From 0d517e98b94e77ff453ec3b710d4418307b44f2a Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Wed, 13 Mar 2024 08:37:54 -0500
Subject: [PATCH] update

---
 .../2.pdf                                     |  Bin 16780 -> 0 bytes
 .../mha-implementations-Copy1.ipynb           |  850 -------
 .../mha-implementations.ipynb                 | 1996 ++++++++---------
 3 files changed, 998 insertions(+), 1848 deletions(-)
 delete mode 100644 ch03/02_bonus_efficient-multihead-attention/2.pdf
 delete mode 100644 ch03/02_bonus_efficient-multihead-attention/mha-implementations-Copy1.ipynb

diff --git a/ch03/02_bonus_efficient-multihead-attention/2.pdf b/ch03/02_bonus_efficient-multihead-attention/2.pdf
deleted file mode 100644
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diff --git a/ch03/02_bonus_efficient-multihead-attention/mha-implementations-Copy1.ipynb b/ch03/02_bonus_efficient-multihead-attention/mha-implementations-Copy1.ipynb
deleted file mode 100644
index 41a4801..0000000
--- a/ch03/02_bonus_efficient-multihead-attention/mha-implementations-Copy1.ipynb
+++ /dev/null
@@ -1,850 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "6f678e62-7bcb-4405-86ae-dce94f494303",
-   "metadata": {
-    "id": "6f678e62-7bcb-4405-86ae-dce94f494303"
-   },
-   "source": [
-    "# Efficient Multi-Head Attention Implementations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b742938a-4bfc-4527-a1f1-d5963508967d",
-   "metadata": {
-    "id": "b742938a-4bfc-4527-a1f1-d5963508967d"
-   },
-   "source": [
-    "This code notebook compares different ways to implement causal multi-head attention used in decoder-style LLMs like GPT, Llama, etc."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "7898551e-f582-48ac-9f66-3632abe2a93f",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "7898551e-f582-48ac-9f66-3632abe2a93f",
-    "outputId": "7d088260-3fa1-44f2-bd65-2a46e289f9d4"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PyTorch version: 2.1.0\n",
-      "Running on cpu\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "\n",
-    "torch.manual_seed(123)\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(f\"PyTorch version: {torch.__version__}\")\n",
-    "print(f\"Running on {device}\")\n",
-    "\n",
-    "batch_size = 8\n",
-    "context_len = 1024\n",
-    "embed_dim = 768\n",
-    "embeddings = torch.randn((batch_size, context_len, embed_dim), device=device)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2f9bb1b6-a1e5-4e0a-884d-0f31b374a8d6",
-   "metadata": {
-    "id": "2f9bb1b6-a1e5-4e0a-884d-0f31b374a8d6"
-   },
-   "source": [
-    "## 1) CausalAttention MHA wrapper class from chapter 3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "297c93ed-aec0-4896-bb89-42c4b294d3d1",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "297c93ed-aec0-4896-bb89-42c4b294d3d1",
-    "outputId": "f8a33752-2cd6-4101-8feb-9d1699984719"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([8, 1024, 768])\n"
-     ]
-    }
-   ],
-   "source": [
-    "from ch03 import MultiHeadAttentionWrapper as Ch03_MHA_Wrapper\n",
-    "\n",
-    "mha_ch03_wrapper = Ch03_MHA_Wrapper(\n",
-    "    d_in=embed_dim,\n",
-    "    d_out=embed_dim//12,\n",
-    "    block_size=context_len,\n",
-    "    dropout=0.0,\n",
-    "    num_heads=12,\n",
-    "    qkv_bias=False\n",
-    ").to(device)\n",
-    "\n",
-    "out = mha_ch03_wrapper(embeddings)\n",
-    "print(out.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21930804-b327-40b1-8e63-94dcad39ce7b",
-   "metadata": {
-    "id": "21930804-b327-40b1-8e63-94dcad39ce7b"
-   },
-   "source": [
-    "## 2) The multi-head attention class from chapter 3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "4ee6a61b-d25c-4a0c-8a59-f285544e3710",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "4ee6a61b-d25c-4a0c-8a59-f285544e3710",
-    "outputId": "b704a040-3547-422c-ecda-df9982a2da35"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([8, 1024, 768])\n"
-     ]
-    }
-   ],
-   "source": [
-    "from ch03 import MultiHeadAttention as Ch03_MHA\n",
-    "\n",
-    "mha_ch03 = Ch03_MHA(\n",
-    "    d_in=embed_dim,\n",
-    "    d_out=embed_dim,\n",
-    "    block_size=context_len,\n",
-    "    dropout=0.0,\n",
-    "    num_heads=12,\n",
-    "    qkv_bias=False\n",
-    ").to(device)\n",
-    "\n",
-    "out = mha_ch03(embeddings)\n",
