RWKV-Runner/finetune/lora/v6/fla/layers/linear_attn.py

# -*- coding: utf-8 -*-

import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange

from fla.modules import RMSNorm
from fla.modules.feature_map import (DPFPFeatureMap, HadamardFeatureMap,
                                     HedgehogFeatureMap, T2RFeatureMap)
from fla.ops.linear_attn import (chunk_linear_attn, fused_chunk_linear_attn,
                                 fused_recurrent_linear_attn)


class LinearAttention(nn.Module):
    def __init__(
        self,
        hidden_size: str = 1024,
        expand_k: int = 1.0,
        expand_v: int = 1.0,
        num_heads: int = 8,
        mode: str = 'chunk',
        feature_map: str = 'elementwise_product',
        tie_feature_map_qk: bool = False,
        output_norm: str = 'rmsnorm',
        norm_q: bool = False,
        norm_k: bool = False,
        # standard linear attention normalization
        do_feature_map_norm: bool = False,
        elementwise_affine: bool = True,
        norm_eps: float = 1e-5,
        **kwargs,
    ):
        super().__init__()
        assert feature_map in ['elu', 'relu', 'hedgehog', 't2r', 'dpfp',
                               'identity', 'elementwise_product'], f"Not supported feature map `{feature_map}`."

        assert output_norm in ['rmsnorm', 'identity'], f"Not supported output norm `{output_norm}`."

        self.hidden_size
        self.mode = mode
        self.key_dim = int(hidden_size * expand_k)
        self.value_dim = int(hidden_size * expand_v)
        self.num_heads = num_heads

        assert mode in ['chunk', 'fused_chunk', 'fused_recurrent'], f"Not suppoerted 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_qk_dim = self.key_dim // num_heads
        self.head_v_dim = self.value_dim // num_heads

        if feature_map == 'hedgehog':
            if tie_feature_map_qk:
                self.feature_map_q = self.feature_map_k = HedgehogFeatureMap(head_dim=self.head_qk_dim)
            else:
                self.feature_map_q = HedgehogFeatureMap(head_dim=self.head_qk_dim)
                self.feature_map_k = HedgehogFeatureMap(head_dim=self.head_qk_dim)

        elif feature_map == 't2r':
            if tie_feature_map_qk:
                self.feature_map_q = self.feature_map_k = T2RFeatureMap(head_dim=self.head_qk_dim)
            else:
                self.feature_map_q = T2RFeatureMap(head_dim=self.head_qk_dim)
                self.feature_map_k = T2RFeatureMap(head_dim=self.head_qk_dim)

        elif feature_map == 'elementwise_product':
            if tie_feature_map_qk:
                self.feature_map_q = self.feature_map_k = HadamardFeatureMap(head_dim=self.head_qk_dim)
            else:
                self.feature_map_q = HadamardFeatureMap(head_dim=self.head_qk_dim)
                self.feature_map_k = HadamardFeatureMap(head_dim=self.head_qk_dim)

        elif feature_map == 'dpfp':
            self.feature_map_q = DPFPFeatureMap(head_dim=self.head_qk_dim)
            self.feature_map_k = DPFPFeatureMap(head_dim=self.head_qk_dim)

        elif feature_map == 'elu':
            def elu(x):
                return F.elu(x) + 1
            self.feature_map_q = elu
            self.feature_map_k = elu

        elif feature_map == 'relu':
            self.feature_map_q = nn.ReLU()
            self.feature_map_k = nn.ReLU()

        elif feature_map == 'identity':
            self.feature_map_q = nn.Identity()
            self.feature_map_k = nn.Identity()
        else:
            raise NotImplementedError

        self.do_feature_map_norm = do_feature_map_norm
        if output_norm == 'rmsnorm':
            self.norm = RMSNorm(self.head_v_dim, elementwise_affine, norm_eps)
        elif output_norm == 'identity':
            self.norm = nn.Identity()
        else:
            raise NotImplementedError

        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.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False)

        self.norm_q = norm_q
        self.norm_k = norm_k

        self.apply(self._initialize_weights)

    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)
        module._is_hf_initialized = True

    def forward(self, x):
        mode = self.mode
        q = rearrange(self.q_proj(x), 'b n (h d) -> b h n d', h=self.num_heads)
        k = rearrange(self.k_proj(x), 'b n (h d) -> b h n d', h=self.num_heads)
        v = rearrange(self.v_proj(x), 'b n (h d) -> b h n d', h=self.num_heads)
        q = self.feature_map_q(q)
        k = self.feature_map_k(k)
        if self.norm_q:
            q = q / (q.sum(-1, keepdim=True) + 1e-4)
        if self.norm_k:
            k = k / (k.sum(-1, keepdim=True) + 1e-4)

        if mode == 'chunk':
            o = chunk_linear_attn(q, k, v, normalize=self.do_feature_map_norm)
        elif mode == 'fused_chunk':
            o = fused_chunk_linear_attn(q, k, v, normalize=self.do_feature_map_norm)
        elif mode == 'fused_recurrent':
            o = fused_recurrent_linear_attn(q, k, v, normalize=self.do_feature_map_norm)
        else:
            raise NotImplementedError
        o = self.norm(o)
        o = rearrange(o, 'b h n d -> b n (h d)')
        o = self.o_proj(o)
        return o


if __name__ == '__main__':
    import torch
    batch = 4
    seq_len = 1024
    hidden_size = 1024
    x = torch.randn(batch, seq_len, hidden_size).to(torch.bfloat16).cuda().requires_grad_(True)
    model = LinearAttention(hidden_size, feature_map='dplp').to(torch.bfloat16).cuda()
    y = model(x)
    print(y.shape)
    y.sum().backward()
    print(x.grad.shape)