jit_diff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.autograd as autograd\n",
    "import torch.distributions as dist\n",
    "\n",
    "torch.set_printoptions(precision=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = dist.Normal(0, 1).sample(torch.Size([100]))\n",
    "\n",
    "def fn(zu):\n",
    "    z = dist.biject_to(dist.constraints.positive)(zu)\n",
    "    return dist.Normal(0, z).log_prob(data).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "zu = torch.tensor(1., requires_grad=True)\n",
    "fn_jit = torch.jit.trace(fn, (zu,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(-85.8022155762)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fn(zu).backward()\n",
    "zu.grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(-85.8021774292)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zu.grad.zero_()\n",
    "fn_jit(zu).backward()\n",
    "zu.grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(-85.8022155762)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autograd.grad(fn(zu), (zu,))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(-85.8021774292)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autograd.grad(fn_jit(zu), (zu,))[0]"
   ]
  }
 ],
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   "language": "python",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 "nbformat": 4,
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