Use narrow instead of dynamic_slice

1 files changed, 19 insertions, 15 deletions

diff --git a/modules/sub_quadratic_attention.py b/modules/sub_quadratic_attention.py
index b11dc1c7..95924d24 100644
--- a/modules/sub_quadratic_attention.py
+++ b/modules/sub_quadratic_attention.py
@@ -5,6 +5,7 @@
 # credit:
 #   Amin Rezaei (original author)
 #   Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks)
+#   brkirch (modified to use torch.narrow instead of dynamic_slice implementation)
 # implementation of:
 #   Self-attention Does Not Need O(n2) Memory":
 #   https://arxiv.org/abs/2112.05682v2
@@ -16,13 +17,13 @@ from torch.utils.checkpoint import checkpoint
 import math
 from typing import Optional, NamedTuple, Protocol, List
 
-def dynamic_slice(
-    x: Tensor,
-    starts: List[int],
-    sizes: List[int],
+def narrow_trunc(
+    input: Tensor,
+    dim: int,
+    start: int,
+    length: int
 ) -> Tensor:
-    slicing = [slice(start, start + size) for start, size in zip(starts, sizes)]
-    return x[slicing]
+    return torch.narrow(input, dim, start, length if input.shape[dim] >= start + length else input.shape[dim] - start)
 
 class AttnChunk(NamedTuple):
     exp_values: Tensor
@@ -76,15 +77,17 @@ def _query_chunk_attention(
     _, _, v_channels_per_head = value.shape
 
     def chunk_scanner(chunk_idx: int) -> AttnChunk:
-        key_chunk = dynamic_slice(
+        key_chunk = narrow_trunc(
             key,
-            (0, chunk_idx, 0),
-            (batch_x_heads, kv_chunk_size, k_channels_per_head)
+            1,
+            chunk_idx,
+            kv_chunk_size
         )
-        value_chunk = dynamic_slice(
+        value_chunk = narrow_trunc(
             value,
-            (0, chunk_idx, 0),
-            (batch_x_heads, kv_chunk_size, v_channels_per_head)
+            1,
+            chunk_idx,
+            kv_chunk_size
         )
         return summarize_chunk(query, key_chunk, value_chunk)
 
@@ -161,10 +164,11 @@ def efficient_dot_product_attention(
         kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min)
 
     def get_query_chunk(chunk_idx: int) -> Tensor:
-        return dynamic_slice(
+        return narrow_trunc(
             query,
-            (0, chunk_idx, 0),
-            (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head)
+            1,
+            chunk_idx,
+            min(query_chunk_size, q_tokens)
         )
     
     summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale)