update_name

graph_dit/models/layers.py

@@ -0,0 +1,114 @@
+from torch.jit import Final
+import torch.nn.functional as F
+from itertools import repeat
+import collections.abc
+
+import torch
+import torch.nn as nn
+
+class Attention(nn.Module):
+    fast_attn: Final[bool]
+
+    def __init__(
+            self,
+            dim,
+            num_heads=8,
+            qkv_bias=False,
+            qk_norm=False,
+            attn_drop=0,
+            proj_drop=0,
+            norm_layer=nn.LayerNorm,
+    ):
+        super().__init__()
+        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+
+        self.scale = self.head_dim ** -0.5
+        self.fast_attn = hasattr(torch.nn.functional, 'scaled_dot_product_attention')  # FIXME
+        assert self.fast_attn, "scaled_dot_product_attention Not implemented"
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+
+        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def dot_product_attention(self, q, k, v):
+        q = q * self.scale
+        attn = q @ k.transpose(-2, -1)
+        attn_sfmx = attn.softmax(dim=-1)
+        attn_sfmx = self.attn_drop(attn_sfmx)
+        x = attn_sfmx @ v
+        return x, attn
+    
+    def forward(self, x, node_mask):
+        B, N, D = x.shape
+        
+        # B, head, N, head_dim
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0) # B, head, N, head_dim
+        q, k = self.q_norm(q), self.k_norm(k)
+        
+        attn_mask = (node_mask[:, None, :, None] & node_mask[:, None, None, :]).expand(-1, self.num_heads, N, N)
+        attn_mask[attn_mask.sum(-1) == 0] = True
+
+        x = F.scaled_dot_product_attention(
+            q, k, v,
+            dropout_p=self.attn_drop.p,
+            attn_mask=attn_mask,
+        )
+
+        x = x.transpose(1, 2).reshape(B, N, -1)
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+
+        return x
+
+
+class Mlp(nn.Module):
+    def __init__(
+            self,
+            in_features,
+            hidden_features=None,
+            out_features=None,
+            act_layer=nn.GELU,
+            bias=True,
+            drop=0.,
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        bias = to_2tuple(bias)
+        drop_probs = to_2tuple(drop)
+        linear_layer = nn.Linear
+
+        self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
+        self.act = act_layer()
+        self.drop1 = nn.Dropout(drop_probs[0])
+        self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
+        self.drop2 = nn.Dropout(drop_probs[1])
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop1(x)
+        x = self.fc2(x)
+        x = self.drop2(x)
+        return x
+
+# From PyTorch internals
+def _ntuple(n):
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
+            return tuple(x)
+        return tuple(repeat(x, n))
+    return parse
+
+to_2tuple = _ntuple(2)
+
+