sync https://github.com/JL-er/RWKV-PEFT

finetune/lora/v6/fla/layers/linear_attn.py

@@ -0,0 +1,156 @@
+# -*- 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)