1059 lines
260 KiB
Text
1059 lines
260 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2026-05-29T15:19:42.541639835Z",
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"start_time": "2026-05-29T15:19:40.265547407Z"
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}
|
||
},
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||
"source": [
|
||
"import torch\n",
|
||
"from d2l import torch as d2l\n"
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],
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||
"outputs": [],
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||
"execution_count": 2
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},
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||
{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-05-29T15:19:42.593785744Z",
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"start_time": "2026-05-29T15:19:42.545273168Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def show_heatmaps(matrices,xlabel,ylabel,titles=None,figsize=(2.5,2.5),cmap='Reds'):\n",
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" d2l.use_svg_display()\n",
|
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" num_rows,num_cols = matrices.shape[0],matrices.shape[1]\n",
|
||
" fig,axes = d2l.plt.subplots(num_rows,num_cols,figsize=figsize,sharex=True,squeeze=False)\n",
|
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" for i,(row_axes,row_matrices) in enumerate(zip(axes,matrices)):\n",
|
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" for j,(ax,matrix) in enumerate(zip(row_axes,row_matrices)):\n",
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" pcm = ax.imshow(matrix.detach().numpy(),cmap=cmap)\n",
|
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" if i == num_rows - 1:\n",
|
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" ax.set_xlabel(xlabel)\n",
|
||
" if j == 0:\n",
|
||
" ax.set_ylabel(ylabel)\n",
|
||
" if titles:\n",
|
||
" ax.set_title(titles[j])\n",
|
||
" fig.colorbar(pcm,ax=axes,shrink=0.6)"
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||
],
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"id": "a0e3f725b7764f08",
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"outputs": [],
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||
"execution_count": 3
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-05-29T15:19:42.832298248Z",
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"start_time": "2026-05-29T15:19:42.594979091Z"
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}
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},
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"cell_type": "code",
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"source": [
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||
"attention_weights = torch.eye(10).reshape((1, 1, 10, 10))\n",
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||
"\n",
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||
"show_heatmaps(attention_weights, xlabel='Keys', ylabel='Queries')"
|
||
],
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||
"id": "bb798de34fc4d0fa",
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||
"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 250x250 with 2 Axes>"
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],
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"50"
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},
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{
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"metadata": {
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"end_time": "2026-05-29T15:19:43.317103476Z",
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}
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},
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"cell_type": "code",
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"source": [
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||
"from torch import nn\n",
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||
"X_repeat = x_test.repeat_interleave(n_train).reshape((-1, n_train))\n",
|
||
"attention_weights = nn.functional.softmax(-(X_repeat - x_train)**2 / 2, dim=1)\n",
|
||
"y_hat = torch.matmul(attention_weights,y_train)\n",
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"x_train,X_repeat,attention_weights"
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],
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"id": "fa7579d7cd8bfcb7",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(tensor([9.4801e-04, 5.2621e-02, 2.3643e-01, 2.9541e-01, 3.2529e-01, 3.5294e-01,\n",
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" 3.6399e-01, 4.1190e-01, 5.8751e-01, 6.8494e-01, 7.2698e-01, 8.0101e-01,\n",
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" 9.2743e-01, 9.7225e-01, 1.1656e+00, 1.4397e+00, 1.4788e+00, 1.6088e+00,\n",
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" 1.7147e+00, 1.8255e+00, 1.8951e+00, 1.9322e+00, 2.3849e+00, 2.4183e+00,\n",
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||
" 2.4902e+00, 2.5441e+00, 2.6483e+00, 2.8371e+00, 2.9705e+00, 3.1383e+00,\n",
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" 3.2709e+00, 3.3751e+00, 3.3849e+00, 3.4902e+00, 3.5679e+00, 3.5782e+00,\n",
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" 3.5821e+00, 3.8648e+00, 4.1552e+00, 4.3914e+00, 4.4348e+00, 4.4414e+00,\n",
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||
" 4.8934e+00, 4.9044e+00]),\n",
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||
" tensor([[0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],\n",
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" [0.1000, 0.1000, 0.1000, ..., 0.1000, 0.1000, 0.1000],\n",
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" [0.2000, 0.2000, 0.2000, ..., 0.2000, 0.2000, 0.2000],\n",
