{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e90392f7",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# The Transformer Architecture\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee18893c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:06.687415Z",
     "iopub.status.busy": "2023-08-18T19:50:06.687094Z",
     "iopub.status.idle": "2023-08-18T19:50:09.889628Z",
     "shell.execute_reply": "2023-08-18T19:50:09.888444Z"
    },
    "origin_pos": 3,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "533fdb62",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Positionwise Feed-Forward Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "623f67ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:09.894092Z",
     "iopub.status.busy": "2023-08-18T19:50:09.893416Z",
     "iopub.status.idle": "2023-08-18T19:50:09.899737Z",
     "shell.execute_reply": "2023-08-18T19:50:09.898347Z"
    },
    "origin_pos": 8,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class PositionWiseFFN(nn.Module):  \n",
    "    \"\"\"The positionwise feed-forward network.\"\"\"\n",
    "    def __init__(self, ffn_num_hiddens, ffn_num_outputs):\n",
    "        super().__init__()\n",
    "        self.dense1 = nn.LazyLinear(ffn_num_hiddens)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dense2 = nn.LazyLinear(ffn_num_outputs)\n",
    "\n",
    "    def forward(self, X):\n",
    "        return self.dense2(self.relu(self.dense1(X)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4b75c73",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The innermost dimension\n",
    "of a tensor changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c462f39f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:09.906345Z",
     "iopub.status.busy": "2023-08-18T19:50:09.905327Z",
     "iopub.status.idle": "2023-08-18T19:50:09.920436Z",
     "shell.execute_reply": "2023-08-18T19:50:09.919542Z"
    },
    "origin_pos": 13,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.6300,  0.7739,  0.0278,  0.2508, -0.0519,  0.4881, -0.4105,  0.5163],\n",
       "        [ 0.6300,  0.7739,  0.0278,  0.2508, -0.0519,  0.4881, -0.4105,  0.5163],\n",
       "        [ 0.6300,  0.7739,  0.0278,  0.2508, -0.0519,  0.4881, -0.4105,  0.5163]],\n",
       "       grad_fn=<SelectBackward0>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ffn = PositionWiseFFN(4, 8)\n",
    "ffn.eval()\n",
    "ffn(torch.ones((2, 3, 4)))[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07085efa",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Compares the normalization across different dimensions\n",
    "by layer normalization and batch normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "81c95717",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:09.926398Z",
     "iopub.status.busy": "2023-08-18T19:50:09.924518Z",
     "iopub.status.idle": "2023-08-18T19:50:09.937855Z",
     "shell.execute_reply": "2023-08-18T19:50:09.936657Z"
    },
    "origin_pos": 18,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer norm: tensor([[-1.0000,  1.0000],\n",
      "        [-1.0000,  1.0000]], grad_fn=<NativeLayerNormBackward0>) \n",
      "batch norm: tensor([[-1.0000, -1.0000],\n",
      "        [ 1.0000,  1.0000]], grad_fn=<NativeBatchNormBackward0>)\n"
     ]
    }
   ],
   "source": [
    "ln = nn.LayerNorm(2)\n",
    "bn = nn.LazyBatchNorm1d()\n",
    "X = torch.tensor([[1, 2], [2, 3]], dtype=torch.float32)\n",
    "print('layer norm:', ln(X), '\\nbatch norm:', bn(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4308349f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Using a residual connection followed by layer normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "331f12e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:09.941811Z",
     "iopub.status.busy": "2023-08-18T19:50:09.941163Z",
     "iopub.status.idle": "2023-08-18T19:50:09.948019Z",
     "shell.execute_reply": "2023-08-18T19:50:09.946884Z"
    },
    "origin_pos": 23,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class AddNorm(nn.Module):  \n",
    "    \"\"\"The residual connection followed by layer normalization.\"\"\"\n",
    "    def __init__(self, norm_shape, dropout):\n",
    "        super().__init__()\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.ln = nn.LayerNorm(norm_shape)\n",
