| |
|
|
| from __future__ import annotations |
|
|
| from typing import TYPE_CHECKING |
|
|
| import torch |
| import torch.nn as nn |
| from einops import rearrange |
| from torch.nn import functional as F |
|
|
| from fla.layers.utils import get_unpad_data, index_first_axis, pad_input |
| from fla.modules import FusedRMSNormGated, RMSNorm, ShortConvolution |
| from fla.modules.l2norm import l2_norm |
| from fla.ops.mesa_net import chunk_mesa_net, mesa_net_decoding_one_step |
|
|
| if TYPE_CHECKING: |
| from transformers.processing_utils import Unpack |
|
|
| from fla.models.utils import Cache |
|
|
|
|
| class MesaNet(nn.Module): |
| """ |
| The layer implementaion for [MesaNet: Sequence Modeling by Locally Optimal Test-Time Training]. # noqa |
| |
| Args: |
| hidden_size (int, Optional): |
| The hidden size of the input. Default: 2048. |
| expand_v (float, Optional): |
| The expansion ratio for the value dim. Default: 1. |
| num_heads (int, Optional): |
| The number of heads. Default: 16. |
| mode (str, Optional): |
| Which MesaNet kernel to use. |
| Currently available: `chunk`. |
| Default: `chunk`. |
| use_output_gate (bool, Optional): |
| Whether to use output gate. Default: `False`. |
| conv_size (int): |
| The kernel size of the short convolution. Default: 4. |
| layer_idx (int, Optional): |
| The index of the layer. Default: None. |
| norm_eps (float, Optional): |
| The epsilon value for the normalization layer. Default: 1e-5. |
| lambda_lower_bound (float): |
| The lower bound for the lambda parameter. Default: 0.25. |
| max_cg_step_training (int): |
| The maximum number of CG steps for training. Default: 30. |
| max_cg_step_decoding (int): |
| The maximum number of CG steps for decoding. Default: 30. |
| """ |
|
|
| def __init__( |
| self, |
| hidden_size: int = 2048, |
| num_heads: int = 16, |
| head_dim: int = 128, |
| mode: str = 'chunk', |
| use_output_gate: bool = False, |
| use_short_conv: bool = True, |
| conv_size: int = 4, |
| conv_bias: bool = False, |
| layer_idx: int = None, |
| norm_eps: float = 1e-5, |
| lambda_lower_bound: float = 0.25, |
| max_cg_step_training: int = 30, |
| max_cg_step_decoding: int = 30, |
| **kwargs, |
| ) -> MesaNet: |
| super().__init__() |
|
|
| self.mode = mode |
| self.hidden_size = hidden_size |
| self.use_output_gate = use_output_gate |
| self.use_short_conv = use_short_conv |
| self.conv_size = conv_size |
| self.conv_bias = conv_bias |
| self.num_heads = num_heads |
| self.head_dim = head_dim |
| self.key_dim = self.num_heads * self.head_dim |
| self.value_dim = self.key_dim |
| self.head_k_dim = self.head_dim |
| self.head_v_dim = self.head_dim |
| self.layer_idx = layer_idx |
| self.lambda_lower_bound = lambda_lower_bound |
| self.max_cg_step_training = max_cg_step_training |
| self.max_cg_step_decoding = max_cg_step_decoding |
|
|
| self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False) |
| self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False) |
| self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False) |
| self.a_proj = nn.Linear(hidden_size, self.num_heads, bias=True) |
| self.b_proj = nn.Linear(hidden_size, self.num_heads, bias=True) |
|
|
| lambda_initial_value = 1.0 |
| init_lamb_value = torch.log(torch.exp(torch.tensor(lambda_initial_value - lambda_lower_bound)) - 1.0) |
| init_lamb_params = torch.empty(self.key_dim, dtype=torch.float32).fill_(init_lamb_value) |
|
|
| self.lambda_params = nn.Parameter(init_lamb_params) |
| self.lambda_params._no_weight_decay = True |
|
|
| self.conv_size = conv_size |
| self.q_conv1d = ShortConvolution( |
| hidden_size=self.key_dim, |
| kernel_size=conv_size, |
| bias=self.conv_bias, |