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" ...,\n",
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||
" [4.7000, 4.7000, 4.7000, ..., 4.7000, 4.7000, 4.7000],\n",
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||
" [4.8000, 4.8000, 4.8000, ..., 4.8000, 4.8000, 4.8000],\n",
|
||
" [4.9000, 4.9000, 4.9000, ..., 4.9000, 4.9000, 4.9000]]),\n",
|
||
" tensor([[6.8662e-02, 6.8567e-02, 6.6769e-02, ..., 6.3916e-07, 4.3363e-07,\n",
|
||
" 4.1091e-07],\n",
|
||
" [6.4257e-02, 6.4501e-02, 6.3975e-02, ..., 9.6787e-07, 6.6191e-07,\n",
|
||
" 6.2792e-07],\n",
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||
" [5.9935e-02, 6.0473e-02, 6.1093e-02, ..., 1.4608e-06, 1.0070e-06,\n",
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||
" 9.5634e-07],\n",
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||
" ...,\n",
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||
" [9.4878e-07, 1.2079e-06, 2.7906e-06, ..., 5.8779e-02, 5.8062e-02,\n",
|
||
" 5.7935e-02],\n",
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||
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|
||
" 6.1781e-02],\n",
|
||
" [4.0301e-07, 5.1842e-07, 1.2425e-06, ..., 6.5370e-02, 6.5614e-02,\n",
|
||
" 6.5615e-02]]))"
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||
]
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||
},
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||
"execution_count": 10,
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||
"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 10
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-05-29T15:19:43.435846223Z",
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"start_time": "2026-05-29T15:19:43.319732830Z"
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}
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},
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"cell_type": "code",
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||
"source": "plot_kernel_reg(y_hat)",
|
||
"id": "f76573b2eb74ba7",
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||
"outputs": [
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||
{
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||
"data": {
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"text/plain": [
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"<Figure size 350x250 with 1 Axes>"
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{
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"source": [
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"d2l.show_heatmaps(attention_weights.unsqueeze(0).unsqueeze(0),\n",
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" xlabel='Sorted training inputs',\n",
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" ylabel='Sorted testing inputs')"
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{
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"<Figure size 250x250 with 2 Axes>"
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},
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"metadata": {},
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"output_type": "display_data",
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"jetTransient": {
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"display_id": null
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}
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}
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],
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"execution_count": 12
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-05-29T15:19:43.610782245Z",
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"start_time": "2026-05-29T15:19:43.560607937Z"
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}
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},
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"cell_type": "code",
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"source": [
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||
"class NWKernelRegression(nn.Module):\n",
|
||
" def __init__(self,**kwargs):\n",
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||
" super().__init__(**kwargs)\n",
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||
" self.w = nn.Parameter(torch.randn((1,),requires_grad=True))\n",
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||
" def forward(self,queries,keys,values):\n",
|
||
" queries = queries.repeat_interleave(keys.shape[1]).reshape(-1,keys.shape[1])\n",
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||
" self.attention_weights = nn.functional.softmax(\n",
|
||
" -((queries - keys) * self.w)**2 / 2, dim=1)\n",
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||
" return torch.bmm(self.attention_weights.unsqueeze(1),values.unsqueeze(-1)).reshape(-1)"
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||
],
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||
"id": "aeb79a3bd64c826",
|
||
"outputs": [],
|
||
"execution_count": 13
|
||
},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-05-29T15:19:43.661276280Z",
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"start_time": "2026-05-29T15:19:43.611752619Z"
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}
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},
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"cell_type": "code",
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"source": [
|
||
"# X_tile的形状:(n_train，n_train)，每一行都包含着相同的训练输入\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))"
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||
],
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||
"id": "7061ed7f139bc78e",
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||
"outputs": [],
