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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "99FBiGH7bsfn"
},
"source": [
"# Compiling \u0026 Visualizing Tracr Models\n",
"\n",
"This notebook demonstrates how to compile a tracr model and provides some tools visualize the model's residual stream or layer outputs for a given input sequence."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qm-PM1PEawCx"
},
"outputs": [],
"source": [
"#@title Imports\n",
"import jax\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# The default of float16 can lead to discrepancies between outputs of\n",
"# the compiled model and the RASP program.\n",
"jax.config.update('jax_default_matmul_precision', 'float32')\n",
"\n",
"from tracr.compiler import compiling\n",
"from tracr.compiler import lib\n",
"from tracr.rasp import rasp"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HtOAc_yWawFR"
},
"outputs": [],
"source": [
"#@title Plotting functions\n",
"def tidy_label(label, value_width=5):\n",
" if ':' in label:\n",
" label, value = label.split(':')\n",
" else:\n",
" value = ''\n",
" return label + f\":{value:\u003e{value_width}}\"\n",
"\n",
"\n",
"def add_residual_ticks(model, value_width=5, x=False, y=True):\n",
" if y:\n",
" plt.yticks(\n",
" np.arange(len(model.residual_labels))+0.5, \n",
" [tidy_label(l, value_width=value_width)\n",
" for l in model.residual_labels], \n",
" family='monospace',\n",
" fontsize=20,\n",
" )\n",
" if x:\n",
" plt.xticks(\n",
" np.arange(len(model.residual_labels))+0.5, \n",
" [tidy_label(l, value_width=value_width)\n",
" for l in model.residual_labels], \n",
" family='monospace',\n",
" rotation=90,\n",
" fontsize=20,\n",
" )\n",
"\n",
"\n",
"def plot_computation_trace(model,\n",
" input_labels,\n",
" residuals_or_outputs,\n",
" add_input_layer=False,\n",
" figsize=(12, 9)):\n",
" fig, axes = plt.subplots(nrows=1, ncols=len(residuals_or_outputs), figsize=figsize, sharey=True)\n",
" value_width = max(map(len, map(str, input_labels))) + 1\n",
"\n",
" for i, (layer, ax) in enumerate(zip(residuals_or_outputs, axes)):\n",
" plt.sca(ax)\n",
" plt.pcolormesh(layer[0].T, vmin=0, vmax=1)\n",
" if i == 0:\n",
" add_residual_ticks(model, value_width=value_width)\n",
" plt.xticks(\n",
" np.arange(len(input_labels))+0.5,\n",
" input_labels,\n",
" rotation=90,\n",
" fontsize=20,\n",
" )\n",
" if add_input_layer and i == 0:\n",
" title = 'Input'\n",
" else:\n",
" layer_no = i - 1 if add_input_layer else i\n",
" layer_type = 'Attn' if layer_no % 2 == 0 else 'MLP'\n",
" title = f'{layer_type} {layer_no // 2 + 1}'\n",
" plt.title(title, fontsize=20)\n",
"\n",
"\n",
"def plot_residuals_and_input(model, inputs, figsize=(12, 9)):\n",
" \"\"\"Applies model to inputs, and plots the residual stream at each layer.\"\"\"\n",
" model_out = assembled_model.apply(inputs)\n",
" residuals = np.concatenate([model_out.input_embeddings[None, ...],\n",
" model_out.residuals], axis=0)\n",
" plot_computation_trace(\n",
" model=model,\n",
" input_labels=inputs,\n",
" residuals_or_outputs=residuals,\n",
" add_input_layer=True,\n",
" figsize=figsize)\n",
"\n",
"\n",
"def plot_layer_outputs(model, inputs, figsize=(12, 9)):\n",
" \"\"\"Applies model to inputs, and plots the outputs of each layer.\"\"\"\n",
" model_out = assembled_model.apply(inputs)\n",
" plot_computation_trace(\n",
" model=model,\n",
" input_labels=inputs,\n",
" residuals_or_outputs=model_out.layer_outputs,\n",
