just tempt some student into trying laser attention

README.md

@@ -514,3 +514,12 @@ docker run -v .:/data --gpus all -it af3
     url     = {https://api.semanticscholar.org/CorpusID:273532030}
 }
 ```
+
+```bibtex
+@inproceedings{Duvvuri2024LASERAW,
+    title   = {LASER: Attention with Exponential Transformation},
+    author  = {Sai Surya Duvvuri and Inderjit S. Dhillon},
+    year    = {2024},
+    url     = {https://api.semanticscholar.org/CorpusID:273849947}
+}
+```

alphafold3_pytorch/attention.py

@@ -40,6 +40,9 @@ def pack_one(t, pattern):
 def unpack_one(t, ps, pattern):
     return unpack(t, ps, pattern)[0]
 
+def log(t, eps = 1e-20):
+    return t.clamp(min = eps).log()
+
 def softclamp(t, value):
     return (t / value).tanh() * value
 
@@ -181,6 +184,7 @@ def __init__(
         query_bias = True,
         window_size = None,
         num_memory_kv: int = 0,
+        laser = False,
         enable_attn_softclamp = False,
         attn_softclamp_value = 50.,
         softmax_full_precision = False
@@ -202,6 +206,7 @@ def __init__(
         dim_inner = dim_head * heads
 
         self.attend = Attend(
+            laser = laser,
             dropout = dropout,
             window_size = window_size,
             enable_attn_softclamp = enable_attn_softclamp,
@@ -299,6 +304,7 @@ class Attend(Module):
     def __init__(
         self,
         dropout = 0.,
+        laser = False,
         window_size = None,
         scale: float | None = None,
         enable_attn_softclamp = False,
@@ -327,6 +333,10 @@ def __init__(
 
         self.attn_dropout = nn.Dropout(dropout)
 
+        # laser attention
+
+        self.laser = laser
+
         # softclamp attention logits
         # being adopted by a number of recent llms (gemma, grok)
 
@@ -447,10 +457,21 @@ def local_attn(
 
         attn = sim.softmax(dim = -1)
 
+        # maybe laser
+
+        if self.laser:
+            v_max = v.amax(dim = -2, keepdim = True)
+            v = (v - v_max).exp()
+
         # aggregate
 
         out = einsum(attn, v, "... i j, ... j d -> ... i d")
 
+        # maybe laser
+
+        if self.laser:
+            out = log(out) + v_max
+
         # un-window the output
 
         out = rearrange(out, "b h n w d -> b h (n w) d")
@@ -546,8 +567,19 @@ def forward(
 
         attn = self.attn_dropout(attn)
 
+        # maybe laser
+
+        if self.laser:
+            v_max = v.amax(dim = -2, keepdim = True)
+            v = (v - v_max).exp()
+
         # aggregate values
 
         out = einsum(attn, v, "b h i j, b h j d -> b h i d")
 
+        # maybe laser
+
+        if self.laser:
+            out = log(out) + v_max
+
         return out

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "alphafold3-pytorch"
-version = "0.6.8"
+version = "0.6.10"
 description = "Alphafold 3 - Pytorch"
 authors = [
     { name = "Phil Wang", email = "[email protected]" },