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||
"execution_count": 14
|
||
},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-05-29T15:19:44.080669795Z",
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"start_time": "2026-05-29T15:19:43.662206275Z"
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}
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},
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"cell_type": "code",
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"source": [
|
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"net = NWKernelRegression()\n",
|
||
"loss = nn.MSELoss(reduction='none')\n",
|
||
"trainer = torch.optim.SGD(net.parameters(), lr=0.5)\n",
|
||
"animator = d2l.Animator(xlabel='epoch', ylabel='loss', xlim=[1, 5])\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()))"
|
||
],
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||
"id": "3dca11d4e962d859",
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"outputs": [
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||
{
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"data": {
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"text/plain": [
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"<Figure size 350x250 with 1 Axes>"
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],
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-05-29T15:19:44.196985129Z",
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"start_time": "2026-05-29T15:19:44.082458369Z"
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}
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},
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"cell_type": "code",
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"source": [
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"# 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",
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"keys = x_train.repeat((n_test, 1))\n",
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"# value的形状:(n_test，n_train)\n",
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"values = y_train.repeat((n_test, 1))\n",
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"y_hat = net(x_test, keys, values).unsqueeze(1).detach()\n",
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"plot_kernel_reg(y_hat)"
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],
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"id": "4ff00f0b4983b55e",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 350x250 with 1 Axes>"
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],
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},
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"metadata": {},
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}
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],
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"execution_count": 16
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-05-29T15:19:44.303651198Z",
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"start_time": "2026-05-29T15:19:44.199584310Z"
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}
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},
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"cell_type": "code",
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"source": [
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"d2l.show_heatmaps(net.attention_weights.unsqueeze(0).unsqueeze(0),\n",
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"xlabel='Sorted training inputs',\n",
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"ylabel='Sorted testing inputs')"
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],
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"id": "97a690478767c3c3",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 250x250 with 2 Axes>"
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],
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],
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},
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"metadata": {
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"ExecuteTime": {
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}
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},
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"cell_type": "code",
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"source": [
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"def masked_softmax(X,valid_lens):\n",
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||
" # X:3D valid_len 1D or 2D\n",
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||
" if valid_lens is None:\n",
|
||
" return nn.functional.softmax(X,dim=-1)\n",
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||
" else:\n",
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||
" shape = X.shape\n",
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" if valid_lens.dim()==1:\n",
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||
" valid_lens = torch.repeat_interleave(valid_lens,shape[1])\n",
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||
" else:\n",
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||
" valid_lens = valid_lens.reshape(-1)\n",
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" #print(valid_lens)\n",
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" X = d2l.sequence_mask(X.reshape(-1,shape[-1]),valid_lens,value=-1e6)\n",
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" return nn.functional.softmax(X.reshape(shape),dim=-1)"
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],
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"id": "8d03d0c7f735b755",
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"outputs": [],
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"execution_count": 18