-    "print(out.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "73cd11da-ea3b-4081-b483-c4965dfefbc4",
-   "metadata": {
-    "id": "73cd11da-ea3b-4081-b483-c4965dfefbc4"
-   },
-   "source": [
-    "## 3) An alternative multi-head attention with combined weights"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1fa1a5ea-eaff-4d2d-aaf0-b34cdb6fd4dd",
-   "metadata": {
-    "id": "1fa1a5ea-eaff-4d2d-aaf0-b34cdb6fd4dd"
-   },
-   "source": [
-    "- The code for the `MultiHeadAttentionAlt` class below is based on code that was kindly shared by [Rayed Bin Wahed](https://github.com/rasbt/LLMs-from-scratch/discussions/51)\n",
-    "- The main difference between the `MultiHeadAttentionAlt` class and the `MultiHeadAttention` class used in chapter 3 is that `MultiHeadAttentionAlt` uses a single weight matrix, `self.qkv = nn.Linear(d_in, 3 * d_out, bias=qkv_bias)` instead of separate weight matrices:\n",
-    "\n",
-    "  - `self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)`\n",
-    "  - `self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)`\n",
-    "  - `self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)`\n",
-    "\n",
-    "- Here, `self.qkv` combines all three weight matrices `self.W_query`, `self.W_key`, and `self.W_value` to carry out the query, key, and value computation in a single step\n",
-    "- Using `q, k, v = qkv.unbind(0)`, we obtain the individual query, key, and value tensors, which are then used similarly to the query, key, and value tensors in the `MultiHeadAttention` class in chapter 3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9a6bd0a2-f27c-4602-afa0-c96cd295c1a6",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "9a6bd0a2-f27c-4602-afa0-c96cd295c1a6",
-    "outputId": "5d948671-176f-4633-bede-97767e36becc"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([8, 1024, 768])\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch.nn as nn\n",
-    "\n",
-    "\n",
-    "class MultiHeadAttentionCombinedQKV(nn.Module):\n",
-    "    def __init__(self, d_in, d_out, num_heads, block_size, dropout=0.0, qkv_bias=False):\n",
-    "        super().__init__()\n",
-    "\n",
-    "        assert d_out % num_heads == 0, \"embed_dim is indivisible by num_heads\"\n",
-    "\n",
-    "        self.num_heads = num_heads\n",
-    "        self.block_size = block_size\n",
-    "        self.head_dim = d_out // num_heads\n",
-    "\n",
-    "        self.qkv = nn.Linear(d_in, 3 * d_out, bias=qkv_bias)\n",
-    "        self.proj = nn.Linear(d_in, d_out)\n",
-    "        self.dropout = nn.Dropout(dropout)\n",
-    "\n",
-    "        self.register_buffer(\n",
-    "            \"mask\", torch.triu(torch.ones(block_size, block_size), diagonal=1)\n",
-    "        )\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        batch_size, num_tokens, embed_dim = x.shape\n",
-    "\n",
-    "        # (b, num_tokens, embed_dim) --> (b, num_tokens, 3 * embed_dim)\n",
-    "        qkv = self.qkv(x)\n",
-    "\n",
-    "        # (b, num_tokens, 3 * embed_dim) --> (b, num_tokens, 3, num_heads, head_dim)\n",
-    "        qkv = qkv.reshape(batch_size, num_tokens, 3, self.num_heads, self.head_dim)\n",
-    "\n",
-    "        # (b, num_tokens, 3, num_heads, head_dim) --> (3, b, num_heads, num_tokens, head_dim)\n",
-    "        qkv = qkv.permute(2, 0, 3, 1, 4)\n",
-    "\n",
-    "        # (3, b, num_heads, num_tokens, head_dim) -> 3 times (b, num_head, num_tokens, head_dim)\n",
-    "        queries, keys, values = qkv.unbind(0)\n",
-    "\n",
-    "        # (b, num_heads, num_tokens, head_dim) --> (b, num_heads, num_tokens, num_tokens)\n",
-    "        attn_scores = queries @ keys.transpose(-2, -1)\n",
-    "        attn_scores = attn_scores.masked_fill(\n",
-    "            self.mask.bool()[:num_tokens, :num_tokens], -torch.inf\n",
-    "        )\n",
-    "\n",
-    "        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**-0.5, dim=-1)\n",
-    "        attn_weights = self.dropout(attn_weights)\n",
-    "\n",
-    "        # (b, num_heads, num_tokens, num_tokens) --> (b, num_heads, num_tokens, head_dim)\n",
-    "        context_vec = attn_weights @ values\n",
-    "\n",
-    "        # (b, num_heads, num_tokens, head_dim) --> (b, num_tokens, num_heads, head_dim)\n",
-    "        context_vec = context_vec.transpose(1, 2)\n",
-    "\n",
