265 lines
9.4 KiB
Python
Vendored
265 lines
9.4 KiB
Python
Vendored
# -*- coding: utf-8 -*-
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# "Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence"[https://arxiv.org/abs/2404.05892]
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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.activations import ACT2FN
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from transformers.cache_utils import Cache
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from fla.modules import FusedLayerNormSwishGate, LayerNorm
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from fla.ops.rwkv6 import chunk_rwkv6, fused_recurrent_rwkv6
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class RWKV6Attention(nn.Module):
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def __init__(
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self,
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mode: str = 'chunk',
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hidden_size: int = 1024,
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expand_k: float = 0.5,
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expand_v: float = 1.0,
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num_heads: int = 4,
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gate_fn: str = 'swish',
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proj_low_rank_dim: int = 32,
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gate_low_rank_dim: int = 64,
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fuse_norm: bool = True,
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elementwise_affine: Optional[bool] = True,
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norm_eps: float = 1e-5,
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layer_idx: int = None,
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**kwargs
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) -> RWKV6Attention:
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super().__init__()
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self.mode = mode
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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.proj_low_rank_dim = proj_low_rank_dim
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self.gate_low_rank_dim = gate_low_rank_dim
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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.layer_idx = layer_idx
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assert mode in ['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.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.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
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self.x_proj = nn.Sequential(
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LerpLinear(hidden_size, proj_low_rank_dim * 5),
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nn.Tanh(),
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nn.Linear(proj_low_rank_dim * 5, hidden_size, bias=True)
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)
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self.r_proj = DDLerpLinear(hidden_size, self.key_dim)
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self.w_proj = DDLerpLinear(hidden_size, self.key_dim, low_rank_dim=gate_low_rank_dim)
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self.k_proj = DDLerpLinear(hidden_size, self.key_dim)
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self.v_proj = DDLerpLinear(hidden_size, self.value_dim)
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self.g_proj = DDLerpLinear(hidden_size, self.value_dim)
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self.bonus = nn.Parameter(torch.zeros(num_heads, self.head_qk_dim))
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self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False)
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if gate_fn == 'swish' and fuse_norm:
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self.g_norm_swish_gate = FusedLayerNormSwishGate(self.head_v_dim, elementwise_affine, norm_eps)
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self.fuse_norm_and_gate = True
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else:
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self.fuse_norm_and_gate = False
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self.g_norm = LayerNorm(self.head_v_dim, elementwise_affine, norm_eps)
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self.gate_fn = ACT2FN[gate_fn]
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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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if isinstance(module, nn.Parameter):
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nn.init.xavier_uniform_(module, gain=2 ** -2.5)
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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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batch_size, seq_len, hidden_size = hidden_states.size()
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# launching the triton kernel for just one token will actually be slower
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mode = 'fused_recurrent' if hidden_states.shape[1] == 1 else self.mode
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delta = self.time_shift(hidden_states) - hidden_states
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x = self.x_proj[0](hidden_states, delta).view(batch_size, seq_len, -1, self.proj_low_rank_dim)
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r, w, k, v, g = torch.einsum('b l n r, n r d-> b l n d',
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self.x_proj[1](x),
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self.x_proj[2].weight.view(5, -1, hidden_size)).unbind(-2)
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r = self.r_proj(hidden_states, r, delta)
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w = self.w_proj(hidden_states, w, delta)
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k = self.k_proj(hidden_states, k, delta)
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v = self.v_proj(hidden_states, v, delta)
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g = self.g_proj(hidden_states, g, delta)
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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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r, w, k, v = map(lambda x: rearrange(x, 'b l (h d) -> b h l d', h=self.num_heads), (r, w, k, v))
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w = -torch.exp(w)
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u = self.bonus
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last_state = past_key_values[self.layer_idx] if use_cache else None
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state = last_state[-1] if use_cache else None
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if mode == 'fused_recurrent':
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o, recurrent_state = fused_recurrent_rwkv6(r, k, v, w, u, initial_state=state, output_final_state=use_cache)
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elif mode == 'chunk':
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o, recurrent_state = chunk_rwkv6(r, k, v, w, u, initial_state=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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past_key_values.update((recurrent_state,), self.layer_idx, r.shape[2])
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o = rearrange(o, 'b h l d -> b l h d')
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if self.fuse_norm_and_gate:
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g = rearrange(g, 'b l (h d) -> b l h d', h=self.num_heads)