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-05-29T15:19:44.420710191Z",
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}
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},
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"source": "masked_softmax(torch.rand(2,2,4),torch.tensor([2,3]))",
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"id": "27f8c24ec572b267",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[[0.6937, 0.3063, 0.0000, 0.0000],\n",
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" [0.6165, 0.3835, 0.0000, 0.0000]],\n",
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"\n",
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" [[0.3526, 0.3291, 0.3183, 0.0000],\n",
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" [0.2735, 0.2359, 0.4906, 0.0000]]])"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 19
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-05-29T15:19:44.470391356Z",
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}
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},
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"cell_type": "code",
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"source": [
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"class AdditiveAttention(nn.Module):\n",
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" def __init__(self,key_size,query_size,num_hiddens,dropout,**kwargs):\n",
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" super(AdditiveAttention,self).__init__(**kwargs)\n",
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" self.W_k = nn.Linear(key_size,num_hiddens,bias=False)\n",
|
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" 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",
|
||
" def forward(self,queries,keys,value,valid_lens):\n",
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" queries,keys=self.W_q(queries),self.W_k(keys)\n",
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" # queries (batch_size,n_q,1,num_hidden)\n",
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" # key (batch_size,1,n_k,num_hiddens)\n",
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" features = queries.unsqueeze(2) + keys.unsqueeze(1)\n",
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" #features (batch_size,n_q,n_k,num_hidden)\n",
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" features = torch.tanh(features)\n",
|
||
" scores = self.w_v(features).squeeze(-1)\n",
|
||
" #print(f\"Inside AdditiveAttention: value.shape = {value.shape}\") # 检查此处形状\n",
|
||
" self.attention_weights = masked_softmax(scores, valid_lens)\n",
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"\n",
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" return torch.bmm(self.dropout(self.attention_weights), value)"
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],
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"id": "c7e5254395b14b1",
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"outputs": [],
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"execution_count": 20
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-05-29T15:19:44.536311739Z",
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"start_time": "2026-05-29T15:19:44.471725853Z"
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}
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},
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"cell_type": "code",
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"source": [
|
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"queries,keys = torch.normal(0,1,(2,1,20)) , torch.ones((2,10,2))\n",
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||
"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,\n",
|
||
" dropout=0.1)\n",
|
||
"attention.eval()\n",
|
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"attention(queries,keys,values,valid_lens)"
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],
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"id": "77e325a032e70d98",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[[ 2.0000, 3.0000, 4.0000, 5.0000]],\n",
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"\n",
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" [[10.0000, 11.0000, 12.0000, 13.0000]]], grad_fn=<BmmBackward0>)"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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},
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}
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},
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"cell_type": "code",
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"source": [
|
||
"d2l.show_heatmaps(attention.attention_weights.reshape((1, 1, 2, 10)),\n",
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" xlabel='Keys', ylabel='Queries')"
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],
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||
"id": "93ceedfd6e03982f",
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"outputs": [
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{
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],
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}
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},
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"cell_type": "code",
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"source": [
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"import math\n",
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"class DotProductAttention(nn.Module):\n",
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" \"\"\"缩放点积注意力\"\"\"\n",
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||
" def __init__(self, dropout, **kwargs):\n",
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||
