85_注意力分数.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 注意力分数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 遮蔽softmax操作\n",
    "def masked_softmax(X, valid_lens):\n",
    "    \"\"\"通过在最后一个轴上遮蔽元素来执行softmax操作\"\"\"\n",
    "    if valid_lens is None:\n",
    "        return nn.functional.softmax(X, dim=-1)\n",
    "    else:\n",
    "        shape = X.shape\n",
    "        if valid_lens.dim() == 1:\n",
    "            valid_lens = torch.repeat_interleave(valid_lens, shape[1])\n",
    "        else:\n",
    "            valid_lens = valid_lens.reshape(-1)\n",
    "        X = d2l.sequence_mask(X.reshape(-1, shape[-1]), valid_lens, value=-1e6)   \n",
    "        return nn.functional.softmax(X.reshape(shape), dim=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[0.3005, 0.6995, 0.0000, 0.0000],\n",
       "         [0.4543, 0.5457, 0.0000, 0.0000]],\n",
       "\n",
       "        [[0.3104, 0.2572, 0.4324, 0.0000],\n",
       "         [0.2256, 0.3151, 0.4594, 0.0000]]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 演示此函数是如何工作\n",
    "masked_softmax(torch.rand(2,2,4), torch.tensor([2,3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[1.0000, 0.0000, 0.0000, 0.0000],\n",
       "         [0.3032, 0.3868, 0.3100, 0.0000]],\n",
       "\n",
       "        [[0.3930, 0.6070, 0.0000, 0.0000],\n",
       "         [0.2379, 0.2396, 0.2583, 0.2642]]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masked_softmax(torch.rand(2,2,4), torch.tensor([[1,3],[2,4]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加性注意力\n",
    "class AdditiveAttention(nn.Module):\n",
    "    \"\"\"加性注意力\"\"\"\n",
    "    def __init__(self, key_size, query_size, num_hiddens, dropout, **kwargs):\n",
    "        super(AdditiveAttention, self).__init__(**kwargs)\n",
    "        self.W_k = nn.Linear(key_size, num_hiddens, bias=False)\n",
    "        self.W_q = nn.Linear(query_size, num_hiddens, bias=False)\n",
    "        self.w_v = nn.Linear(num_hiddens, 1, bias=False)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, queries, keys, values, valid_lens):\n",
    "        queries, keys = self.W_q(queries), self.W_k(keys)\n",
    "        features = queries.unsqueeze(2) + keys.unsqueeze(1)\n",
    "        features = torch.tanh(features)\n",
    "        scores = self.w_v(features).squeeze(-1)\n",
    "        self.attention_weights = masked_softmax(scores, valid_lens)\n",
    "        return torch.bmm(self.dropout(self.attention_weights), values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 2.0000,  3.0000,  4.0000,  5.0000]],\n",
       "\n",
       "        [[10.0000, 11.0000, 12.0000, 13.0000]]], grad_fn=<BmmBackward0>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 演示上面的AdditiveAttention类\n",
    "queries, keys = torch.normal(0, 1, (2,1,20)), torch.ones((2,10,2))\n",
    "values = torch.arange(40, dtype=torch.float32).reshape(1,10,4).repeat(2,1,1)  \n",
    "valid_lens = torch.tensor([2,6])\n",
    "attention = AdditiveAttention(key_size=2, query_size=20, num_hiddens=8, dropout=0.1)  \n",
    "attention.eval()\n",
    "attention(queries, keys, values, valid_lens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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      "text/plain": [
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     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "# 注意力权重\n",
    "d2l.show_heatmaps(attention.attention_weights.reshape((1,1,2,10)),\n",
    "                 xlabel='Keys', ylabel='Queries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 缩放点积注意力\n",
    "class DotProductAttention(nn.Module):\n",
    "    \"\"\"缩放点积注意力\"\"\"\n",
    "    def __init__(self, dropout, **kwargs):\n",
    "        super(DotProductAttention, self).__init__(**kwargs)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, queries, keys, values, valid_lens=None):\n",
    "        d = queries.shape[-1]\n",
    "        scores = torch.bmm(queries, keys.transpose(1,2)) / math.sqrt(d)\n",
    "        self.attention_weights = masked_softmax(scores, valid_lens)\n",
    "        return torch.bmm(self.dropout(self.attention_weights), values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 2.0000,  3.0000,  4.0000,  5.0000]],\n",
       "\n",
       "        [[10.0000, 11.0000, 12.0000, 13.0000]]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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   "source": [
    "# 演示上述的DotProductAttention类\n",
    "queries = torch.normal(0,1,(2,1,2))\n",
    "attention = DotProductAttention(dropout=0.5)\n",
    "attention.eval()\n",
    "attention(queries, keys, values, valid_lens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
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   "source": [
    "# 均匀的注意力权重\n",
    "d2l.show_heatmaps(attention.attention_weights.reshape((1,1,2,10)),\n",
    "                 xlabel='Keys', ylabel='Queries')"
   ]
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