diff --git a/python/Demonstration.ipynb b/python/Demonstration.ipynb
index 6a52fac..ecb864f 100644
--- a/python/Demonstration.ipynb
+++ b/python/Demonstration.ipynb
@@ -24,11 +24,11 @@
"metadata": {},
"outputs": [
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "The autoreload extension is already loaded. To reload it, use:\n",
- " %reload_ext autoreload\n"
+ "/workspaces/CircuitsVis/python/.venv/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
@@ -38,12 +38,17 @@
"%autoreload 2\n",
"\n",
"# Imports\n",
- "import numpy as np\n",
"from circuitsvis.attention import attention_patterns, attention_pattern\n",
"from circuitsvis.activations import text_neuron_activations\n",
"from circuitsvis.examples import hello\n",
+ "from circuitsvis.logits import token_log_probs\n",
"from circuitsvis.tokens import colored_tokens\n",
- "from circuitsvis.topk_tokens import topk_tokens"
+ "from circuitsvis.topk_tokens import topk_tokens\n",
+ "\n",
+ "import numpy as np\n",
+ "import random\n",
+ "import string\n",
+ "import torch"
]
},
{
@@ -63,71 +68,70 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Text Neuron Activations (single sample)"
+ "#### Text Neuron Activations"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
- "\n",
+ "\n",
" "
],
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 10,
+ "execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
@@ -4971,11 +4975,17 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Text Neuron Activations (multiple samples)"
+ "### Attention"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Attention Pattern (single head)"
]
},
{
@@ -4986,56 +4996,56 @@
{
"data": {
"text/html": [
- "\n",
+ "\n",
" "
],
"text/plain": [
- ""
+ ""
]
},
"execution_count": 3,
@@ -9870,29 +9880,16 @@
}
],
"source": [
- "tokens = [['Hi', ' and', ' welcome', ' to', ' the', ' Attention', ' Patterns', ' example'], ['This', ' is', ' another', ' example', ' of', ' colored', ' tokens'], ['And', ' here', ' another', ' example', ' of', ' colored', ' tokens', ' with', ' more', ' words.'], ['This', ' is', ' another', ' example', ' of', ' tokens.']]\n",
- "n_layers = 3\n",
- "n_neurons_per_layer = 4\n",
- "activations = []\n",
- "for sample in tokens:\n",
- " sample_activations = np.random.normal(size=(len(sample), n_layers, n_neurons_per_layer)) * 5\n",
- " activations.append(sample_activations)\n",
- "text_neuron_activations(tokens=tokens, activations=activations)\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Attention"
+ "tokens = ['Hi', ' and', ' welcome', ' to', ' the', ' Attention', ' Patterns', ' example']\n",
+ "attention = np.tril(np.random.normal(loc=0.3, scale=0.2, size=(8,8)))\n",
+ "attention_pattern(tokens=tokens, attention=attention)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Attention Pattern (single head)"
+ "#### Attention Patterns"
]
},
{
@@ -9903,56 +9900,56 @@
{
"data": {
"text/html": [
- "\n",
+ "\n",
" "
],
"text/plain": [
- ""
+ ""
]
},
"execution_count": 4,
@@ -14788,15 +14785,24 @@
],
"source": [
"tokens = ['Hi', ' and', ' welcome', ' to', ' the', ' Attention', ' Patterns', ' example']\n",
- "attention = np.tril(np.random.normal(loc=0.3, scale=0.2, size=(8,8)))\n",
- "attention_pattern(tokens=tokens, attention=attention)"
+ "attention = np.tril(np.random.normal(loc=0.3, scale=0.2, size=(12,8,8)))\n",
+ "attention_patterns(tokens=tokens, attention=attention)"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Attention Patterns"
+ "### Logits"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Token Log Probs"
]
},
{
@@ -14807,56 +14813,56 @@
{
"data": {
"text/html": [
- "\n",
+ "\n",
" "
],
"text/plain": [
- ""
+ ""
]
},
"execution_count": 5,
@@ -19691,9 +19697,11 @@
}
],
"source": [
- "tokens = ['Hi', ' and', ' welcome', ' to', ' the', ' Attention', ' Patterns', ' example']\n",
- "attention = np.tril(np.random.normal(loc=0.3, scale=0.2, size=(12,8,8)))\n",
- "attention_patterns(tokens=tokens, attention=attention)"
+ "tokens = ['Hi', ' and', ' welcome', ' to', ' the', ' example']\n",
+ "token_indicies = torch.randint(0, 50000, (len(tokens),))\n",
+ "log_probs = torch.rand((len(tokens), 50000), dtype=torch.float32)\n",
+ "to_string = lambda x: ''.join(random.choice(string.ascii_lowercase) for i in range(10))\n",
+ "token_log_probs(token_indices=token_indicies, log_probs=log_probs, to_string=to_string)"
]
},
{
@@ -19718,56 +19726,56 @@
{
"data": {
"text/html": [
- "\n",
+ "\n",
" "
],
"text/plain": [
- ""
+ ""
]
},
"execution_count": 6,
@@ -24616,62 +24624,62 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
- "\n",
+ "\n",
"