diff --git a/README.md b/README.md
index 829230c..e4151a6 100644
--- a/README.md
+++ b/README.md
@@ -42,7 +42,7 @@ To install:
 3. Make sure that `ninja` is installed and that it works correctly (e.g. `ninja
 --version` then `echo $?` should return exit code 0). If not (sometimes `ninja
 --version` then `echo $?` returns a nonzero exit code), uninstall then reinstall
-`ninja` (`pip uninstall -y ninja && pip install ninja`). Without `ninja`
+`ninja` (`pip uninstall -y ninja && pip install ninja`). Without `ninja`,
 compiling can take a very long time (2h) since it does not use multiple CPU
 cores. With `ninja` compiling takes 3-5 minutes on a 64-core machine.
 4. Then:
@@ -202,10 +202,10 @@ pytest -q -s tests/test_flash_attn.py
 ```
 ## When you encounter issues
 
-This new release of FlashAttention-2 have been tested on several GPT-style
+This new release of FlashAttention-2 has been tested on several GPT-style
 models, mostly on A100 GPUs.
 
-If you encounter any of bugs, please open a respective GitHub Issue!
+If you encounter bugs, please open a GitHub Issue!
 
 ## Citation
 If you use this codebase, or otherwise found our work valuable, please cite:
diff --git a/flash_attn/flash_attn_interface.py b/flash_attn/flash_attn_interface.py
index c6e4340..19796a0 100644
--- a/flash_attn/flash_attn_interface.py
+++ b/flash_attn/flash_attn_interface.py
@@ -37,12 +37,8 @@ def _get_block_size(device, head_dim, is_dropout, is_causal):
 
 
 def _flash_attn_forward(q, k, v, dropout_p, softmax_scale, causal, return_softmax):
-    if q.stride(-1) != 1:
-        q = q.contiguous()
-    if k.stride(-1) != 1:
-        k = k.contiguous()
-    if v.stride(-1) != 1:
-        v = v.contiguous()
+    maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
+    q, k, v = [maybe_contiguous(x) for x in (q, k, v)]
     out, q, k, v, out_padded, softmax_lse, S_dmask = flash_attn_cuda.fwd(
         q, k, v, None, dropout_p, softmax_scale, causal, return_softmax, None
     )
@@ -51,12 +47,8 @@ def _flash_attn_forward(q, k, v, dropout_p, softmax_scale, causal, return_softma
 
 def _flash_attn_varlen_forward(q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
                                dropout_p, softmax_scale, causal, return_softmax):
-    if q.stride(-1) != 1:
-        q = q.contiguous()
-    if k.stride(-1) != 1:
-        k = k.contiguous()
-    if v.stride(-1) != 1:
-        v = v.contiguous()
+    maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
+    q, k, v = [maybe_contiguous(x) for x in (q, k, v)]
     out, q, k, v, out_padded, softmax_lse, S_dmask = flash_attn_cuda.varlen_fwd(
         q, k, v, None, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p,
         softmax_scale, False, causal, return_softmax, None
@@ -68,6 +60,9 @@ def _flash_attn_varlen_forward(q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q
 
 def _flash_attn_backward(dout, q, k, v, out, softmax_lse, dq, dk, dv,
                          dropout_p, softmax_scale, causal):
+    maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
+    # dq, dk, dv are allocated by us so they should already be contiguous
+    dout, q, k, v, out = [maybe_contiguous(x) for x in (dout, q, k, v, out)]
     dq, dk, dv, softmax_d, = flash_attn_cuda.bwd(
         dout, q, k, v, out, softmax_lse, dq, dk, dv, dropout_p, softmax_scale, causal, None
     )
@@ -77,6 +72,9 @@ def _flash_attn_backward(dout, q, k, v, out, softmax_lse, dq, dk, dv,
 def _flash_attn_varlen_backward(dout, q, k, v, out, softmax_lse, dq, dk, dv,
                                 cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
                                 dropout_p, softmax_scale, causal):
+    maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
+    # dq, dk, dv are allocated by us so they should already be contiguous
+    dout, q, k, v, out = [maybe_contiguous(x) for x in (dout, q, k, v, out)]
     dq, dk, dv, softmax_d, = flash_attn_cuda.varlen_bwd(
         dout, q, k, v, out, softmax_lse, dq, dk, dv, cu_seqlens_q, cu_seqlens_k,
         max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale, False, causal, None