fix issue with seq len < world size in tree attn decoding after new u…

…pdate, also start using tensor typing

ring_attention_pytorch/tensor_typing.py

@@ -0,0 +1,26 @@
+from torch import Tensor
+
+from jaxtyping import (
+    Float,
+    Int,
+    Bool
+)
+
+# jaxtyping is a misnomer, works for pytorch
+
+class TorchTyping:
+    def __init__(self, abstract_dtype):
+        self.abstract_dtype = abstract_dtype
+
+    def __getitem__(self, shapes: str):
+        return self.abstract_dtype[Tensor, shapes]
+
+Float = TorchTyping(Float)
+Int   = TorchTyping(Int)
+Bool  = TorchTyping(Bool)
+
+__all__ = [
+    Float,
+    Int,
+    Bool
+]

ring_attention_pytorch/tree_attn_decoding.py

@@ -6,6 +6,8 @@
 
 from ring_attention_pytorch.distributed import get_rank, get_world_size
 
+from ring_attention_pytorch.tensor_typing import Float
+
 # functions
 
 def exists(v):
@@ -18,21 +20,17 @@ def default(v, d):
 
 @torch.no_grad()
 def tree_attn_decode(
-    q: Tensor,
-    k: Tensor | None = None,
-    v: Tensor | None = None,
+    q: Float['b h 1 d'],
+    k: Float['b h n d'] | None = None,
+    v: Float['b h n dv'] | None = None,
     eps = 1e-8,
-    shard_kv_seq = False,
+    shard_kv_seq = True,
     use_triton = None
-):
-    dtype = q.dtype
+) -> Float['b h 1 dv']:
 
-    assert not (exists(k) ^ exists(v)), 'keys and values are either both None, or both present'
+    q_prec_dims, dtype = q.shape[:-1], q.dtype
 
-    if exists(k):
-        assert k.shape[:-1] == v.shape[:-1]
-        assert q.shape[-2:] == (1, k.shape[-1])
-        assert q.shape[:-2] == k.shape[:-2]
+    assert not (exists(k) ^ exists(v)), 'keys and values are either both None, or both present'
 
     """
     Algorithm 3 proposed in Tree Attention
@@ -43,6 +41,7 @@ def tree_attn_decode(
 
     if shard_kv_seq:
         assert exists(k), 'keys and values must be passed if not already sharded across sequence'
+        dim_v = v.shape[-1]
 
         rank, world_size = get_rank(), get_world_size()
         k = k.chunk(world_size, dim = -2)
@@ -68,6 +67,7 @@ def tree_attn_decode(
                 remove_padding = True
             )
 
+            lse = rearrange(lse, '... -> ... 1')
         else:
             scale = q.shape[-1] ** -0.5
             sim = einsum('... i d, ... j d -> ... i j', q, k) * scale
@@ -79,8 +79,8 @@ def tree_attn_decode(
     else:
         # handle edge case where seq length < world size
 
-        local_out = q.new_zeros((*q.shape[:-1], v.shape[-1]), dtype = torch.float32)
-        lse = torch.full_like(den, -torch.finfo(torch.float32).max)
+        local_out = q.new_zeros((*q_prec_dims, dim_v), dtype = torch.float32)
+        lse = torch.full((*q_prec_dims, 1), -torch.finfo(torch.float32).max, device = q.device, dtype = torch.float32)
 
     # first get max(lse) through an all reduce
 

ring_attention_pytorch/triton_flash_attn.py

@@ -322,10 +322,10 @@ def flash_attn_forward(
     q, k, v = [x if is_contiguous(x) else x.contiguous() for x in (q, k, v)]
 
     if head_first_dim:
-        q, k, v = tuple(rearrange(t, 'b n h d -> b h n d') for t in (q, k, v))
+        q, k, v = tuple(rearrange(t, 'b h n d -> b n h d') for t in (q, k, v))
 
         if exists(o):
-            o = rearrange(o, 'b n h d -> b h n d')
+            o = rearrange(o, 'b h n d -> b n h d')
 
     batch, seqlen_q, nheads, d = q.shape
     _, seqlen_k, _, _ = k.shape
@@ -421,7 +421,7 @@ def flash_attn_forward(
     )
 
     if head_first_dim:
-        o = rearrange(o, 'b h n d -> b n h d')
+        o = rearrange(o, 'b n h d -> b h n d')
 
     if remove_padding:
         m = m[..., :seqlen_q]

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'ring-attention-pytorch',
   packages = find_packages(exclude=[]),
-  version = '0.5.12',
+  version = '0.5.16',
   license='MIT',
   description = 'Ring Attention - Pytorch',
   author = 'Phil Wang',
@@ -18,7 +18,8 @@
   install_requires=[
     'beartype',
     'einops>=0.8.0',
-    'torch>=2.0'
+    'jaxtyping',
+    'torch>=2.0',
   ],
   classifiers=[
     'Development Status :: 4 - Beta',