-    "        # (b, num_tokens, num_heads, head_dim) --> (b, num_tokens, embed_dim)\n",
-    "        context_vec = context_vec.reshape(batch_size, num_tokens, embed_dim)\n",
-    "\n",
-    "        context_vec = self.proj(context_vec)\n",
-    "\n",
-    "        return context_vec\n",
-    "\n",
-    "\n",
-    "mha_combined_qkv = MultiHeadAttentionCombinedQKV(\n",
-    "    d_in=embed_dim,\n",
-    "    d_out=embed_dim,\n",
-    "    block_size=context_len,\n",
-    "    dropout=0.0,\n",
-    "    num_heads=12,\n",
-    "    qkv_bias=False\n",
-    ").to(device)\n",
-    "\n",
-    "out = mha_combined_qkv(embeddings)\n",
-    "print(out.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "48a042d3-ee78-4c29-bf63-d92fe6706632",
-   "metadata": {
-    "id": "48a042d3-ee78-4c29-bf63-d92fe6706632"
-   },
-   "source": [
-    "## 4) Multihead attention with PyTorch's scaled dot product attention"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f78e346f-3b85-44e6-9feb-f01131381148",
-   "metadata": {
-    "id": "f78e346f-3b85-44e6-9feb-f01131381148"
-   },
-   "source": [
-    "- The implementation below uses PyTorch's [`scaled_dot_product_attention`](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) function, which implements a memory-optimized version of self-attention calld [flash attention](https://arxiv.org/abs/2205.14135)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "1b8e5a0d-1f65-4a03-bf6e-723f0cc428f5",
-   "metadata": {
-    "id": "1b8e5a0d-1f65-4a03-bf6e-723f0cc428f5"
-   },
-   "outputs": [],
-   "source": [
-    "class MHAPyTorchScaledDotProduct(nn.Module):\n",
-    "    def __init__(self, d_in, d_out, num_heads, block_size, dropout=0.0, qkv_bias=False):\n",
-    "        super().__init__()\n",
-    "\n",
-    "        assert d_out % num_heads == 0, \"embed_dim is indivisible by num_heads\"\n",
-    "\n",
-    "        self.num_heads = num_heads\n",
-    "        self.block_size = block_size\n",
-    "        self.head_dim = d_out // num_heads\n",
-    "        self.d_out = d_out\n",
-    "\n",
-    "        self.qkv = nn.Linear(d_in, 3 * d_out, bias=qkv_bias)\n",
-    "        self.proj = nn.Linear(d_in, d_out)\n",
-    "        self.dropout = dropout\n",
-    "\n",
-    "        self.register_buffer(\n",
-    "            \"mask\", torch.triu(torch.ones(block_size, block_size), diagonal=1)\n",
-    "        )\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        batch_size, num_tokens, embed_dim = x.shape\n",
-    "\n",
-    "        # (b, num_tokens, embed_dim) --> (b, num_tokens, 3 * embed_dim)\n",
-    "        qkv = self.qkv(x)\n",
-    "\n",
-    "        # (b, num_tokens, 3 * embed_dim) --> (b, num_tokens, 3, num_heads, head_dim)\n",
-    "        qkv = qkv.reshape(batch_size, num_tokens, 3, self.num_heads, self.head_dim)\n",
-    "\n",
-    "        # (b, num_tokens, 3, num_heads, head_dim) --> (3, b, num_heads, num_tokens, head_dim)\n",
-    "        qkv = qkv.permute(2, 0, 3, 1, 4)\n",
-    "\n",
-    "        # (3, b, num_heads, num_tokens, head_dim) -> 3 times (b, num_heads, num_tokens, head_dim)\n",
-    "        queries, keys, values = qkv.unbind(0)\n",
-    "\n",
-    "        use_dropout = 0. if not self.training else self.dropout\n",
-    "        context_vec = nn.functional.scaled_dot_product_attention(\n",
-    "            queries, keys, values, attn_mask=None, dropout_p=use_dropout, is_causal=True)\n",
-    "\n",
-    "        # Combine heads, where self.d_out = self.num_heads * self.head_dim\n",
-    "        context_vec = context_vec.transpose(1, 2).contiguous().view(batch_size, num_tokens, self.d_out)\n",
-    "\n",
-    "        return context_vec"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "fbc8ba92-3471-41cb-b1b2-4c0ef5be392b",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "fbc8ba92-3471-41cb-b1b2-4c0ef5be392b",
-    "outputId": "af9e4855-7f20-4d61-8532-4827df8dfb30"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([8, 1024, 768])\n"
-     ]
-    }
-   ],
-   "source": [
-    "mha_pytorch_scaled = MHAPyTorchScaledDotProduct(\n",
-    "    d_in=embed_dim,\n",
-    "    d_out=embed_dim,\n",
-    "    block_size=context_len,\n",
-    "    dropout=0.0,\n",
-    "    num_heads=12,\n",
-    "    qkv_bias=False\n",
-    ").to(device)\n",
-    "\n",
-    "out = mha_pytorch_scaled(embeddings)\n",
-    "print(out.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "351c318f-4835-4d74-8d58-a070222447c4",
