",
"<|end_header_id|>",
"<|reserved_special_token_4|>",
"<|eot_id|>", # end of turn
] + [
f"<|reserved_special_token_{i}|>"
for i in range(5, self.num_reserved_special_tokens - 5)
]
self.special_tokens = {
token: num_base_tokens + i for i, token in enumerate(special_tokens)
}
self.model = tiktoken.Encoding(
name=Path(model_path).name,
pat_str=self.pat_str,
mergeable_ranks=mergeable_ranks,
special_tokens=self.special_tokens,
)
logger.info(f"Reloaded tiktoken model from {model_path}")
self.n_words: int = self.model.n_vocab
# BOS / EOS token IDs
self.bos_id: int = self.special_tokens["<|begin_of_text|>"]
self.eos_id: int = self.special_tokens["<|end_of_text|>"]
self.pad_id: int = -1
self.stop_tokens = {
self.special_tokens["<|end_of_text|>"],
self.special_tokens["<|eot_id|>"],
}
logger.info(
f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}"
)
def encode(
self,
s: str,
*,
bos: bool,
eos: bool,
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = (),
) -> List[int]:
"""
Encodes a string into a list of token IDs.
Args: s (str): The input string to be encoded. bos (bool): Whether to prepend the beginning-of-sequence token. eos (bool): Whether to append the end-of-sequence token. allowed_tokens ("all"|set[str]): allowed special tokens in string disallowed_tokens ("all"|set[str]): special tokens that raise an error when in string
Returns: list[int]: A list of token IDs.
By default, setting disallowed_special=() encodes a string by ignoring special tokens. Specifically: - Setting `disallowed_special` to () will cause all text corresponding to special tokens to be encoded as natural text (insteading of raising an error). - Setting `allowed_special` to "all" will treat all text corresponding to special tokens to be encoded as special tokens. """ assert type(s) is str
# The tiktoken tokenizer can handle <=400k chars without
# pyo3_runtime.PanicException. TIKTOKEN_MAX_ENCODE_CHARS = 400_000
# https://github.com/openai/tiktoken/issues/195
# Here we iterate over subsequences and split if we exceed the limit # of max consecutive non-whitespace or whitespace characters. MAX_NO_WHITESPACES_CHARS = 25_000
substrs = (
substr
for i in range(0, len(s), TIKTOKEN_MAX_ENCODE_CHARS)
for substr in self._split_whitespaces_or_nonwhitespaces(
s[i : i + TIKTOKEN_MAX_ENCODE_CHARS], MAX_NO_WHITESPACES_CHARS
)
)
t: List[int] = []
for substr in substrs:
t.extend(
self.model.encode(
substr,
allowed_special=allowed_special,
disallowed_special=disallowed_special,
)
)
if bos:
t.insert(0, self.bos_id)
if eos:
t.append(self.eos_id)
return t
def decode(self, t: Sequence[int]) -> str:
"""
Decodes a list of token IDs into a string.
Args: t (List[int]): The list of token IDs to be decoded.
Returns: str: The decoded string. """ # Typecast is safe here. Tiktoken doesn't do anything list-related with the sequence.
return self.model.decode(cast(List[int], t))
@staticmethod
def _split_whitespaces_or_nonwhitespaces(
s: str, max_consecutive_slice_len: int
) -> Iterator[str]:
"""
Splits the string `s` so that each substring contains no more than `max_consecutive_slice_len` consecutive whitespaces or consecutive non-whitespaces. """ current_slice_len = 0
current_slice_is_space = s[0].isspace() if len(s) > 0 else False
slice_start = 0
for i in range(len(s)):
is_now_space = s[i].isspace()
if current_slice_is_space ^ is_now_space:
current_slice_len = 1
current_slice_is_space = is_now_space
else:
current_slice_len += 1
if current_slice_len > max_consecutive_slice_len:
yield s[slice_start:i]
slice_start = i
current_slice_len = 1
yield s[slice_start:]
class ChatFormat:
def __init__(self, tokenizer: Tokenizer):
self.tokenizer = tokenizer
def encode_header(self, message: Message) -> List[int]:
tokens = []
tokens.append(self.tokenizer.special_tokens["<|start_header_id|>"])
tokens.extend(self.tokenizer.encode(message["role"], bos=False, eos=False))
tokens.append(self.tokenizer.special_tokens["<|end_header_id|>"])
tokens.extend(self.tokenizer.encode("\n\n", bos=False, eos=False))
return tokens
def encode_message(self, message: Message) -> List[int]:
tokens = self.encode_header(message)
tokens.extend(
self.tokenizer.encode(message["content"].strip(), bos=False, eos=False)
)
tokens.append(self.tokenizer.special_tokens["<|eot_id|>"])
return tokens
def encode_dialog_prompt(self, dialog: Dialog) -> List[int]:
tokens = []
tokens.append(self.tokenizer.special_tokens["<|begin_of_text|>"])
for message in dialog:
tokens.extend(self.encode_message(message))
