254 lines
9.9 KiB
Python
Vendored
254 lines
9.9 KiB
Python
Vendored
# -*- coding: utf-8 -*-
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# Sect4.2 of Linear Transformers Are Secretly Fast Weight Programmers https://arxiv.org/abs/2102.11174
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from __future__ import annotations
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from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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from einops import rearrange
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from transformers.cache_utils import Cache
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from fla.modules import FusedRMSNormSwishGate, RMSNorm, ShortConvolution, LayerNorm
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from fla.modules.rotary import RotaryEmbedding
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from fla.ops.delta_rule import (fused_chunk_delta_rule,
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fused_recurrent_linear_attn_delta_rule,
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chunk_delta_rule)
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from torch.nn import functional as F
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def simple_norm(x):
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return (F.normalize(x, dim=-1) * x.shape[-1] ** 0.5).to(x)
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# @torch.jit.script
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def elu_p1(x):
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return (F.elu(x, 1., False) + 1.).to(x)
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# @torch.jit.script
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def sum_norm(x):
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return (x / x.sum(-1, keepdim=True)).to(x)
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# @torch.jit.script
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def elu_norm(x):
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dtype = x.dtype
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x = F.elu(x, 1., False) + 1.
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return (x / x.sum(-1, keepdim=True)).to(dtype)
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# https://github.com/IDSIA/recurrent-fwp/blob/master/algorithmic/layers.py#L86C1-L146C1
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class DeltaNet(nn.Module):
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def __init__(
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self,
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d_model: int = None,
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hidden_size: int = 1024,
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expand_k: float = 1.0,
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expand_v: float = 1.0,
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num_heads: int = 4,
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mode: str = 'fused_chunk',
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chunk_size: int = 16,
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use_beta: bool = True,
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use_gate: bool = True,
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use_rope: bool = False,
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use_output_norm: bool = True,
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use_elu: bool = False,
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use_short_conv: bool = True,
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conv_size: int = 4,
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conv_bias: bool = False,
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share_conv_kernel: bool = False,
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layer_idx: int = None,
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qk_activation: str = 'silu',
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qk_norm: str = None,
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save_memory: str = False,
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**kwargs
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) -> DeltaNet:
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super().__init__()
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self.mode = mode
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self.qk_activation = qk_activation
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self.qk_norm = qk_norm
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assert self.qk_activation in ['silu', 'relu', 'elu', 'identity']
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assert self.qk_norm in ['l2', 'sum']
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if d_model is not None:
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hidden_size = d_model
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self.hidden_size = hidden_size
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self.expand_k = expand_k
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self.expand_v = expand_v
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self.num_heads = num_heads
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self.chunk_size = chunk_size
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self.use_gate = use_gate
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self.use_output_norm = use_output_norm
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self.use_short_conv = use_short_conv
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self.conv_size = conv_size
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self.conv_bias = conv_bias
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self.share_conv_kernel = share_conv_kernel
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self.key_dim = int(hidden_size * expand_k)
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self.value_dim = int(hidden_size * expand_v)
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self.head_qk_dim = self.key_dim // num_heads
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self.head_v_dim = self.value_dim // num_heads
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self.layer_idx = layer_idx
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self.silu = torch.nn.SiLU()
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assert mode in ['chunk', 'fused_chunk', 'fused_recurrent'], f"Not suppoerted mode `{mode}`."
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assert self.key_dim % num_heads == 0, f"key dim must be divisible by num_heads of {num_heads}"
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assert self.value_dim % num_heads == 0, f"value dim must be divisible by num_heads of {num_heads}"
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self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
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self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
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self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False)
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self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False)
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self.use_beta = use_beta
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self.use_elu = use_elu
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if self.use_beta:
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self.b_proj = nn.Linear(hidden_size, self.num_heads, bias=False)
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if use_short_conv:
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self.conv_size = conv_size
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if share_conv_kernel:
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self.h_conv1d = ShortConvolution(hidden_size, conv_size, activation=None)
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else:
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self.q_conv1d = ShortConvolution(self.key_dim, conv_size, activation='silu' if qk_activation == 'silu' else None)
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self.k_conv1d = ShortConvolution(self.key_dim, conv_size, activation='silu' if qk_activation == 'silu' else None)
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self.v_conv1d = ShortConvolution(self.value_dim, conv_size, activation='silu')
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if use_gate:
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self.g_proj = nn.Linear(hidden_size, self.value_dim, bias=False)
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if self.use_gate:
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self.norm = FusedRMSNormSwishGate(self.head_v_dim)
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else:
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self.norm = RMSNorm(self.head_v_dim)
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self.apply(self._initialize_weights)
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def _initialize_weights(self, module: nn.Module):
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if getattr(module, "_is_hf_initialized", False):
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return
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if isinstance(module, nn.Linear):
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nn.init.xavier_uniform_(module.weight, gain=2 ** -2.5)
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if module.bias is not None:
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nn.init.zeros_(module.bias)
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module._is_hf_initialized = True
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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past_key_values: Optional[Cache] = None,
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use_cache: Optional[bool] = False,
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output_attentions: Optional[bool] = False,
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**kwargs
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
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# change to inference mode.
