array_api_compat_binary_op.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.4'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy\n",
    "import array_api_compat as aac\n",
    "\n",
    "aac.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = torch.arange(10)\n",
    "n = numpy.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.add(n, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.add(t, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aac.get_namespace(n).add(n, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'float' object has no attribute 'dtype'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43maac\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_namespace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/ab/main/.conda/lib/python3.10/site-packages/array_api_compat/torch/_aliases.py:91\u001b[0m, in \u001b[0;36m_two_arg.<locals>._f\u001b[0;34m(x1, x2, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m     90\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_f\u001b[39m(x1, x2, \u001b[38;5;241m/\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 91\u001b[0m     x1, x2 \u001b[38;5;241m=\u001b[39m \u001b[43m_fix_promotion\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx2\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     92\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m f(x1, x2, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/ab/main/.conda/lib/python3.10/site-packages/array_api_compat/torch/_aliases.py:104\u001b[0m, in \u001b[0;36m_fix_promotion\u001b[0;34m(x1, x2, only_scalar)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_fix_promotion\u001b[39m(x1, x2, only_scalar\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m--> 104\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m x1\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _array_api_dtypes \u001b[38;5;129;01mor\u001b[39;00m \u001b[43mx2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _array_api_dtypes:\n\u001b[1;32m    105\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m x1, x2\n\u001b[1;32m    106\u001b[0m     \u001b[38;5;66;03m# If an argument is 0-D pytorch downcasts the other argument\u001b[39;00m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'float' object has no attribute 'dtype'"
     ]
    }
   ],
   "source": [
    "aac.get_namespace(t).add(t, 1.0)"
   ]
  }
 ],
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