Add code to JA-multifit

notebooks/JA-multifit_fp16.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -40,12 +40,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "from ulmfit.datasets import ULMFiTDataset, Dataset\n",
-    "from ulmfit import ULMFiT, multifit1552_fp16"
+    "from ulmfit import ULMFiT, multifit1552_fp16, multifit1552_fp32"
    ]
   },
   {
@@ -57,7 +57,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "def multifit1552_fp16():\n",
+      "    return multifit1552_fp32(bs=128).replace_(fp16=True, name=multifit1552_fp16.__name__)\n",
+      "\n",
+      "def multifit1552_fp32(bs=64):\n",
+      "    self = ULMFiT()\n",
+      "    self.replace_(\n",
+      "        label_smoothing_eps=0.0,\n",
+      "        true_wd=True,\n",
+      "        wd=0.1,\n",
+      "        seed=0,\n",
+      "        fp16=False,\n",
+      "        bs=bs,\n",
+      "        use_adam_08=False,\n",
+      "        early_stopping=None,\n",
+      "        name=multifit1552_fp32.__name__\n",
+      "    )\n",
+      "    self.arch.replace_(\n",
+      "        tokenizer='fsp',\n",
+      "        max_vocab=15000,\n",
+      "        qrnn=True,\n",
+      "        n_layers=4,\n",
+      "        n_hid=1552\n",
+      "    )\n",
+      "    self.pretrain_lm.replace_(num_epochs=10, drop_mult=0.5, lr=(1e-2 * bs / 48))\n",
+      "    self.finetuine_lm.replace_(num_epochs=10, drop_mult=1.0, lr=(1e-3 * bs / 48))\n",
+      "    self.classifier.replace_(num_epochs=8, drop_mult=0.5, bs=20, label_smoothing_eps=0.1)\n",
+      "    return self\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import inspect\n",
+    "print(inspect.getsource(multifit1552_fp16))\n",
+    "print(inspect.getsource(multifit1552_fp32))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {

ulmfit/training.py

@@ -454,3 +454,11 @@ class ULMFiT:
         self.finetuine_lm.replace_(**kwargs)
         self.classifier.replace_(**kwargs)
         return self
+
+    def pprint(self):
+        print(f"""ULMFiT(
+    {self.arch},
+    {self.pretrain_lm},
+    {self.finetuine_lm},
+    {self.classifier},
+)""")