" super(DotProductAttention, self).__init__(**kwargs)\n",
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||
" self.dropout = nn.Dropout(dropout)\n",
|
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"# queries的形状：(batch_size，查询的个数，d)\n",
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"# keys的形状：(batch_size，“键－值”对的个数，d)\n",
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"# values的形状：(batch_size，“键－值”对的个数，值的维度)\n",
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"# valid_lens的形状:(batch_size，)或者(batch_size，查询的个数)\n",
|
||
" def forward(self, queries, keys, values, valid_lens=None):\n",
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" d = queries.shape[-1]\n",
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||
" # 设置transpose_b=True为了交换keys的最后两个维度\n",
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" scores = torch.bmm(queries, keys.transpose(1,2)) / math.sqrt(d)\n",
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" self.attention_weights = masked_softmax(scores, valid_lens)\n",
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"\n",
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" return torch.bmm(self.dropout(self.attention_weights), values)"
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],
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"id": "8453c76623a5b435",
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"outputs": [],
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"execution_count": 23
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},
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{
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"source": [
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"queries = torch.normal(0, 1, (2, 1, 2))\n",
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"attention = DotProductAttention(dropout=0.5)\n",
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||
"attention.eval()\n",
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"attention(queries, keys, values, valid_lens)"
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],
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"id": "8af6b28944977a62",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[[ 2.0000, 3.0000, 4.0000, 5.0000]],\n",
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"\n",
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" [[10.0000, 11.0000, 12.0000, 13.0000]]])"
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]
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},
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"execution_count": 24,
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"metadata": {},
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{
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"end_time": "2026-05-29T15:19:44.822905172Z",
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}
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},
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"cell_type": "code",
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"source": [
|
||
"d2l.show_heatmaps(attention.attention_weights.reshape((1, 1, 2, 10)),\n",
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"xlabel='Keys', ylabel='Queries')"
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||
],
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||
"id": "28f7e16771076e33",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 250x250 with 2 Axes>"
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],
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}
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}
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],
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},
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{
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"ExecuteTime": {
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}
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},
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"cell_type": "code",
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"source": [
|
||
"class AttentionDecoder(d2l.Decoder):\n",
|
||
" def __init__(self,**kwargs):\n",
|
||
" super(AttentionDecoder, self).__init__(**kwargs)\n",
|
||
" @property\n",
|
||
" def attention_weight(self):\n",
|
||
" raise NotImplementedError"
|
||
],
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"id": "33507bd2e1917a29",
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"outputs": [],
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"execution_count": 26
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},
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{
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"metadata": {
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"ExecuteTime": {
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}
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},
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"cell_type": "code",
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"source": [
|
||
"class Seq2SeqAttentionDecoder(AttentionDecoder):\n",
|
||
" def __init__(self,vocab_size,embed_size,num_hiddens,num_layers,dropout=0,**kwargs):\n",
|
||
" super(Seq2SeqAttentionDecoder, self).__init__(**kwargs)\n",
|
||
" self.attention = AdditiveAttention(\n",
|
||
" num_hiddens, num_hiddens, num_hiddens,dropout)\n",
|
||
" self.embedding = nn.Embedding(vocab_size, embed_size)\n",
|
||
" self.rnn = nn.GRU(\n",
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||
" embed_size + num_hiddens, num_hiddens, num_layers,\n",
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||
" dropout=dropout)\n",
|
||
" self.dense = nn.Linear(num_hiddens, vocab_size)\n",
|
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" def init_state(self, enc_outputs, enc_valid_lens, *args):\n",