    "\n",
    "    def forward(self, X, Y):\n",
    "        return self.ln(self.dropout(Y) + X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b454c0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The output tensor also has the same shape after the addition operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d3835d18",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:09.951769Z",
     "iopub.status.busy": "2023-08-18T19:50:09.951126Z",
     "iopub.status.idle": "2023-08-18T19:50:09.957096Z",
     "shell.execute_reply": "2023-08-18T19:50:09.956115Z"
    },
    "origin_pos": 28,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "add_norm = AddNorm(4, 0.5)\n",
    "shape = (2, 3, 4)\n",
    "d2l.check_shape(add_norm(torch.ones(shape), torch.ones(shape)), shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39166416",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "A single layer within the encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2c7cce60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:09.960576Z",
     "iopub.status.busy": "2023-08-18T19:50:09.960031Z",
     "iopub.status.idle": "2023-08-18T19:50:09.965982Z",
     "shell.execute_reply": "2023-08-18T19:50:09.965069Z"
    },
    "origin_pos": 33,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class TransformerEncoderBlock(nn.Module):  \n",
    "    \"\"\"The Transformer encoder block.\"\"\"\n",
    "    def __init__(self, num_hiddens, ffn_num_hiddens, num_heads, dropout,\n",
    "                 use_bias=False):\n",
    "        super().__init__()\n",
    "        self.attention = d2l.MultiHeadAttention(num_hiddens, num_heads,\n",
    "                                                dropout, use_bias)\n",
    "        self.addnorm1 = AddNorm(num_hiddens, dropout)\n",
    "        self.ffn = PositionWiseFFN(ffn_num_hiddens, num_hiddens)\n",
    "        self.addnorm2 = AddNorm(num_hiddens, dropout)\n",
    "\n",
    "    def forward(self, X, valid_lens):\n",
    "        Y = self.addnorm1(X, self.attention(X, X, X, valid_lens))\n",
    "        return self.addnorm2(Y, self.ffn(Y))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e688bde",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "No layer in the Transformer encoder\n",
    "changes the shape of its input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9aefd8d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:09.969308Z",
     "iopub.status.busy": "2023-08-18T19:50:09.968775Z",
     "iopub.status.idle": "2023-08-18T19:50:09.982374Z",
     "shell.execute_reply": "2023-08-18T19:50:09.981506Z"
    },
    "origin_pos": 38,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "X = torch.ones((2, 100, 24))\n",
    "valid_lens = torch.tensor([3, 2])\n",
    "encoder_blk = TransformerEncoderBlock(24, 48, 8, 0.5)\n",
    "encoder_blk.eval()\n",
    "d2l.check_shape(encoder_blk(X, valid_lens), X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "691630cf",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Transformer encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bdcabb11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:09.986032Z",
     "iopub.status.busy": "2023-08-18T19:50:09.985758Z",
     "iopub.status.idle": "2023-08-18T19:50:09.993370Z",
     "shell.execute_reply": "2023-08-18T19:50:09.992550Z"
    },
    "origin_pos": 43,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class TransformerEncoder(d2l.Encoder):  \n",
    "    \"\"\"The Transformer encoder.\"\"\"\n",
    "    def __init__(self, vocab_size, num_hiddens, ffn_num_hiddens,\n",
    "                 num_heads, num_blks, dropout, use_bias=False):\n",
    "        super().__init__()\n",
    "        self.num_hiddens = num_hiddens\n",
    "        self.embedding = nn.Embedding(vocab_size, num_hiddens)\n",
    "        self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)\n",
    "        self.blks = nn.Sequential()\n",
    "        for i in range(num_blks):\n",
    "            self.blks.add_module(\"block\"+str(i), TransformerEncoderBlock(\n",
    "                num_hiddens, ffn_num_hiddens, num_heads, dropout, use_bias))\n",
    "\n",
    "    def forward(self, X, valid_lens):\n",
