Merge pull request #2388 from laclouis5/fix-mqa-v2

Fix MQA V2

tests/test_layers.py

@@ -2,7 +2,7 @@
 import torch
 import torch.nn as nn
 
-from timm.layers import create_act_layer, set_layer_config, get_act_layer, get_act_fn, Attention2d
+from timm.layers import create_act_layer, set_layer_config, get_act_layer, get_act_fn, Attention2d, MultiQueryAttentionV2
 
 import importlib
 import os
@@ -121,6 +121,23 @@ def test_get_act_fn_none():
     assert get_act_fn('') is None
 
 
+@pytest.mark.parametrize("dim", [128])
+@pytest.mark.parametrize("dim_out", [128, 256])
+@pytest.mark.parametrize("use_m", [True, False])
+def test_mqa_v2(dim, dim_out, use_m):
+    mqa = MultiQueryAttentionV2(dim, dim_out)
+
+    x = torch.randn(1, dim, 32, 48)
+    if use_m:
+        m = torch.randn(1, dim, 16, 24)
+    else:
+        m = None
+
+    y = mqa(x, m=m)
+
+    assert (y.shape) == (1, dim_out, 32, 48)
+
+
 @pytest.mark.parametrize("bias", [True, False])
 @pytest.mark.parametrize("expand_first", [True, False])
 @pytest.mark.parametrize("head_first", [True, False])
@@ -141,6 +158,3 @@ def test_attn2d(bias, expand_first, head_first, attn_mask):
     o2 = attn(x, mask)
 
     assert torch.allclose(o1, o2, atol=1e-5), f"{torch.abs(o1 - o2).max()}"
-
-
-

timm/layers/attention2d.py

@@ -59,24 +59,24 @@ def _reshape_input(self, t):
 
     def forward(self, x, m: Optional[torch.Tensor] = None):
         """Run layer computation."""
-        s = x.shape
-        m = m or x
+        b, _, h, w = x.shape
+        m = m if m is not None else x
 
         reshaped_x = self._reshape_input(x)
         reshaped_m = self._reshape_input(m)
 
         q = torch.einsum('bnd,hkd->bnhk', reshaped_x, self.query_proj)
         k = torch.einsum('bmd,dk->bmk', reshaped_m, self.key_proj)
 
-        attn = torch.einsum('bnhk,bmk->bnhm', q, k)
+        attn = torch.einsum('bnhk,bmk->bnhm', q, k) * self.scale
         attn = attn.softmax(dim=-1)
         attn = self.attn_drop(attn)
 
         v = torch.einsum('bmd,dv->bmv', reshaped_m, self.value_proj)
         o = torch.einsum('bnhm,bmv->bnhv', attn, v)
-        result = torch.einsum('bnhv,dhv->bnd', o, self.out_proj)
+        result = torch.einsum('bnhv,dhv->bdn', o, self.out_proj)
         result = self.proj_drop(result)
-        return result.reshape(s)
+        return result.reshape(b, -1, h, w)
 
 
 class MultiQueryAttention2d(nn.Module):