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commit 3782b521d9d394c867f7b6524c7afb2ed342e995
Author: Shyam Pather <shyam.pather@gmail.com>
Date:   Thu Sep 21 17:05:20 2023 -0700

    Fix bugs in plotting positional encodings and plot them for b0h0

diff --git a/nbs/transformer-analysis.ipynb b/nbs/transformer-analysis.ipynb
index de414fa..9fe7ef3 100644
--- a/nbs/transformer-analysis.ipynb
    b/nbs/transformer-analysis.ipynb
@@ -6207,9  6207,10 @@
     "    pos_embeddings = m.position_embedding_table(torch.arange(s_len)).detach()\n",
     "\n",
     "    # Run through the block's layernorm\n",
     "    block = m.blocks[block_idx]\n",
     "    pos_embeddings = block.ln1(pos_embeddings)\n",
     "\n",
-    "    head = m.blocks[block_idx].sa.heads[head_idx] \n",
     "    head = block.sa.heads[head_idx] \n",
     "\n",
     "    # Hate that this duplicates the head logic yet again.\n",
     "    q = head.query(pos_embeddings).detach()\n",
@@ -6224,8  6225,8 @@
     "    ax.set_title(\"Attention weights for all positional embeddings\", fontsize=26)\n",
     "    im = ax.imshow(wei, cmap='viridis', interpolation='none')\n",
     "\n",
-    "    for i in range(T):\n",
-    "        for j in range(T):\n",
     "    for i in range(s_len):\n",
     "        for j in range(s_len):\n",
     "            # Display gray boxes over the upper triangle whose values won't matter\n",
     "            if i < j:\n",
     "                rect = patches.Rectangle((j-0.5, i-0.5), 1, 1, linewidth=1, edgecolor='gray', facecolor='gray', alpha=0.8)\n",
@@ -7098,6  7099,34 @@
     "plot_wei([str(i) for i in range(s_len)], mean_wei)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
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7UN77xjdtfEK6ptbVVX//619XU1KTCwkLNnTtXe/fu1ec//3mvSwNGlYsXL2r16tXq6OhQaWmpVq5cqf3796u0tNTr0jBo8eLF2r17tzZu3Kgf//jHqqqq0rZt27RmzRqvSwOG4XOWAQAAAAAwcM0yAAAAAAAGwjIAAAAAAAbCMgAAAAAABsIyAAAAAAAGwjIAAAAAAAbCMgAAAAAABsIyAAAAAAAGwjIAAAAAAAbCMgAAAAAABsIyAAAAAAAGwjIAAAAAAAbCMgAAAAAAhv8PeOu6rbj7vSIAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1200x1200 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# Plot the positional embeddings alone\n",
     "plot_pos_embedding_attention_weights(s_len, block_idx=block_idx, head_idx=head_idx)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Looks a lot like the mean weights! "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,