    "        X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))\n",
    "        self.attention_weights = [None] * len(self.blks)\n",
    "        for i, blk in enumerate(self.blks):\n",
    "            X = blk(X, valid_lens)\n",
    "            self.attention_weights[\n",
    "                i] = blk.attention.attention.attention_weights\n",
    "        return X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "424c1cc5",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Create a two-layer Transformer encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e09106b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:09.996457Z",
     "iopub.status.busy": "2023-08-18T19:50:09.996186Z",
     "iopub.status.idle": "2023-08-18T19:50:10.014181Z",
     "shell.execute_reply": "2023-08-18T19:50:10.013041Z"
    },
    "origin_pos": 48,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "encoder = TransformerEncoder(200, 24, 48, 8, 2, 0.5)\n",
    "d2l.check_shape(encoder(torch.ones((2, 100), dtype=torch.long), valid_lens),\n",
    "                (2, 100, 24))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "953a02ea",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The Transformer decoder\n",
    "is composed of multiple identical layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "23664727",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:10.017719Z",
     "iopub.status.busy": "2023-08-18T19:50:10.017425Z",
     "iopub.status.idle": "2023-08-18T19:50:10.027060Z",
     "shell.execute_reply": "2023-08-18T19:50:10.026020Z"
    },
    "origin_pos": 53,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class TransformerDecoderBlock(nn.Module):\n",
    "    def __init__(self, num_hiddens, ffn_num_hiddens, num_heads, dropout, i):\n",
    "        super().__init__()\n",
    "        self.i = i\n",
    "        self.attention1 = d2l.MultiHeadAttention(num_hiddens, num_heads,\n",
    "                                                 dropout)\n",
    "        self.addnorm1 = AddNorm(num_hiddens, dropout)\n",
    "        self.attention2 = d2l.MultiHeadAttention(num_hiddens, num_heads,\n",
    "                                                 dropout)\n",
    "        self.addnorm2 = AddNorm(num_hiddens, dropout)\n",
    "        self.ffn = PositionWiseFFN(ffn_num_hiddens, num_hiddens)\n",
    "        self.addnorm3 = AddNorm(num_hiddens, dropout)\n",
    "\n",
    "    def forward(self, X, state):\n",
    "        enc_outputs, enc_valid_lens = state[0], state[1]\n",
    "        if state[2][self.i] is None:\n",
    "            key_values = X\n",
    "        else:\n",
    "            key_values = torch.cat((state[2][self.i], X), dim=1)\n",
    "        state[2][self.i] = key_values\n",
    "        if self.training:\n",
    "            batch_size, num_steps, _ = X.shape\n",
    "            dec_valid_lens = torch.arange(\n",
    "                1, num_steps + 1, device=X.device).repeat(batch_size, 1)\n",
    "        else:\n",
    "            dec_valid_lens = None\n",
    "        X2 = self.attention1(X, key_values, key_values, dec_valid_lens)\n",
    "        Y = self.addnorm1(X, X2)\n",
    "        Y2 = self.attention2(Y, enc_outputs, enc_outputs, enc_valid_lens)\n",
    "        Z = self.addnorm2(Y, Y2)\n",
    "        return self.addnorm3(Z, self.ffn(Z)), state"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1025f42",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The feature dimension (`num_hiddens`) of the decoder is\n",
    "the same as that of the encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1f487464",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:10.030357Z",
     "iopub.status.busy": "2023-08-18T19:50:10.030070Z",
     "iopub.status.idle": "2023-08-18T19:50:10.048172Z",
     "shell.execute_reply": "2023-08-18T19:50:10.046972Z"
    },
    "origin_pos": 58,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "decoder_blk = TransformerDecoderBlock(24, 48, 8, 0.5, 0)\n",
    "X = torch.ones((2, 100, 24))\n",
    "state = [encoder_blk(X, valid_lens), valid_lens, [None]]\n",
    "d2l.check_shape(decoder_blk(X, state)[0], X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab792ba3",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Construct the entire Transformer decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38ebb1e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:10.051657Z",
