import torch import torch.nn as nn from torchdiffeq import odeint import math class ODELinear(nn.Module): def __init__( self, dim: int, factor, **kwargs ): super().__init__() self.ode_up_proj = nn.Parameter(torch.empty(dim//2, factor*dim).to(torch.float32)) self.ode_down_proj = nn.Parameter(torch.empty(factor*dim, dim//2).to(torch.float32)) self.dim = dim self.act = torch.nn.SiLU() self.reset_parameters() def reset_parameters(self): nn.init.kaiming_uniform_(self.ode_up_proj, a=math.sqrt(5)) nn.init.zeros_(self.ode_down_proj) def get_time_embedding(self, t, base=10000, device='cuda', dtype=torch.float32): if t < 1: alpha = 1 else: alpha = 2*t-1 ntk_base = base * alpha ** (self.dim / (self.dim-2)) ntk_inv_freq = 1.0 / (ntk_base ** (torch.arange(0, self.dim, 2, dtype=torch.float32).to(device) / self.dim)) index = torch.arange(0, self.dim, 2, dtype=torch.float32).to(device) delta_ntk_freq = -2*index/(self.dim-2) * 1 / (base ** (index/self.dim) * (alpha ** (index/(self.dim-2) + 1))) return delta_ntk_freq.to(device, dtype=dtype), ntk_inv_freq.to(device, dtype=dtype) def forward(self, t, x: torch.Tensor): delta_time, time = self.get_time_embedding(t, device=x.device, dtype=x.dtype) x = x + torch.log(time) time_embed = delta_time / time delta_inv_freq = self.act(x @ self.ode_up_proj.float()) @ self.ode_down_proj.float() + time_embed return delta_inv_freq class LlamaCLEXScalingRotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=2048, rope_scaling=None, base=10000, device=None) -> None: super().__init__() self.max_t = rope_scaling["max_factor"] self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq) self.proj_func = ODELinear(dim, rope_scaling["param_factor"]) self.rope_cached = None self.max_t_cached = 0 self.freq_cached = None self.time_dt = 0.01 self.ode_args = { "method": "rk4", "options": {"step_size": self.time_dt}, } def sample_random_times(self, max_t, device): return torch.randint(2, max_t, (1,), dtype = torch.long, device=device) def get_random_position_ids(self, n=2048, max=8192): positions = torch.randperm(max)[:n].sort().values # positions = positions.to(device=device) return positions def get_continuous_freq(self, time_grid, ex_positions, device): solution = odeint( self.proj_func, torch.log(self.inv_freq.to(device, dtype=torch.float32)), time_grid, **self.ode_args ) if time_grid.size(0) == 2: training scale_inv_freq = torch.exp(solution[1]) # print(time_grid[1].tolist(), torch.sum(scale_inv_freq).tolist(), torch.sum(self.proj_func.ode_down_proj).tolist()) freqs = torch.outer(ex_positions.float().squeeze(), scale_inv_freq) else: scale_inv_freq = torch.exp(solution) freqs = torch.einsum('i, kl -> kil', ex_positions, scale_inv_freq) embed = torch.cat((freqs,freqs), dim=-1) return embed def forward(self, device, dtype, seq_len, do_train=False): device = self.proj_func.ode_up_proj.device scale_factor = seq_len // self.max_position_embeddings if do_train: t_val = self.sample_random_times(self.max_t+1, device)[0] import math sampled_position_ids = self.get_random_position_ids(n=seq_len-2, max=seq_len*t_val-2).float() ex_positions = torch.cat([ torch.tensor([0]), (sampled_position_ids + 1) / scale_factor, torch.tensor([seq_len*t_val//scale_factor-1])] ).to(device, dtype=torch.float32) else: t_val = scale_factor if seq_len%self.max_position_embeddings == 0.0 else scale_factor + 1 t_val = t_val if t_val <= self.max_t else self.max_t ex_positions = torch.arange(0, self.max_position_embeddings * t_val, dtype=torch.float32).to(device) if t_val == 1.0: scale_inv_freq = self.inv_freq.to(device) freqs = torch.outer(ex_positions.float().squeeze(), scale_inv_freq) embed = torch.cat((freqs,freqs), dim=-1) cos, sin = embed.cos()[None, None, :, :], embed.sin()[None, None, :, :] elif do_train: time_grid = torch.tensor([1.0, t_val]).float().to(device) embed = self.get_continuous_freq(time_grid, ex_positions, device) cos, sin = embed.cos()[None, None, :, :], embed.sin()[None, None, :, :] else: if t_val > self.max_t_cached: time_grid = torch.arange(1.0, self.max_t + 1.0, dtype=torch.float32).to(device) if self.freq_cached is None: self.freq_cached = self.get_continuous_freq(time_grid, ex_positions, device) embed = self.freq_cached[int(t_val)-1.0] self.rope_cached = torch.cat((embed.cos()[None, None, None, :, :], embed.sin()[None, None, None, :, :]), dim=0) self.max_t_cached = t_val cos, sin = self.rope_cached return torch.cat( (cos[None, :, :, :seq_len, ...].to(dtype=dtype), sin[None, :, :, :seq_len, ...].to(dtype=dtype)), dim=0 )