45-注意力机制-Watson核回归-自然语言处理-pytorch.ipynb

{
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"
    },
    "image-6.png": {
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"
    },
    "image-7.png": {
     "image/png": 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"
    },
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "77eb7f21",
   "metadata": {},
   "source": [
    "## 注意力汇聚-watson核回归\n",
    "1. 生成数据集：\n",
    "![image.png](attachment:image.png)\n",
    "2. 平均汇聚:基于平均汇聚来计算所有训练样本输出值的平均值\n",
    "![image-2.png](attachment:image-2.png)\n",
    "3. 非参数注意力汇聚\n",
    "![image-7.png](attachment:image-7.png)\n",
    "![image-3.png](attachment:image-3.png)\n",
    "![image-4.png](attachment:image-4.png)\n",
    "![image-5.png](attachment:image-5.png)\n",
    "4. 带参数注意力汇聚\n",
    "![image-6.png](attachment:image-6.png)\n",
    "    1. 批量矩阵乘法\n",
    "    2. 定义模型\n",
    "    3. 训练\n",
    "5. 小结\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ba820b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import dltools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "460be757",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.1460, 0.3968, 0.4273, 0.4801, 0.5118, 0.5828, 0.7924, 0.8084, 0.8569,\n",
      "        0.9211, 0.9233, 0.9581, 1.2528, 1.3142, 1.3719, 1.4706, 1.4993, 1.6430,\n",
      "        1.8192, 1.8387, 1.9106, 1.9272, 1.9503, 1.9701, 2.0296, 2.0575, 2.1680,\n",
      "        2.2052, 2.2272, 2.3021, 2.4630, 2.6981, 2.9630, 3.4872, 3.5731, 3.6360,\n",
      "        3.7355, 3.7371, 3.9474, 4.0651, 4.1327, 4.1617, 4.3437, 4.4190, 4.5313,\n",
      "        4.5353, 4.7938, 4.8110, 4.8491, 4.8977])\n"
     ]
    }
   ],
   "source": [
    "n_train = 50  # 训练样本数\n",
    "x_train, _ = torch.sort(torch.rand(n_train) * 5)   # 排序后的训练样本\n",
    "\n",
    "def f(x):\n",
    "    return 2 * torch.sin(x) + x**0.8\n",
    "\n",
    "y_train = f(x_train) + torch.normal(0.0, 0.5, (n_train,))  # 训练样本的输出\n",
    "x_test = torch.arange(0, 5, 0.1)  # 测试样本\n",
    "y_truth = f(x_test)  # 测试样本的真实输出\n",
    "n_test = len(x_test)  # 测试样本数\n",
    "print(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d6a883bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_kernel_reg(y_hat):\n",
    "    dltools.plot(x_test, [y_truth, y_hat], 'x', 'y', legend=['Truth', 'Pred'],\n",
    "             xlim=[0, 5], ylim=[-1, 5])\n",
    "    dltools.plt.plot(x_train, y_train, 'o', alpha=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "99b018e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "# 基于平均汇聚\n",
    "y_hat = torch.repeat_interleave(y_train.mean(), n_test)\n",
    "plot_kernel_reg(y_hat)"
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "49142088",
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   "source": [
    "# 非参数的注意力汇聚\n",
    "# X_repeat的形状:(n_test,n_train),\n",
    "# 每一行都包含着相同的测试输入（例如：同样的查询）\n",
    "X_repeat = x_test.repeat_interleave(n_train).reshape((-1, n_train))\n",
    "# x_train包含着键。attention_weights的形状：(n_test,n_train),\n",
    "# 每一行都包含着要在给定的每个查询的值（y_train）之间分配的注意力权重\n",
    "attention_weights = nn.functional.softmax(-(X_repeat - x_train)**2 / 2, dim=1)\n",
    "# y_hat的每个元素都是值的加权平均值，其中的权重是注意力权重\n",
    "y_hat = torch.matmul(attention_weights, y_train)\n",
    "plot_kernel_reg(y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d42ea3e9",
   "metadata": {},
   "outputs": [
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      "text/plain": [
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      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "dltools.show_heatmaps(attention_weights.unsqueeze(0).unsqueeze(0),\n",
    "                  xlabel='Sorted training inputs',\n",
    "                  ylabel='Sorted testing inputs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "473d0f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 1, 6])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 批量矩阵乘法 注意相撞变换\n",