     "iopub.status.busy": "2023-08-18T19:50:10.051318Z",
     "iopub.status.idle": "2023-08-18T19:50:10.061485Z",
     "shell.execute_reply": "2023-08-18T19:50:10.060579Z"
    },
    "origin_pos": 63,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class TransformerDecoder(d2l.AttentionDecoder):\n",
    "    def __init__(self, vocab_size, num_hiddens, ffn_num_hiddens, num_heads,\n",
    "                 num_blks, dropout):\n",
    "        super().__init__()\n",
    "        self.num_hiddens = num_hiddens\n",
    "        self.num_blks = num_blks\n",
    "        self.embedding = nn.Embedding(vocab_size, num_hiddens)\n",
    "        self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)\n",
    "        self.blks = nn.Sequential()\n",
    "        for i in range(num_blks):\n",
    "            self.blks.add_module(\"block\"+str(i), TransformerDecoderBlock(\n",
    "                num_hiddens, ffn_num_hiddens, num_heads, dropout, i))\n",
    "        self.dense = nn.LazyLinear(vocab_size)\n",
    "\n",
    "    def init_state(self, enc_outputs, enc_valid_lens):\n",
    "        return [enc_outputs, enc_valid_lens, [None] * self.num_blks]\n",
    "\n",
    "    def forward(self, X, state):\n",
    "        X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))\n",
    "        self._attention_weights = [[None] * len(self.blks) for _ in range (2)]\n",
    "        for i, blk in enumerate(self.blks):\n",
    "            X, state = blk(X, state)\n",
    "            self._attention_weights[0][\n",
    "                i] = blk.attention1.attention.attention_weights\n",
    "            self._attention_weights[1][\n",
    "                i] = blk.attention2.attention.attention_weights\n",
    "        return self.dense(X), state\n",
    "\n",
    "    @property\n",
    "    def attention_weights(self):\n",
    "        return self._attention_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d905f877",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "74f2da96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:10.065769Z",
     "iopub.status.busy": "2023-08-18T19:50:10.064767Z",
     "iopub.status.idle": "2023-08-18T19:50:42.759965Z",
     "shell.execute_reply": "2023-08-18T19:50:42.758647Z"
    },
    "origin_pos": 67,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
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       "<Figure size 350x250 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = d2l.MTFraEng(batch_size=128)\n",
    "num_hiddens, num_blks, dropout = 256, 2, 0.2\n",
    "ffn_num_hiddens, num_heads = 64, 4\n",
    "encoder = TransformerEncoder(\n",
    "    len(data.src_vocab), num_hiddens, ffn_num_hiddens, num_heads,\n",
    "    num_blks, dropout)\n",
    "decoder = TransformerDecoder(\n",
    "    len(data.tgt_vocab), num_hiddens, ffn_num_hiddens, num_heads,\n",
    "    num_blks, dropout)\n",
    "model = d2l.Seq2Seq(encoder, decoder, tgt_pad=data.tgt_vocab['<pad>'],\n",
    "                    lr=0.001)\n",
    "trainer = d2l.Trainer(max_epochs=30, gradient_clip_val=1, num_gpus=1)\n",
    "trainer.fit(model, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "871837ed",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Translate a few English sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "06e5e238",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:42.765441Z",
     "iopub.status.busy": "2023-08-18T19:50:42.764512Z",
     "iopub.status.idle": "2023-08-18T19:50:42.852261Z",
     "shell.execute_reply": "2023-08-18T19:50:42.850805Z"
    },
    "origin_pos": 69,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "go . => ['va', '!'], bleu,1.000\n",
      "i lost . => ['je', 'perdu', '.'], bleu,0.687\n",
      "he's calm . => ['il', 'est', 'mouillé', '.'], bleu,0.658\n",
      "i'm home . => ['je', 'suis', 'chez', 'moi', '.'], bleu,1.000\n"
     ]
    }
   ],
   "source": [
    "engs = ['go .', 'i lost .', 'he\\'s calm .', 'i\\'m home .']\n",
    "fras = ['va !', 'j\\'ai perdu .', 'il est calme .', 'je suis chez moi .']\n",
    "preds, _ = model.predict_step(\n",
    "    data.build(engs, fras), d2l.try_gpu(), data.num_steps)\n",