# Add the start of an assistant message for the model to complete.
tokens.extend(self.encode_header({"role": "assistant", "content": ""}))
return tokens
- 为了防止因字符串过长而产生的性能问题，
encode方法使用一个循环来处理不超过 400,000 字符的子字符串。这种方法可以避免运行时错误，例如在 Python 的外部库（如 C 或 Rust 写的库）中可能发生的内存错误。 - 使用
_split_whitespaces_or_nonwhitespaces方法来处理可能的大量连续空格或非空格字符，限制每个片段的最大长度为 25,000 字符。这样做既保证了处理的灵活性，也避免了处理过长片段可能带来的问题。
训练数据
为了训练最佳的语言模型，收集一个大规模、高质量的训练数据集至关重要。Meta AI在预训练数据上投入了大量资金。Llama 3在超过15T的token上进行预训练，所有数据都来自公开可用的来源。我们的训练数据集比用于Llama 2的数据集大了七倍，并且包括了四倍的代码。为了准备即将到来的多语言用例，超过5%的Llama 3预训练数据集由高质量的非英语数据组成，覆盖了超过30种语言。然而，我们不期望在这些语言中达到与英语相同的性能水平。
为了确保Llama 3训练的数据质量最高，我们开发了一系列数据过滤管道。这些管道包括使用启发式过滤器、NSFW过滤器、语义去重方法和文本分类器来预测数据质量。我们发现，Llama的前几代在识别高质量数据方面出奇地好，因此我们使用Llama 2生成了为Llama 3提供动力的文本质量分类器的训练数据。
为了在Llama 3模型中有效利用我们的预训练数据，我们投入了大量精力来扩大预训练规模。具体来说，我们为下游基准评估开发了一系列详细的扩展法则。这些扩展法则使我们能够选择最佳数据混合方案，并就如何最佳利用我们的训练计算资源做出明智的决策。重要的是，扩展法则允许我们在实际训练模型之前预测我们最大模型在关键任务上的性能。这帮助我们确保最终模型在各种使用场景和能力上的强劲性能。
在Llama 3的开发过程中，我们对扩展行为做出了几项新的观察。例如，虽然对于80亿参数模型来说，Chinchilla最优的训练计算量对应于约2000亿个token，但我们发现即使模型在数据量增加两个数量级后，模型性能仍然在持续提升。在我们的80亿和700亿参数模型经过高达15T个token的训练后，它们的性能继续以对数线性方式提升。大型模型可以在较少的训练计算量下匹配这些小型模型的性能，但通常更倾向于使用小型模型，因为它们在推理过程中效率更高。
为了训练我们最大的Llama 3模型，我们结合了三种类型的并行化：数据并行化、模型并行化和流水线并行化。我们最有效的实现方式在同时训练16K个GPU时，每个GPU的计算利用率超过400 TFLOPS。我们在两个定制构建的24K GPU集群上执行了训练运行。为了最大化GPU的运行时间，我们开发了一个新的高级训练堆栈，自动化了错误检测、处理和维护。我们还大大提高了硬件的可靠性和检测机制，用于静默数据损坏，并开发了新的可扩展存储系统，减少了检查点和回滚的开销。这些改进使得整体有效训练时间超过了95%。综合来看，这些改进将Llama 3的训练效率提高了约三倍，与Llama 2相比。
指令微调
为了充分释放我们预训练模型在聊天用例中的潜力，我们对指令调整方法也进行了创新。我们的后训练方法是监督式微调（SFT）、拒绝采样、近端策略优化（PPO）和直接策略优化（DPO）的组合。用于SFT的提示质量和用于PPO和DPO的偏好排名对对齐模型的性能有巨大影响。我们在模型质量上的一些最大改进来自于仔细筛选这些数据，并对人类标注者提供的多轮质量保证进行多次审查。
通过PPO和DPO从偏好排名中学习也大大提高了Llama 3在推理和编码任务上的性能。我们发现，如果你问一个模型一个它难以回答的推理问题，模型有时会产生正确的推理轨迹：模型知道如何产生正确的答案，但它不知道如何选择它。在偏好排名上进行训练使模型学会了如何选择它。
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