    "X = torch.ones((2, 1, 4))\n",
    "Y = torch.ones((2, 4, 6))\n",
    "torch.bmm(X, Y).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "12f380f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 4.5000]],\n",
       "\n",
       "        [[14.5000]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "weights = torch.ones((2, 10)) * 0.1\n",
    "values = torch.arange(20.0).reshape((2, 10))\n",
    "torch.bmm(weights.unsqueeze(1), values.unsqueeze(-1))"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1cbeea50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义模型\n",
    "class NWKernelRegression(nn.Module):\n",
    "    def __init__(self, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.w = nn.Parameter(torch.rand((1,), requires_grad=True))\n",
    "\n",
    "    def forward(self, queries, keys, values):\n",
    "        # queries和attention_weights的形状为(查询个数，“键－值”对个数)\n",
    "        queries = queries.repeat_interleave(keys.shape[1]).reshape((-1, keys.shape[1]))\n",
    "        self.attention_weights = nn.functional.softmax(-((queries - keys) * self.w)**2 / 2, dim=1)\n",
    "        # values的形状为(查询个数，“键－值”对个数)\n",
    "        return torch.bmm(self.attention_weights.unsqueeze(1),values.unsqueeze(-1)).reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7582f119",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 开始训练\n",
    "# X_tile的形状:(n_train，n_train)，每一行数据重复一遍\n",
    "\n",
    "X_tile = x_train.repeat((n_train, 1))\n",
    "# Y_tile的形状:(n_train，n_train)，每一行都包含着相同的训练输出\n",
    "Y_tile = y_train.repeat((n_train, 1))\n",
    "# keys的形状:('n_train'，'n_train'-1)\n",
    "keys = X_tile[(1 - torch.eye(n_train)).type(torch.bool)].reshape((n_train, -1))\n",
    "# values的形状:('n_train'，'n_train'-1)\n",
    "values = Y_tile[(1 - torch.eye(n_train)).type(torch.bool)].reshape((n_train, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bdb2c82d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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   "source": [
    "net = NWKernelRegression()\n",
    "loss = nn.MSELoss(reduction='none')\n",
    "trainer = torch.optim.SGD(net.parameters(), lr=0.5)\n",
    "animator = dltools.Animator(xlabel='epoch', ylabel='loss', xlim=[1, 5])\n",
    "\n",
    "for epoch in range(5):\n",
    "    trainer.zero_grad()\n",
    "    l = loss(net(x_train, keys, values), y_train)\n",
    "    l.sum().backward()\n",
    "    trainer.step()\n",
    "    print(f'epoch {epoch + 1}, loss {float(l.sum()):.6f}')\n",
    "    animator.add(epoch + 1, float(l.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "acb882c5",
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      "text/plain": [
       "<Figure size 252x180 with 1 Axes>"
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     },
     "metadata": {
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   ],
   "source": [
    "# keys的形状:(n_test，n_train)，每一行包含着相同的训练输入（例如，相同的键）\n",
    "keys = x_train.repeat((n_test, 1))\n",
    "# value的形状:(n_test，n_train)\n",
    "values = y_train.repeat((n_test, 1))\n",
    "y_hat = net(x_test, keys, values).unsqueeze(1).detach()\n",
    "plot_kernel_reg(y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "34eafa21",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 180x180 with 2 Axes>"
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     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "dltools.show_heatmaps(net.attention_weights.unsqueeze(0).unsqueeze(0),\n",
    "                  xlabel='Sorted training inputs',\n",
    "                  ylabel='Sorted testing inputs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f7d19da",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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