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| """ Glossary: b: batch size (`B` in Mamba paper [1] Algorithm 2) l: sequence length (`L` in [1] Algorithm 2) d or d_model: hidden dim n or d_state: latent state dim (`N` in [1] Algorithm 2) expand: expansion factor (`E` in [1] Section 3.4) d_in or d_inner: d * expand (`D` in [1] Algorithm 2) A, B, C, D: state space parameters (See any state space representation formula) (B, C are input-dependent (aka selective, a key innovation in Mamba); A, D are not) Δ or delta: input-dependent step size dt_rank: rank of Δ (See [1] Section 3.6 "Parameterization of ∆") """
import math import json from typing import Union import torch import torch.nn as nn import torch.nn.functional as F import einops from dataclasses import dataclass
@dataclass class ModelArgs: d_model: int n_layer: int vocab_size: int d_state: int = 16 expand: int = 2 dt_rank: Union[int, str] = 'auto' d_conv: int = 4 pad_vocab_size_multiple: int = 8 conv_bias: bool = True bias: bool = False
def __post__init__(self): self.d_inner = int(self.expand * self.d_model)
if self.dt_rank == 'auto': self.dt_rank = math.ceil(self.d_model / 16) if self.vocab_size % self.pad_vocab_size_multiple != 0: self.vocab_size += (self.pad_vocab_size_multiple - self.vocab_size % self.pad_vocab_size_multiple)
""" MabmbaBlock是最基本的Mamba模块,他实现的就是单个的MambaBlock """ class MambaBlock(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.args = args
self.linear1(args.d_inner, args.d_model, bias=args.bias) self.linear2(args.d_inner, args.d_model, bias=args.bias)
self.conv1d = nn.Conv1d( in_channels=args.d_inner, out_channels=args.d_inner, bias=args.conv_bias, kernel_size=args.d_conv, groups=args.d_inner, padding=args.d_conv - 1 )
self.x2Delta = nn.Linear(args.d_inner, args.dt_rank, bias=False) self.x2B = nn.Linear(args.d_inner, args.d_state, bias=False) self.x2C = nn.Linear(args.d_inner, args.d_state, bias=False)
self.dt_proj = nn.Linear(args.dt_rank, args.d_inner, bias=False)
A = einops.repeat(torch.arange(1, args.d_state + 1), 'n -> d n', d=args.d_inner) self.A_log = nn.Parameter(torch.log(A)) self.D = nn.Parameter(torch.ones(args.d_inner)) self.out_proj = nn.Linear(args.d_inner, args.d_model, bias=args.bias)
def forward(self, x): (b, l, d) = x.shape res = self.linear1(x) x = self.linear2(x) x = einops.rearrange(x, 'b l d_in -> b d_in l') x = self.conv1d(x) x =einops.rearrange(x, 'b d_in l -> b l d_in') x = F.silu(x)
y = self.ssm(x) y = y * F.silu(res) output = self.out_proj(y) return output def ssm(self, x): (d_in ,n) = self.A_log.shape A = - torch.exp(self.A_log.float()) D = self.D.float()
delta = self.x2Delta(x) B = self.x2B(x) C = self.x2C(x) delta = F.softplus(self.dt_proj(delta)) y = self.selective_scan(x, delta, A, B, C, D)
return y def selective_scan(self, x, delta, A, B, C, D): (b, l, d_in) = x.shape n = A.shape[1] A_bar = torch.exp(einops.einsum(delta, A, 'b l d_in, d_in n -> b l d_in n')) B_bar_x = einops.einsum(delta, B, x, 'b l d_in, b l n, b l d_in -> b l d_in n')
""" 下面这段没有用并行算法,实现起来也应该不复杂 我试着以后有时间实现一下 """ h = torch.zeros((b, d_in, n), device=A_bar.device) ys = [] for i in range(l): h = A_bar[:, i] * h + B_bar_x[:, i] y = einops.einsum(h, C[:, i, :], 'b d_in n, b n -> b d_in') ys.append(y) y = torch.stack(ys, dim=1) y = y + x * D return y
class ResidualBlock(nn.Module): def __init__(self, args: ModelArgs): """Simple block wrapping Mamba block with normalization and residual connection.""" super().__init__() self.args = args self.mixer = MambaBlock(args) self.norm = RMSNorm(args.d_model)
