flash attn

README.md

@@ -40,6 +40,12 @@ pip install open-mythos
 #uv pip install open-mythos
 ```
 
+To enable Flash Attention 2 in `GQAttention` (requires CUDA and build tools):
+
+```bash
+pip install open-mythos[flash]
+```
+
 ## Usage
 
 ```python

open_mythos/main.py

@@ -5,6 +5,13 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+try:
+    from flash_attn import flash_attn_func
+
+    _HAS_FLASH_ATTN = True
+except ImportError:
+    _HAS_FLASH_ATTN = False
+
 
 @dataclass
 class MythosConfig:
@@ -169,12 +176,17 @@ def apply_rope(x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
 
 class GQAttention(nn.Module):
     """
-    Grouped Query Attention (Ainslie et al., 2023).
+    Grouped Query Attention (Ainslie et al., 2023) with Flash Attention 2 (Dao et al., 2023).
 
     Uses fewer KV heads than Q heads (n_kv_heads < n_heads). Each KV head is
     shared across n_heads // n_kv_heads query heads, reducing the KV cache size
     by that factor while keeping full query expressiveness.
 
+    When flash-attn is installed, uses flash_attn_func which handles GQA natively
+    (no KV head expansion needed) and is IO-bound-optimal. Inputs are cast to
+    bfloat16 for flash_attn and restored to the original dtype afterward.
+    Falls back to manual scaled dot-product attention when flash-attn is absent.
+
     RoPE is applied to both Q and K. K and V are stored in kv_cache after
     RoPE application so that cached values are already positionally encoded and
     do not need to be re-rotated on retrieval.
@@ -195,7 +207,7 @@ class GQAttention(nn.Module):
         self.wk = nn.Linear(cfg.dim, cfg.n_kv_heads * self.head_dim, bias=False)
         self.wv = nn.Linear(cfg.dim, cfg.n_kv_heads * self.head_dim, bias=False)
         self.wo = nn.Linear(cfg.n_heads * self.head_dim, cfg.dim, bias=False)
-        self.attn_drop = nn.Dropout(cfg.dropout)
+        self.dropout_p = cfg.dropout
 
     def forward(
         self,
@@ -230,21 +242,35 @@ class GQAttention(nn.Module):
                 v = torch.cat([kv_cache[cache_key]["v"], v], dim=1)
             kv_cache[cache_key] = {"k": k.detach(), "v": v.detach()}
 
-        # expand KV to match Q heads
-        k = k.repeat_interleave(self.groups, dim=2)
-        v = v.repeat_interleave(self.groups, dim=2)
+        if _HAS_FLASH_ATTN:
+            # flash_attn_func expects (B, T, H, head_dim) — GQA is handled natively
+            # (n_kv_heads < n_heads is supported without repeat_interleave).
+            # causal=True when mask is present (full-sequence prefill/training);
+            # causal=False for single-token decode where T=1 and mask is None.
+            orig_dtype = q.dtype
+            q = q.to(torch.bfloat16)
+            k = k.to(torch.bfloat16)
+            v = v.to(torch.bfloat16)
+            dropout_p = self.dropout_p if self.training else 0.0
+            out = flash_attn_func(q, k, v, dropout_p=dropout_p, causal=(mask is not None))
+            out = out.to(orig_dtype).contiguous().view(B, T, -1)
+        else:
+            # Fallback: manual scaled dot-product with explicit KV head expansion.
+            k = k.repeat_interleave(self.groups, dim=2)
+            v = v.repeat_interleave(self.groups, dim=2)
+            q = q.transpose(1, 2)  # (B, H, T, head_dim)
+            k = k.transpose(1, 2)
+            v = v.transpose(1, 2)
+            scale = self.head_dim**-0.5
+            attn = torch.matmul(q, k.transpose(-2, -1)) * scale
+            if mask is not None:
+                attn = attn + mask
+            attn = F.dropout(
+                F.softmax(attn, dim=-1), p=self.dropout_p, training=self.training
+            )
+            out = torch.matmul(attn, v)
+            out = out.transpose(1, 2).contiguous().view(B, T, -1)
 
-        q = q.transpose(1, 2)  # (B, H, T, head_dim)
-        k = k.transpose(1, 2)
-        v = v.transpose(1, 2)
-
-        scale = self.head_dim**-0.5
-        attn = torch.matmul(q, k.transpose(-2, -1)) * scale
-        if mask is not None:
-            attn = attn + mask
-        attn = self.attn_drop(F.softmax(attn, dim=-1))
-        out = torch.matmul(attn, v)
-        out = out.transpose(1, 2).contiguous().view(B, T, -1)
         return self.wo(out)
 
 

pyproject.toml

@@ -42,6 +42,13 @@ torch = "2.11.0"
 transformers = ">=4.40.0"
 datasets = ">=2.18.0"
 
+[tool.poetry.extras]
+flash = ["flash-attn"]
+
+[tool.poetry.dependencies.flash-attn]
+version = ">=2.8.3"
+optional = true
+
 
 [tool.poetry.group.lint.dependencies]
 black = ">=23.1,<27.0"

requirements.txt

@@ -2,3 +2,6 @@ torch>=2.1.0
 transformers>=4.40.0
 datasets>=2.18.0
 pytest>=7.0.0
+
+# optional — enables Flash Attention 2 in GQAttention (requires CUDA + build tools)
+# flash-attn>=2.8.3