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# "Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence"[https://arxiv.org/abs/2404.05892]
from __future__ import annotations
import math
import warnings
from typing import TYPE_CHECKING
import torch
import torch.nn as nn
from einops import rearrange
from fla.modules import GroupNorm
from fla.modules.activations import ACT2FN
from fla.modules.token_shift import token_shift
from fla.ops.rwkv6 import chunk_rwkv6, fused_recurrent_rwkv6
if TYPE_CHECKING:
from fla.models.utils import Cache
class RWKV6Attention(nn.Module):
def __init__(
self,
mode: str = 'chunk',
hidden_size: int = 1024,
expand_k: float = 0.5,
expand_v: float = 1.0,
num_heads: int = 4,
gate_fn: str = 'swish',
proj_low_rank_dim: int = 32,
gate_low_rank_dim: int = 64,
fuse_norm: bool = True,
elementwise_affine: bool | None = True,
norm_eps: float = 1e-5,
layer_idx: int = None,
**kwargs,
) -> RWKV6Attention:
super().__init__()
self.mode = mode
self.hidden_size = hidden_size
self.expand_k = expand_k
self.expand_v = expand_v
self.num_heads = num_heads
self.proj_low_rank_dim = proj_low_rank_dim
self.gate_low_rank_dim = gate_low_rank_dim
self.key_dim = int(hidden_size * expand_k)
self.value_dim = int(hidden_size * expand_v)
self.layer_idx = layer_idx
assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`."
assert self.key_dim % num_heads == 0, f"key dim must be divisible by num_heads of {num_heads}"
assert self.value_dim % num_heads == 0, f"value dim must be divisible by num_heads of {num_heads}"
self.head_k_dim = self.key_dim // num_heads
self.head_v_dim = self.value_dim // num_heads
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
self.x_proj = nn.Sequential(
LerpLinear(hidden_size, proj_low_rank_dim * 5),
nn.Tanh(),
nn.Linear(proj_low_rank_dim * 5, hidden_size, bias=False),
)
self.x_bias = nn.Parameter(torch.zeros(5, hidden_size))
self.r_proj = DDLerpLinear(hidden_size, self.key_dim)
self.w_proj = DDLerpLinear(hidden_size, self.key_dim, low_rank_dim=gate_low_rank_dim)
self.k_proj = DDLerpLinear(hidden_size, self.key_dim)
self.v_proj = DDLerpLinear(hidden_size, self.value_dim)
self.g_proj = DDLerpLinear(hidden_size, self.value_dim)
self.bonus = nn.Parameter(torch.zeros(num_heads, self.head_k_dim))
# TODO: fuse GroupNorm and output gate
self.g_norm = GroupNorm(self.num_heads, self.value_dim, elementwise_affine=elementwise_affine, bias=True, eps=norm_eps)
self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False)
self.gate_fn = ACT2FN[gate_fn]
try:
from transformers.modeling_utils import _init_weights
except ImportError:
_init_weights = True
if _init_weights:
self.apply(self._initialize_weights)
warnings.warn(
"According to Bo, you are using a potentially buggy FLA implementation of RWKV. "
"If you plan to report any numbers based on this implementation, we strongly recommend "
"cross-checking with the official repo: https://github.com/BlinkDL/RWKV-LM. "
"Bo may disagree with results reported from this version.",
)
def _initialize_weights(self, module: nn.Module):
if getattr(module, "_is_hf_initialized", False):
return
if isinstance(module, nn.Linear):
nn.init.xavier_uniform_(module.weight, gain=2 ** -2.5)
if module.bias is not None:
nn.init.zeros_(module.bias)
if isinstance(module, nn.Parameter):
nn.init.xavier_uniform_(module, gain=2 ** -2.5)
module._is_hf_initialized = True
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,
cu_seqlens: torch.LongTensor | None = None,
**kwargs,
) -> 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, seq_len, hidden_size = hidden_states.shape
# launching the triton kernel for just one token will actually be slower
mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode
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]
if attention_mask is not None:
hidden_states = hidden_states.mul_(attention_mask[:, -hidden_states.shape[-2]:, None])
if hidden_states.shape[1] == 1 and last_state is not None:
shifted = last_state['conv_state'].unsqueeze(1)
delta = shifted - hidden_states
elif last_state is None:
delta = token_shift(hidden_states, cu_seqlens)
else:
shifted = self.time_shift(hidden_states)
shifted[:, 0] = last_state['conv_state']
delta = shifted - hidden_states
x = self.x_proj[0](hidden_states, delta, cu_seqlens).view(batch_size, seq_len, -1, self.proj_low_rank_dim)
x = torch.einsum('b t n r, h n r-> b t n h', self.x_proj[1](x), self.x_proj[2].weight.view(hidden_size, 5, -1))
r, w, k, v, g = x.add_(self.x_bias).unbind(-2)
r = self.r_proj(hidden_states, r, delta, cu_seqlens)
w = self.w_proj(hidden_states, w, delta, cu_seqlens)
k = self.k_proj(hidden_states, k, delta, cu_seqlens)
v = self.v_proj(hidden_states, v, delta, cu_seqlens)
g = self.g_proj(hidden_states, g, delta, cu_seqlens)
# dealing with left-padding
if attention_mask is not None:
v = v.mul_(attention_mask[:, -v.shape[-2]:, None])
r, w, k = map(lambda x: rearrange(x, 'b t (h d) -> b t h d', d=self.head_k_dim), (r, w, k))
v = rearrange(v, 'b t (h d) -> b t h d', d=self.head_v_dim)
w = -torch.exp(w)
u = self.bonus
recurrent_state = last_state['recurrent_state'] if last_state is not None else None
if mode == 'fused_recurrent':
o, recurrent_state = fused_recurrent_rwkv6(
r=r,
k=k,
v=v,
w=w,
u=u,
scale=1.,
initial_state=recurrent_state,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
elif mode == 'chunk':
o, recurrent_state = chunk_rwkv6(
r=r,
k=k,
v=v,
w=w,
u=u,
scale=1.,
initial_state=recurrent_state,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
else:
raise NotImplementedError(f"Not supported mode `{mode}`.")
