Print PyTorch backwards ops

pytorch_backward_ops.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ba437fc",
   "metadata": {},
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   "source": [
    "import torch\n",
    "from torch import nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b5cb8743",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tracing back tensors:\n",
      "<MulBackward0 object at 0x11c9ffaf0>\n",
      "<SumBackward0 object at 0x12e9c3a60>\n",
      "<MulBackward0 object at 0x12e9c3400>\n",
      "<AccumulateGrad object at 0x12e9c3070>\n",
      "Tensor with grad found: tensor([0.3643, 0.6264, 0.1329, 0.5581, 0.3163], requires_grad=True)\n",
      " - gradient: tensor([3., 3., 3., 3., 3.])\n",
      "\n",
      "<AccumulateGrad object at 0x12e9c3ee0>\n",
      "Tensor with grad found: tensor([1., 1., 1., 1., 1.], requires_grad=True)\n",
      " - gradient: tensor([1.0928, 1.8791, 0.3986, 1.6743, 0.9489])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "input1 = torch.randn(100, 128, requires_grad=True)\n",
    "input2 = torch.randn(100, 128, requires_grad=True)\n",
    "cos = nn.CosineSimilarity(dim=1, eps=1e-6)\n",
    "output = cos(input1, input2)\n",
    "\n",
    "print()\n",
    "print('Tracing back tensors:')\n",
    "def getBack(var_grad_fn):\n",
    "    print(var_grad_fn)\n",
    "    for n in var_grad_fn.next_functions:\n",
    "        if n[0]:\n",
    "            try:\n",
    "                tensor = getattr(n[0], 'variable')\n",
    "                print(n[0])\n",
    "                print('Tensor with grad found:', tensor)\n",
    "                print(' - gradient:', tensor.grad)\n",
    "                print()\n",
    "            except AttributeError as e:\n",
    "                getBack(n[0])\n",
    "\n",
    "output.sum().backward()\n",
    "getBack(loss.grad_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e320c04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.8534,  0.8103, -0.8892,  ..., -0.2915, -0.8183, -0.6481],\n",
       "        [-0.9195, -0.0834,  1.6122,  ...,  0.0945, -0.0291, -0.7190],\n",
       "        [ 0.3594,  1.0440, -0.5852,  ..., -0.2921, -0.4885,  0.1041],\n",
       "        ...,\n",
       "        [-0.3927,  0.2467,  0.3223,  ...,  0.1250, -0.3101,  0.2410],\n",
       "        [ 1.3586, -1.4949,  0.3142,  ..., -0.1608,  0.8276,  0.8251],\n",
       "        [-0.2738,  0.9730, -0.6034,  ..., -0.8690,  0.0268, -0.8985]],\n",
       "       grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad4fd55f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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