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| 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) | |
| return 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: | |
| if self.freq_cached is None: | |
| time_grid = torch.arange(1.0, self.max_t, dtype=torch.float32).to(device) | |
| self.freq_cached = self.get_continuous_freq(time_grid, ex_positions, device) | |
| scale_inv_freq = self.freq_cached[int(t_val-1.0)] | |
| freqs = torch.outer(ex_positions.float().squeeze(), scale_inv_freq) | |
| embed = torch.cat((freqs,freqs), dim=-1) | |
| 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 | |
| ) | |