def forward(self, x): output = self.mixer(self.norm(x)) + x return output
""" 用多个Mamba块组成的模型 """ class Mamba(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.args = args self.embedding = nn.Embedding(args.vocab_size, args.d_model) self.layers = nn.ModuleList([ResidualBlock(args) for _ in range(args.n_layer)]) self.norm_f = RMSNorm(args.d_model)
self.lm_head = nn.Linear(args.d_model, args.vocab_size, bias=False) self.lm_head.weight = self.embedding.weight
def forward(self, input_ids): x = self.embedding(input_ids) for layer in self.layers: x = layer(x) x = self.norm_f(x) logits = self.lm_head(x) return logits
@staticmethod def from_pretrained(pretrained_model_name: str): """ 在huggingface的transformers库里找预训练好的模型 Args: pretrained_model_name: One of * 'state-spaces/mamba-2.8b-slimpj' * 'state-spaces/mamba-2.8b' * 'state-spaces/mamba-1.4b' * 'state-spaces/mamba-790m' * 'state-spaces/mamba-370m' * 'state-spaces/mamba-130m' Returns: model: Mamba model with weights loaded """ from transformers.utils import WEIGHTS_NAME, CONFIG_NAME from transformers.utils.hub import cached_file def load_config_hf(model_name): resolved_archive_file = cached_file(model_name, CONFIG_NAME, _raise_exceptions_for_missing_entries=False) return json.load(open(resolved_archive_file)) def load_state_dict_hf(model_name, device=None, dtype=None): resolved_archive_file = cached_file(model_name, WEIGHTS_NAME, _raise_exceptions_for_missing_entries=False) return torch.load(resolved_archive_file, weights_only=True, map_location='cpu', mmap=True) config_data = load_config_hf(pretrained_model_name) args = ModelArgs( d_model=config_data['d_model'], n_layer=config_data['n_layer'], vocab_size=config_data['vocab_size'] ) model = Mamba(args) state_dict = load_state_dict_hf(pretrained_model_name) new_state_dict = {} for key in state_dict: new_key = key.replace('backbone.', '') new_state_dict[new_key] = state_dict[key] model.load_state_dict(new_state_dict) return model
class RMSNorm(nn.Module): def __init__(self, d_model: int, eps: float = 1e-5): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.ones(d_model))
def forward(self, x): output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.weight return output
if __name__ == '__main__': def generate(model, tokenizer, prompt: str, n_tokens_to_gen: int = 50, sample: bool = True, top_k: int = 40): model.eval() input_ids = tokenizer(prompt, return_tensors='pt').input_ids for token_n in range(n_tokens_to_gen): with torch.no_grad(): indices_to_input = input_ids next_token_logits = model(indices_to_input)[:, -1] probs = F.softmax(next_token_logits, dim=-1) (batch, vocab_size) = probs.shape if top_k is not None: (values, indices) = torch.topk(probs, k=top_k) probs[probs < values[:, -1, None]] = 0 probs = probs / probs.sum(axis=1, keepdims=True) if sample: next_indices = torch.multinomial(probs, num_samples=1) else: next_indices = torch.argmax(probs, dim=-1)[:, None] input_ids = torch.cat([input_ids, next_indices], dim=1)
output_completions = [tokenizer.decode(output.tolist()) for output in input_ids][0] return output_completions pretrained_model_name = 'state-spaces/mamba-370m'
from transformers import AutoTokenizer model = Mamba.from_pretrained(pretrained_model_name) tokenizer = AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b') print(generate(model, tokenizer, 'Mamba is the'))
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