Fix minor typo in example (#57)

examples/flex_attn.ipynb

@@ -267,7 +267,7 @@
    "outputs": [],
    "source": [
     "def checkerboard(score, batch, head, token_q, token_kv):\n",
-    "    score = torch.where(torch.abs(token_kv - token_q) % 1 == 0, score * 0.5, score)\n",
+    "    score = torch.where(torch.abs(token_kv - token_q) % 2 == 1, score * 0.5, score)\n",
     "    score = torch.where(torch.abs(token_kv - token_q) % 2 == 0, score * 2.0, score)\n",
     "    return score\n",
     "\n",
@@ -316,7 +316,7 @@
     "The implementation using a score_mod:\n",
     "```Python\n",
     "def causal_bias(score, b, h, q_idx, kv_idx):\n",
-    "    return torch.where(q >= kv_idx, score, -float(\"inf\"))\n",
+    "    return torch.where(q_idx >= kv_idx, score, -float(\"inf\"))\n",
     "```\n",
     "\n",
     "Whenever you are writing a score_mod function that passes through the original score for some elements and sets others to -inf, you should likely be using a mask mod.\n",
@@ -326,7 +326,7 @@
     "```Python\n",
     "The implementation using a mask_mod:\n",
     "def casual_mask(b,h,q_idx, kv_idx):\n",
-    "    return q >= kv_idx\n",
+    "    return q_idx >= kv_idx\n",
     "```\n",
     "As you can see they look very similar, both return scalar tensors. The key differences\n",
     "1. mask_mods return boolean tensors where `True` indicates this score should be calculated, and `False` indicates we that we want to mask out this score\n",