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o = self.g_norm_swish_gate(o, g)
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o = rearrange(o, 'b l h d -> b l (h d)')
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else:
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o = self.g_norm(o)
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o = rearrange(o, 'b l h d -> b l (h d)')
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o = o * self.gate_fn(g)
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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 = (param.new_zeros(batch_size, self.num_heads, self.head_qk_dim, self.head_v_dim),)
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return state
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def state_size(self, **kwargs) -> int:
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state_size = self.key_dim * self.head_v_dim
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return state_size
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class LoRA(nn.Module):
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def __init__(
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self,
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input_dim: int,
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output_dim: int,
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low_rank_dim: int,
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bias: Optional[bool] = True
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):
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super().__init__()
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self.input_dim = input_dim
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self.output_dim = output_dim
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self.low_rank_dim = low_rank_dim
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self.bias = bias
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self.lora = nn.Sequential(
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nn.Linear(input_dim, low_rank_dim, bias=False),
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nn.Tanh(),
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nn.Linear(low_rank_dim, output_dim, bias=bias)
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)
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def __repr__(self) -> str:
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s = f"{self.__class__.__name__}("
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s += f"input_dim={self.input_dim}, low_rank_dim={self.low_rank_dim}, output_dim={self.output_dim}"
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if not self.bias:
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s += f", bias={self.bias}"
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s += ")"
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return s
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.lora(x)
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class LerpLinear(nn.Module):
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def __init__(
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self,
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input_dim: int,
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output_dim: int,
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low_rank_dim: Optional[int] = None
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):
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super().__init__()
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self.input_dim = input_dim
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self.output_dim = output_dim
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self.low_rank_dim = low_rank_dim
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self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
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if low_rank_dim is None:
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self.linear = nn.Linear(input_dim, output_dim, bias=False)
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else:
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self.linear = LoRA(input_dim, output_dim, low_rank_dim)
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self.mu = nn.Parameter(torch.zeros(input_dim))
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def __repr__(self) -> str:
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s = f"{self.__class__.__name__}({self.input_dim}, {self.output_dim}"
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if self.low_rank_dim is not None:
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s += f", low_rank_dim={self.low_rank_dim}"
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s += ")"
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return s
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def forward(self, x: torch.Tensor, delta: Optional[torch.Tensor] = None) -> torch.Tensor:
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if delta is None:
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shifted = self.time_shift(x)
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if len(shifted.shape) == 2:
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shifted = shifted.unsqueeze(1)
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delta = shifted - x
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return self.linear(x + delta * self.mu)
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class DDLerpLinear(nn.Module):
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def __init__(
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self,
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input_dim: int,
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output_dim: int,
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low_rank_dim: Optional[int] = None
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):
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super().__init__()
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self.input_dim = input_dim
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self.output_dim = output_dim
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self.low_rank_dim = low_rank_dim
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self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
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if low_rank_dim is None:
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self.linear = nn.Linear(input_dim, output_dim, bias=False)
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else:
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self.linear = LoRA(input_dim, output_dim, low_rank_dim)
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def __repr__(self) -> str:
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s = f"{self.__class__.__name__}({self.input_dim}, {self.output_dim}"
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if self.low_rank_dim is not None:
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s += f", low_rank_dim={self.low_rank_dim}"
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s += ")"
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return s
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def forward(self, x: torch.Tensor, mu: torch.Tensor, delta: Optional[torch.Tensor] = None) -> torch.Tensor:
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if delta is None:
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shifted = self.time_shift(x)
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if len(shifted.shape) == 2:
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shifted = shifted.unsqueeze(1)
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delta = shifted - x
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return self.linear(x + delta * mu)
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