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mode = 'fused_recurrent' if hidden_states.shape[1] < 64 else self.mode
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last_state = past_key_values[self.layer_idx] if use_cache else None
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if attention_mask is not None:
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if attention_mask.shape[-1] != hidden_states.shape[-2]:
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attention_mask = attention_mask[:, -1:]
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if self.use_short_conv:
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conv_state = last_state[0] if use_cache else None
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if self.share_conv_kernel:
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# conv state is updated inplace
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hidden_states = self.h_conv1d(hidden_states, attention_mask, conv_state)
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q = self.q_proj(hidden_states)
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k = self.k_proj(hidden_states)
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v = self.v_proj(hidden_states)
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else:
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conv_state_q = last_state[0] if use_cache else None
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conv_state_k = last_state[1] if use_cache else None
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conv_state_v = last_state[2] if use_cache else None
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k = self.k_proj(hidden_states)
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v = self.v_proj(hidden_states)
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q = self.q_proj(hidden_states)
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q = self.q_conv1d(q, attention_mask, conv_state_q)
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k = self.k_conv1d(k, attention_mask, conv_state_k)
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v = self.v_conv1d(v, attention_mask, conv_state_v)
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else:
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q = (self.q_proj(hidden_states))
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k = (self.k_proj(hidden_states))
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v = self.silu(self.v_proj(hidden_states))
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# dealing with left-padding
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if attention_mask is not None:
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v = v.mul_(attention_mask.unsqueeze(-1))
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q, k, v = map(lambda x: rearrange(x, 'b l (h d) -> b h l d', h=self.num_heads), (q, k, v))
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if self.qk_activation != 'silu':
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if self.qk_activation == 'relu':
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q, k = q.relu(), k.relu()
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elif self.qk_activation == 'elu':
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q, k = elu_p1(q), elu_p1(k)
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elif self.qk_activation == 'identity':
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pass
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else:
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raise NotImplementedError
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if self.qk_norm is not None:
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if self.qk_norm == 'l2':
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k = torch.nn.functional.normalize(k, dim=-1, p=2).to(v) #auto mixed precision type transfer is annoying.
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q = torch.nn.functional.normalize(q, dim=-1, p=2).to(v)
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elif self.qk_norm == 'sum':
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q = sum_norm(q).to(v)
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k = sum_norm(k).to(v)
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if self.use_beta:
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beta = rearrange(self.b_proj(hidden_states), 'b l h -> b h l').sigmoid()
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else:
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beta = q.new_ones(q.shape[0], q.shape[1], q.shape[2])
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state = past_key_values[self.layer_idx][-1] if use_cache else None
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if mode == 'fused_recurrent':
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o, recurrent_state = fused_recurrent_linear_attn_delta_rule(q, k, v, beta, state, output_final_state=use_cache)
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elif mode == 'fused_chunk':
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assert self.chunk_size in [16, 32, 64]
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o, recurrent_state = fused_chunk_delta_rule(q, k, v, beta, self.chunk_size, state, output_final_state=use_cache)
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elif mode == 'chunk':
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assert self.chunk_size in [16, 32, 64]
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o, recurrent_state = chunk_delta_rule(q, k, v, beta, self.chunk_size, state, output_final_state=use_cache)
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else:
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raise NotImplementedError(f"Not supported mode `{mode}`.")
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if past_key_values is not None:
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if self.use_short_conv:
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if self.share_conv_kernel:
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state = (conv_state, recurrent_state)
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else:
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state = (conv_state_q, conv_state_k, conv_state_v, recurrent_state)
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else:
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state = (recurrent_state,)
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past_key_values.update(state, self.layer_idx)
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o = rearrange(o, 'b h l d -> b l h d')
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if self.use_gate:
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g = rearrange(self.g_proj(hidden_states), 'b l (h d) -> b l h d', h=self.num_heads)
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o = self.norm(o, g)
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else:
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o = self.norm(o)
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o = rearrange(o, 'b l h d -> b l (h d)')
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o = self.o_proj(o)
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return o, None, past_key_values
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def init_state(self, batch_size: int) -> Tuple[torch.Tensor]:
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param = next(self.parameters())
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state = tuple()
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if self.use_short_conv:
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if self.share_conv_kernel:
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state += (param.new_zeros(batch_size, self.hidden_size, self.conv_size),)
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else:
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# for q/k/v each
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state += (param.new_zeros(batch_size, self.key_dim, self.conv_size),
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param.new_zeros(batch_size, self.key_dim, self.conv_size),
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param.new_zeros(batch_size, self.value_dim, self.conv_size))
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state += (param.new_zeros(batch_size, self.num_heads, self.head_qk_dim, self.head_v_dim),)
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return state |