|
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"# outputs的形状为(batch_size，num_steps，num_hiddens).\n",
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||
"# hidden_state的形状为(num_layers，batch_size，num_hiddens)\n",
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" outputs, hidden_state = enc_outputs\n",
|
||
" #print(f\"Encoder outputs shape before permute: {outputs.shape}\") # 应为 (num_steps, batch_size, num_hiddens) 或 (batch_size, num_steps, num_hiddens)\n",
|
||
" enc_outputs_permuted = outputs.permute(1, 0, 2)\n",
|
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" #print(f\"After permute: {enc_outputs_permuted.shape}\") # 期望 (batch_size, num_steps, num_hiddens)\n",
|
||
" return (enc_outputs_permuted, hidden_state, enc_valid_lens)\n",
|
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" def forward(self, X, state):\n",
|
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" # enc_outputs的形状为(batch_size,num_steps,num_hiddens).\n",
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" # hidden_state的形状为(num_layers,batch_size,\n",
|
||
" # num_hiddens)\n",
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||
" enc_outputs, hidden_state, enc_valid_lens = state\n",
|
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" # 输出X的形状为(num_steps,batch_size,embed_size)\n",
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||
" X = self.embedding(X).permute(1, 0, 2)\n",
|
||
" outputs, self._attention_weights = [], []\n",
|
||
" for x in X:\n",
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" # query的形状为(batch_size,1,num_hiddens)\n",
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||
" query = torch.unsqueeze(hidden_state[-1], dim=1)\n",
|
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" # context的形状为(batch_size,1,num_hiddens)\n",
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" #print(f\"values shape before attention: {enc_outputs.shape}\") # 应为 (4, 7, 16)\n",
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" context = self.attention(\n",
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||
" query, enc_outputs, enc_outputs, enc_valid_lens)\n",
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" # 在特征维度上连结\n",
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" x = torch.cat((context, torch.unsqueeze(x, dim=1)), dim=-1)\n",
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" # 将x变形为(1,batch_size,embed_size+num_hiddens)\n",
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" out, hidden_state = self.rnn(x.permute(1, 0, 2), hidden_state)\n",
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" outputs.append(out)\n",
|
||
" self._attention_weights.append(self.attention.attention_weights)\n",
|
||
" # 全连接层变换后，outputs的形状为\n",
|
||
" # (num_steps,batch_size,vocab_size)\n",
|
||
" outputs = self.dense(torch.cat(outputs, dim=0))\n",
|
||
" return outputs.permute(1, 0, 2), [enc_outputs, hidden_state,\n",
|
||
" enc_valid_lens]\n",
|
||
" @property\n",
|
||
" def attention_weights(self):\n",
|
||
" return self._attention_weights"
|
||
],
|
||
"id": "a8a497c9041910a1",
|
||
"outputs": [],
|
||
"execution_count": 27
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-05-29T15:19:44.996664037Z",
|
||
"start_time": "2026-05-29T15:19:44.927689328Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"encoder = d2l.Seq2SeqEncoder(vocab_size=10, embed_size=8, num_hiddens=16,\n",
|
||
"num_layers=2)\n",
|
||
"encoder.eval()\n",
|
||
"decoder = Seq2SeqAttentionDecoder(vocab_size=10, embed_size=8, num_hiddens=16,\n",
|
||
"num_layers=2)\n",
|
||
"decoder.eval()\n",
|
||
"X = torch.zeros((4, 7), dtype=torch.long) # (batch_size,num_steps)\n",
|
||
"state = decoder.init_state(encoder(X), None)\n",
|
||
"output, state = decoder(X, state)\n",
|
||
"output.shape, len(state), state[0].shape, len(state[1]), state[1][0].shape"
|
||
],
|
||
"id": "b2a6bc735743bd0f",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(torch.Size([4, 7, 10]), 3, torch.Size([4, 7, 16]), 2, torch.Size([4, 16]))"
|
||
]
|
||
},
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 28
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-05-29T15:19:45.047118759Z",
|
||
"start_time": "2026-05-29T15:19:44.998053092Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": "",
|
||
"id": "4c7bf86095f15c98",
|
||
"outputs": [],
|
||
"execution_count": 28
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-05-29T15:20:14.678359202Z",
|
||
"start_time": "2026-05-29T15:19:45.048442204Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"embed_size, num_hiddens, num_layers, dropout = 32, 32, 2, 0.1\n",
|
||
"batch_size, num_steps = 64, 10\n",
|
||
"lr, num_epochs, device = 0.005, 250, d2l.try_gpu()\n",
|
||
"train_iter, src_vocab, tgt_vocab = d2l.load_data_nmt(batch_size, num_steps)\n",
|
||
"encoder = d2l.Seq2SeqEncoder(\n",
|
||
"len(src_vocab), embed_size, num_hiddens, num_layers, dropout)\n",
|
||
"decoder = Seq2SeqAttentionDecoder(\n",
|
||
"len(tgt_vocab), embed_size, num_hiddens, num_layers, dropout)\n",
|
||
"net = d2l.EncoderDecoder(encoder, decoder)\n",
|
||
"d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)"
|
||
],
|
||
"id": "5f5cedf74b97bd12",
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"loss 0.019, 17862.0 tokens/sec on cuda:0\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
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"text/plain": [
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||
],
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],
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"execution_count": 29
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},
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"metadata": {
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}