    "for en, fr, p in zip(engs, fras, preds):\n",
    "    translation = []\n",
    "    for token in data.tgt_vocab.to_tokens(p):\n",
    "        if token == '<eos>':\n",
    "            break\n",
    "        translation.append(token)\n",
    "    print(f'{en} => {translation}, bleu,'\n",
    "          f'{d2l.bleu(\" \".join(translation), fr, k=2):.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f7fb206",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Visualize the Transformer attention weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "520c51a5",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2023-08-18T19:50:42.930241Z",
     "iopub.status.idle": "2023-08-18T19:50:44.499507Z",
     "shell.execute_reply": "2023-08-18T19:50:44.498627Z"
    },
    "origin_pos": 75,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
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    "_, dec_attention_weights = model.predict_step(\n",
    "    data.build([engs[-1]], [fras[-1]]), d2l.try_gpu(), data.num_steps, True)\n",
    "enc_attention_weights = torch.cat(model.encoder.attention_weights, 0)\n",
    "shape = (num_blks, num_heads, -1, data.num_steps)\n",
    "enc_attention_weights = enc_attention_weights.reshape(shape)\n",
    "d2l.check_shape(enc_attention_weights,\n",
    "                (num_blks, num_heads, data.num_steps, data.num_steps))\n",
    "\n",
    "d2l.show_heatmaps(\n",
    "    enc_attention_weights.cpu(), xlabel='Key positions',\n",
    "    ylabel='Query positions', titles=['Head %d' % i for i in range(1, 5)],\n",
    "    figsize=(7, 3.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f81b4e58",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "To visualize the decoder self-attention weights and the encoder--decoder attention weights,\n",
    "we need more data manipulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8430c053",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:44.533271Z",
     "iopub.status.busy": "2023-08-18T19:50:44.532389Z",
     "iopub.status.idle": "2023-08-18T19:50:45.954406Z",
     "shell.execute_reply": "2023-08-18T19:50:45.953261Z"
    },
    "origin_pos": 83,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
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   "source": [
    "dec_attention_weights_2d = [head[0].tolist()\n",
    "                            for step in dec_attention_weights\n",
    "                            for attn in step for blk in attn for head in blk]\n",
    "dec_attention_weights_filled = torch.tensor(\n",
    "    pd.DataFrame(dec_attention_weights_2d).fillna(0.0).values)\n",
    "shape = (-1, 2, num_blks, num_heads, data.num_steps)\n",
    "dec_attention_weights = dec_attention_weights_filled.reshape(shape)\n",
    "dec_self_attention_weights, dec_inter_attention_weights = \\\n",
    "    dec_attention_weights.permute(1, 2, 3, 0, 4)\n",
    "\n",
    "d2l.check_shape(dec_self_attention_weights,\n",
    "                (num_blks, num_heads, data.num_steps, data.num_steps))\n",
    "d2l.check_shape(dec_inter_attention_weights,\n",
    "                (num_blks, num_heads, data.num_steps, data.num_steps))\n",
    "\n",
    "d2l.show_heatmaps(\n",
    "    dec_self_attention_weights[:, :, :, :],\n",
    "    xlabel='Key positions', ylabel='Query positions',\n",
    "    titles=['Head %d' % i for i in range(1, 5)], figsize=(7, 3.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "527e9143",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "No query from the output sequence\n",
    "attends to those padding tokens from the input sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1c0b1dfe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T19:50:45.958174Z",
     "iopub.status.busy": "2023-08-18T19:50:45.957587Z",
     "iopub.status.idle": "2023-08-18T19:50:47.397366Z",
     "shell.execute_reply": "2023-08-18T19:50:47.396481Z"
    },
    "origin_pos": 85,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
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    "d2l.show_heatmaps(\n",
    "    dec_inter_attention_weights, xlabel='Key positions',\n",
    "    ylabel='Query positions', titles=['Head %d' % i for i in range(1, 5)],\n",
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