| activation='silu', |
| ) |
| self.k_conv1d = ShortConvolution( |
| hidden_size=self.key_dim, |
| kernel_size=conv_size, |
| bias=self.conv_bias, |
| activation='silu', |
| ) |
| if use_output_gate: |
| self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False) |
| self.o_norm = FusedRMSNormGated(self.head_v_dim, eps=norm_eps) |
| else: |
| self.o_norm = RMSNorm(self.head_v_dim, eps=norm_eps) |
| self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False) |
|
|
| def forward( |
| self, |
| hidden_states: torch.Tensor, |
| attention_mask: torch.Tensor | None = None, |
| past_key_values: Cache | None = None, |
| use_cache: bool | None = False, |
| output_attentions: bool | None = False, |
| **kwargs: Unpack[dict], |
| ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: |
| if attention_mask is not None: |
| assert len(attention_mask.shape) == 2, ( |
| "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] " |
| "for padding purposes (0 indicating padding). " |
| "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed." |
| ) |
|
|
| batch_size, q_len, _ = hidden_states.shape |
| last_state = None |
| if past_key_values is not None and len(past_key_values) > self.layer_idx: |
| last_state = past_key_values[self.layer_idx] |
|
|
| cu_seqlens = kwargs.get('cu_seqlens') |
| if attention_mask is not None: |
| indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:]) |
| hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0) |
|
|
| conv_state_q, conv_state_k = None, None |
| if last_state is not None: |
| conv_state_q, conv_state_k = last_state['conv_state'] |
| q, conv_state_q = self.q_conv1d( |
| x=self.q_proj(hidden_states), |
| cache=conv_state_q, |
| output_final_state=use_cache, |
| cu_seqlens=cu_seqlens, |
| ) |
| k, conv_state_k = self.k_conv1d( |
| x=self.k_proj(hidden_states), |
| cache=conv_state_k, |
| output_final_state=use_cache, |
| cu_seqlens=cu_seqlens, |
| ) |
| v = self.v_proj(hidden_states) |
|
|
| q, k = map(lambda x: rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim), (q, k)) |
| v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim) |
| beta = self.b_proj(hidden_states).float().sigmoid() |
| g = F.logsigmoid(self.a_proj(hidden_states).float()) |
| lamb = F.softplus(self.lambda_params.float()) + self.lambda_lower_bound |
| lamb = lamb.reshape(self.num_heads, -1) |
|
|
| last_h_kk, last_h_kv = last_state['recurrent_state'] if last_state is not None else (None, None) |
|
|
| |
| |
| if last_state is None: |
| o, h_kk, h_kv = chunk_mesa_net( |
| q=q, |
| k=k, |
| v=v, |
| g=g, |
| beta=beta, |
| lamb=lamb, |
| output_final_state=use_cache, |
| max_CG_iteration=self.max_cg_step_training, |
| use_qk_l2norm_in_kernel=True, |
| cu_seqlens=cu_seqlens, |
| ) |
| |
| else: |
| q = l2_norm(q) |
| k = l2_norm(k) |
| o, h_kk, h_kv = mesa_net_decoding_one_step( |
| q=q.squeeze(0), |
| k=k.squeeze(0), |
| v=v.squeeze(0), |
| g=g.squeeze(0), |
| beta=beta.squeeze(0), |
| lamb=lamb, |
| prev_h_kk=last_h_kk, |
| prev_h_kv=last_h_kv, |
| max_CG_iteration=self.max_cg_step_decoding, |
| ) |
| o = o.unsqueeze(0).to(q) |
|
|
| if past_key_values is not None: |
| past_key_values.update( |
| recurrent_state=(h_kk, h_kv), |
| conv_state=(conv_state_q, conv_state_k), |
| layer_idx=self.layer_idx, |
| offset=q_len, |
| ) |
| if self.use_output_gate: |
| g = rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim) |
| o = self.o_norm(o, g) |
| else: |
| o = self.o_norm(o) |
| o = rearrange(o, 'b t h d -> b t (h d)') |
| o = self.o_proj(o) |
| if attention_mask is not None: |
| o = pad_input(o.squeeze(0), indices, batch_size, q_len) |
| return o, None, past_key_values |
|
|