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"cell_type": "code",
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"source": [
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"engs = ['go .', \"i lost .\", 'he\\'s calm .', 'i\\'m home .']\n",
|
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"fras = ['va !', 'j\\'ai perdu .', 'il est calme .', 'je suis chez moi .']\n",
|
||
"for eng, fra in zip(engs, fras):\n",
|
||
" translation, dec_attention_weight_seq = d2l.predict_seq2seq(\n",
|
||
" net, eng, src_vocab, tgt_vocab, num_steps, device, True)\n",
|
||
" print(f'{eng} => {translation}, ',\n",
|
||
" f'bleu {d2l.bleu(translation, fra, k=2):.3f}')"
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],
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"id": "ddaed8bd284ee01b",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"go . => va !, bleu 1.000\n",
|
||
"i lost . => j'ai perdu ., bleu 1.000\n",
|
||
"he's calm . => il est <unk> ., bleu 0.658\n",
|
||
"i'm home . => je suis chez moi ., bleu 1.000\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 30
|
||
},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-05-29T15:20:38.342057516Z",
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}
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},
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"cell_type": "code",
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"source": [
|
||
"class MultiHeadAttention(nn.Module):\n",
|
||
" def __init__(self, key_size, query_size, value_size, num_hiddens,\n",
|
||
" num_heads, dropout, bias=False, **kwargs):\n",
|
||
" super(MultiHeadAttention, self).__init__(**kwargs)\n",
|
||
" self.num_heads = num_heads\n",
|
||
" self.attention = d2l.DotProductAttention(dropout)\n",
|
||
" self.W_q = nn.Linear(query_size, num_hiddens, bias=bias)\n",
|
||
" self.W_k = nn.Linear(key_size, num_hiddens, bias=bias)\n",
|
||
" self.W_v = nn.Linear(value_size, num_hiddens, bias=bias)\n",
|
||
" self.W_o = nn.Linear(num_hiddens, num_hiddens, bias=bias)\n",
|
||
" def forward(self, queries, keys, values, valid_lens):\n",
|
||
" # 1. 线性投影 + 变换形状以分割多头\n",
|
||
" queries = transpose_qkv(self.W_q(queries), self.num_heads)\n",
|
||
" keys = transpose_qkv(self.W_k(keys), self.num_heads)\n",
|
||
" values = transpose_qkv(self.W_v(values), self.num_heads)\n",
|
||
"\n",
|
||
" # 2. 处理有效长度掩码（valid_lens）以适配多头\n",
|
||
" if valid_lens is not None:\n",
|
||
" valid_lens = torch.repeat_interleave(valid_lens, repeats=self.num_heads, dim=0)\n",
|
||
"\n",
|
||
" # 3. 计算注意力（每个头独立计算）\n",
|
||
" output = self.attention(queries, keys, values, valid_lens)\n",
|
||
"\n",
|
||
" # 4. 合并多头，并通过输出线性层\n",
|
||
" output_concat = transpose_output(output, self.num_heads)\n",
|
||
" return self.W_o(output_concat)\n",
|
||
"def transpose_qkv(X, num_heads):\n",
|
||
" \"\"\"为了多注意力头的并行计算而变换形状\"\"\"\n",
|
||
" # 输入X的形状:(batch_size，查询或者“键－值”对的个数，num_hiddens)\n",
|
||
" # 输出X的形状:(batch_size，查询或者“键－值”对的个数，num_heads，\n",
|
||
" # num_hiddens/num_heads)\n",
|
||
" X = X.reshape(X.shape[0], X.shape[1], num_heads, -1)\n",
|
||
" # 输出X的形状:(batch_size，num_heads，查询或者“键－值”对的个数,\n",
|
||
" # num_hiddens/num_heads)\n",
|
||
" X = X.permute(0, 2, 1, 3)\n",
|
||
" # 最终输出的形状:(batch_size*num_heads,查询或者“键－值”对的个数,\n",
|
||
" # num_hiddens/num_heads)\n",
|
||
" return X.reshape(-1, X.shape[2], X.shape[3])\n",
|
||
"\n",
|
||
"def transpose_output(X, num_heads):\n",
|
||
" \"\"\"逆转transpose_qkv函数的操作\"\"\"\n",
|
||
" X = X.reshape(-1, num_heads, X.shape[1], X.shape[2])\n",
|
||
" X = X.permute(0, 2, 1, 3)\n",
|
||
" return X.reshape(X.shape[0], X.shape[1], -1)\n"
|
||
],
|
||
"id": "13d19f8b5048c74d",
|
||
"outputs": [],
|
||
"execution_count": 32
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-05-29T15:20:39.251763154Z",
|
||
"start_time": "2026-05-29T15:20:39.201715213Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"num_hiddens, num_heads = 100, 5\n",
|
||
"attention = MultiHeadAttention(num_hiddens, num_hiddens, num_hiddens,\n",
|
||
"num_hiddens, num_heads, 0.5)\n",
|
||
"attention.eval()"
|
||
],
|
||
"id": "b25c661a511d7763",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"MultiHeadAttention(\n",
|
||
" (attention): DotProductAttention(\n",
|
||
" (dropout): Dropout(p=0.5, inplace=False)\n",
|
||
" )\n",
|
||
" (W_q): Linear(in_features=100, out_features=100, bias=False)\n",
|
||
" (W_k): Linear(in_features=100, out_features=100, bias=False)\n",
|
||
" (W_v): Linear(in_features=100, out_features=100, bias=False)\n",
|
||
" (W_o): Linear(in_features=100, out_features=100, bias=False)\n",
|
||
")"
|
||
]
|
||
},
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 33
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-05-29T15:20:40.455647106Z",
|
||
"start_time": "2026-05-29T15:20:40.307682855Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"batch_size, num_queries = 2, 4\n",
|
||
"num_kvpairs, valid_lens = 6, torch.tensor([3, 2])\n",
|
||
"X = torch.ones((batch_size, num_queries, num_hiddens))\n",
|
||
"Y = torch.ones((batch_size, num_kvpairs, num_hiddens))\n",
|
||
"attention(X, Y, Y, valid_lens).shape"
|
||
],
|
||
"id": "49877c929741ec94",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"torch.Size([2, 4, 100])"
|
||
]
|
||
},
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 34
|
||
},
|
||
{
|
||
"metadata": {},
|
||
"cell_type": "code",
|
||
"outputs": [],
|
||
"execution_count": null,
|
||
"source": "",
|
||
"id": "b8a10576c7789750"
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
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"language": "python",
|
||
"name": "python3"
|
||
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|
||
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||
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||
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