if past_key_values is not None:
past_key_values.update(
recurrent_state=recurrent_state,
conv_state=hidden_states[:, -1],
layer_idx=self.layer_idx,
offset=r.shape[2],
)
o = self.g_norm(rearrange(o, '... h d -> ... (h d)')) * self.gate_fn(g)
o = self.o_proj(o)
return o, None, past_key_values
class LoRA(nn.Module):
def __init__(
self,
input_dim: int,
output_dim: int,
low_rank_dim: int,
bias: bool | None = True,
activation: str | None = 'tanh',
):
super().__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.low_rank_dim = low_rank_dim
self.bias = bias
if activation is None:
self.activation = nn.Identity()
elif activation == 'sigmoid':
self.activation = nn.Sigmoid()
elif activation == 'tanh':
self.activation = nn.Tanh()
elif activation == 'relu':
self.activation = nn.ReLU()
else:
raise ValueError(f"Not supported activation `{activation}`.")
self.lora = nn.Sequential(
nn.Linear(input_dim, low_rank_dim, bias=False),
self.activation,
nn.Linear(low_rank_dim, output_dim, bias=bias),
)
try:
from transformers.modeling_utils import _init_weights
except ImportError:
_init_weights = True
if _init_weights:
self.apply(self._initialize_weights)
def __repr__(self) -> str:
s = f"{self.__class__.__name__}("
s += f"input_dim={self.input_dim}, low_rank_dim={self.low_rank_dim}, output_dim={self.output_dim}"
if not self.bias:
s += f", bias={self.bias}"
s += ")"
return s
def _initialize_weights(self, module: nn.Module):
if getattr(module, "_is_hf_initialized", False):
return
# Initialize weights to zero as in original code
nn.init.zeros_(self.lora[0].weight)
original_dtype = self.lora[2].weight.dtype
shape = self.lora[2].weight.shape
# Convert to float32 for numerical stability in orthogonal init
weight_fp32 = self.lora[2].weight.float()
# Calculate gain based on dimensions
gain = math.sqrt(shape[1] / shape[0]) if shape[1] > shape[0] else 1
# Apply orthogonal initialization with scaling factor 0.1
nn.init.orthogonal_(weight_fp32, gain=gain * 0.1)
# Convert back to original dtype
self.lora[2].weight.data.copy_(weight_fp32.to(original_dtype))
# Set Lora[2] bias to zero
if self.lora[2].bias is not None:
nn.init.zeros_(self.lora[2].bias)
module._is_hf_initialized = True
def set_bias_value(self, value):
"""Set bias to a specific value (for v0, w0 etc.)"""
if self.bias and self.lora[2].bias is not None:
if isinstance(value, torch.Tensor):
# Handle tensor values
self.lora[2].bias.data.copy_(value.to(self.lora[2].bias.dtype))
else:
# Handle scalar values
nn.init.constant_(self.lora[2].bias, value)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.lora(x)
class LerpLinear(nn.Module):
def __init__(
self,
input_dim: int,
output_dim: int,
low_rank_dim: int | None = None,
):
super().__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.low_rank_dim = low_rank_dim
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
if low_rank_dim is None:
self.linear = nn.Linear(input_dim, output_dim, bias=False)
else:
self.linear = LoRA(input_dim, output_dim, low_rank_dim)
self.mu = nn.Parameter(torch.zeros(input_dim))
def __repr__(self) -> str:
s = f"{self.__class__.__name__}({self.input_dim}, {self.output_dim}"
if self.low_rank_dim is not None:
s += f", low_rank_dim={self.low_rank_dim}"
s += ")"
return s
def forward(self, x: torch.Tensor, delta: torch.Tensor | None = None,
cu_seqlens: torch.LongTensor | None = None) -> torch.Tensor:
if delta is None:
delta = token_shift(x, cu_seqlens)
return self.linear(x + delta * self.mu)
class DDLerpLinear(nn.Module):
def __init__(
self,
input_dim: int,
output_dim: int,
low_rank_dim: int | None = None,
):
super().__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.low_rank_dim = low_rank_dim
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
if low_rank_dim is None:
self.linear = nn.Linear(input_dim, output_dim, bias=False)
else:
self.linear = LoRA(input_dim, output_dim, low_rank_dim)
def __repr__(self) -> str:
s = f"{self.__class__.__name__}({self.input_dim}, {self.output_dim}"
if self.low_rank_dim is not None:
s += f", low_rank_dim={self.low_rank_dim}"
s += ")"
return s
def forward(self, x: torch.Tensor, mu: torch.Tensor,
delta: torch.Tensor | None = None,
cu_seqlens: torch.LongTensor | None = None) -> torch.Tensor:
if delta is None:
delta = token_shift(x, cu_seqlens)
return self.linear(x + delta * mu)
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