" add_input_layer=False,\n",
" figsize=figsize)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "8hV0nv_ISmhM"
},
"outputs": [],
"source": [
"#@title Define RASP programs\n",
"def get_program(program_name, max_seq_len):\n",
" \"\"\"Returns RASP program and corresponding token vocabulary.\"\"\"\n",
" if program_name == \"length\":\n",
" vocab = {\"a\", \"b\", \"c\", \"d\"}\n",
" program = lib.make_length()\n",
" elif program_name == \"frac_prevs\":\n",
" vocab = {\"a\", \"b\", \"c\", \"x\"}\n",
" program = lib.make_frac_prevs((rasp.tokens == \"x\").named(\"is_x\"))\n",
" elif program_name == \"dyck-2\":\n",
" vocab = {\"(\", \")\", \"{\", \"}\"}\n",
" program = lib.make_shuffle_dyck(pairs=[\"()\", \"{}\"])\n",
" elif program_name == \"dyck-3\":\n",
" vocab = {\"(\", \")\", \"{\", \"}\", \"[\", \"]\"}\n",
" program = lib.make_shuffle_dyck(pairs=[\"()\", \"{}\", \"[]\"])\n",
" elif program_name == \"sort\":\n",
" vocab = {1, 2, 3, 4, 5}\n",
" program = lib.make_sort(\n",
" rasp.tokens, rasp.tokens, max_seq_len=max_seq_len, min_key=1)\n",
" elif program_name == \"sort_unique\":\n",
" vocab = {1, 2, 3, 4, 5}\n",
" program = lib.make_sort_unique(rasp.tokens, rasp.tokens)\n",
" elif program_name == \"hist\":\n",
" vocab = {\"a\", \"b\", \"c\", \"d\"}\n",
" program = lib.make_hist()\n",
" elif program_name == \"sort_freq\":\n",
" vocab = {\"a\", \"b\", \"c\", \"d\"}\n",
" program = lib.make_sort_freq(max_seq_len=max_seq_len)\n",
" elif program_name == \"pair_balance\":\n",
" vocab = {\"(\", \")\"}\n",
" program = lib.make_pair_balance(\n",
" sop=rasp.tokens, open_token=\"(\", close_token=\")\")\n",
" else:\n",
" raise NotImplementedError(f\"Program {program_name} not implemented.\")\n",
" return program, vocab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "L_m_ufaua9ri"
},
"outputs": [],
"source": [
"#@title: Assemble model\n",
"program_name = \"sort_unique\" #@param [\"length\", \"frac_prevs\", \"dyck-2\", \"dyck-3\", \"sort\", \"sort_unique\", \"hist\", \"sort_freq\", \"pair_balance\"]\n",
"max_seq_len = 5 #@param {label: \"Test\", type: \"integer\"}\n",
"\n",
"program, vocab = get_program(program_name=program_name,\n",
" max_seq_len=max_seq_len)\n",
"\n",
"print(f\"Compiling...\")\n",
"print(f\" Program: {program_name}\")\n",
"print(f\" Input vocabulary: {vocab}\")\n",
"print(f\" Context size: {max_seq_len}\")\n",
"\n",
"assembled_model = compiling.compile_rasp_to_model(\n",
" program=program,\n",
" vocab=vocab,\n",
" max_seq_len=max_seq_len,\n",
" causal=False,\n",
" compiler_bos=\"bos\",\n",
" compiler_pad=\"pad\",\n",
" mlp_exactness=100)\n",
"\n",
"print(\"Done.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wtwiE-JiXF3F"
},
"outputs": [],
"source": [
"#@title Forward pass\n",
"assembled_model.apply([\"bos\", 3, 4, 1]).decoded"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RkEkVcEHa2gf"
},
"outputs": [],
"source": [
"#@title Plot residual stream\n",
"plot_residuals_and_input(\n",
" model=assembled_model,\n",
" inputs=[\"bos\", 3, 4, 1],\n",
" figsize=(10, 9)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8c4LakWHa4ey"
},
"outputs": [],
"source": [
"#@title Plot layer outputs\n",
"plot_layer_outputs(\n",
" model=assembled_model,\n",
" inputs = [\"bos\", 3, 4, 1],\n",
" figsize=(8, 9)\n",
")"
]
}
],
"metadata": {
"colab": {
"private_outputs": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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