Created
February 6, 2020 14:24
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GRU tuning
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"import torch\n", | |
"import torch.nn as nn\n", | |
"from torch import Tensor\n", | |
"from typing import List" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"inputs = [torch.randn(64, 256, 256), torch.randn(128, 128, 256), torch.randn(256, 64, 256), torch.randn(512, 32, 256)]\n", | |
"inputs = [x.cuda() for x in inputs]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"gru_cudnn = nn.GRU(256, 256).cuda()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"2.93 ms ± 36.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%%timeit\n", | |
"out, _ = gru_cudnn(inputs[1])\n", | |
"s = out.sum()\n", | |
"s.sum().detach().cpu().numpy()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"8.91 ms ± 796 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%%timeit\n", | |
"out, _ = gru_cudnn(inputs[1])\n", | |
"s = out.sum()\n", | |
"s.backward()\n", | |
"gru_cudnn.bias_ih_l0.grad.sum().detach().cpu().numpy()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"class GRU(nn.Module):\n", | |
" def __init__(self, ks=256):\n", | |
" super(GRU, self).__init__()\n", | |
" \n", | |
" self.weight_ih = nn.Parameter(torch.randn(ks, ks * 3))\n", | |
" self.weight_hh = nn.Parameter(torch.randn(ks, ks * 3))\n", | |
" self.bias_ih = nn.Parameter(torch.randn(3 * ks))\n", | |
" self.bias_hh = nn.Parameter(torch.randn(3 * ks))\n", | |
" \n", | |
" self.ks = ks\n", | |
" \n", | |
" def forward(self, x: Tensor):\n", | |
" xparts = torch.unbind(x, 0)\n", | |
" \n", | |
" outs: List[Tensor] = []\n", | |
" last = torch.zeros_like((x[0]))\n", | |
" for i in range(len(xparts)):\n", | |
" part = xparts[i]\n", | |
" ip = torch.mm(part, self.weight_ih) + self.bias_ih\n", | |
" hp = torch.mm(last, self.weight_hh) + self.bias_hh\n", | |
" i_r, i_z, i_n = ip.chunk(3, 1)\n", | |
" h_r, h_z, h_n = hp.chunk(3, 1)\n", | |
" r = torch.sigmoid(i_r + h_r)\n", | |
" z = torch.sigmoid(i_z + h_z)\n", | |
" \n", | |
" n = torch.tanh(i_n + r * h_n)\n", | |
" last = (1 - z) * n + z * last\n", | |
" \n", | |
" outs += [last]\n", | |
" \n", | |
" return torch.stack(outs)\n", | |
" \n", | |
"gru_raw = GRU(256).cuda()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"68.9 ms ± 1.52 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%%timeit\n", | |
"out = gru_raw(inputs[1])\n", | |
"s = out.sum()\n", | |
"s.sum().detach().cpu().numpy()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"161 ms ± 2.04 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%%timeit\n", | |
"out = gru_raw(inputs[1])\n", | |
"s = out.sum()\n", | |
"s.backward()\n", | |
"gru_raw.bias_ih.grad.sum().detach().cpu().numpy()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"gru_jit = torch.jit.script(GRU(256).cuda())" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"22.7 ms ± 23.4 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%%timeit\n", | |
"out = gru_jit(inputs[1])\n", | |
"s = out.sum()\n", | |
"s.sum().detach().cpu().numpy()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"58.1 ms ± 682 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" | |
] | |
} | |
], | |
"source": [ | |
"%%timeit\n", | |
"out = gru_jit(inputs[1])\n", | |
"s = out.sum()\n", | |
"s.backward()\n", | |
"gru_jit.bias_ih.grad.sum().detach().cpu().numpy()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": {}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"language": "python", | |
"name": "python3" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.6.10" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 4 | |
} |
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