-   "metadata": {
-    "id": "351c318f-4835-4d74-8d58-a070222447c4"
-   },
-   "source": [
-    "## 5) Using PyTorch's torch.nn.MultiheadAttention"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74a6d060-6324-48fa-a35c-cb09f2a48965",
-   "metadata": {
-    "id": "74a6d060-6324-48fa-a35c-cb09f2a48965"
-   },
-   "source": [
-    "- Below, we use PyTorch's [torch.nn.MultiheadAttention](https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html) implementation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3799c7ef-3155-42c6-a829-f95656453ae0",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "3799c7ef-3155-42c6-a829-f95656453ae0",
-    "outputId": "2a085df8-0445-4818-9978-6dc74469f568"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([8, 1024, 768])\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch.nn as nn\n",
-    "\n",
-    "\n",
-    "class MHAPyTorchClass(nn.Module):\n",
-    "    def __init__(self, d_in, d_out, num_heads, block_size, dropout=0.0, qkv_bias=False, need_weights=True):\n",
-    "        super().__init__()\n",
-    "\n",
-    "        self.block_size = block_size\n",
-    "        self.multihead_attn = nn.MultiheadAttention(\n",
-    "            embed_dim=d_out,\n",
-    "            num_heads=num_heads,\n",
-    "            dropout=dropout,\n",
-    "            bias=qkv_bias,\n",
-    "            add_bias_kv=qkv_bias,\n",
-    "            batch_first=True,\n",
-    "        )\n",
-    "\n",
-    "        self.need_weights = need_weights\n",
-    "        self.proj = nn.Linear(d_out, d_out)\n",
-    "        self.register_buffer(\"mask\", torch.triu(torch.ones(block_size, block_size), diagonal=1).bool())\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        batch_size, num_tokens, _ = x.shape\n",
-    "\n",
-    "        # Ensure attn_mask is compatible with expected shape and `batch_first=True`\n",
-    "        # No need to manually adjust for num_heads; ensure it's right for the sequence\n",
-    "        if self.block_size >= num_tokens:\n",
-    "            attn_mask = self.mask[:num_tokens, :num_tokens]\n",
-    "        else:\n",
-    "            attn_mask = self.mask[:self.block_size, :self.block_size]\n",
-    "\n",
-    "        # attn_mask broadcasting will handle batch_size dimension implicitly\n",
-    "        attn_output, _ = self.multihead_attn(\n",
-    "            x, x, x, attn_mask=attn_mask, need_weights=self.need_weights\n",
-    "        )\n",
-    "\n",
-    "        output = self.proj(attn_output)\n",
-    "\n",
-    "        return output\n",
-    "\n",
-    "\n",
-    "mha_pytorch_class_default = MHAPyTorchClass(\n",
-    "    d_in=embed_dim,\n",
-    "    d_out=embed_dim,\n",
-    "    block_size=context_len,\n",
-    "    dropout=0.0,\n",
-    "    num_heads=12,\n",
-    "    qkv_bias=False\n",
-    ").to(device)\n",
-    "\n",
-    "out = mha_pytorch_class_default(embeddings)\n",
-    "print(out.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3953bff-1056-4de2-bfd1-dfccf659eee4",
-   "metadata": {
-    "id": "a3953bff-1056-4de2-bfd1-dfccf659eee4"
-   },
-   "source": [
-    "## 6) Using PyTorch's torch.nn.MultiheadAttention with `scaled_dot_product_attention`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d2164859-31a0-4537-b4fb-27d57675ba77",
-   "metadata": {
-    "id": "d2164859-31a0-4537-b4fb-27d57675ba77"
-   },
-   "source": [
-    "- Set `need_weights` (default `True`) to need_weights=False so that MultiheadAttention uses `scaled_dot_product_attention` [according to the documentation](https://github.com/pytorch/pytorch/blob/71d020262793542974cf13b30f2a9099773f015c/torch/nn/modules/activation.py#L1096)\n",
-    "\n",
-    ">  need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``.\n",
-    "            Set ``need_weights=False`` to use the optimized ``scaled_dot_product_attention``\n",
-    "            and achieve the best performance for MHA.\n",
-    "            Default: ``True``."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "4a4c2afe-5e1f-4bd7-a118-67031176f147",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "4a4c2afe-5e1f-4bd7-a118-67031176f147",
-    "outputId": "234771f4-8a53-4478-8a9b-cf19f79a5e07"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([8, 1024, 768])\n"
-     ]
-    }
-   ],
-   "source": [
-    "mha_pytorch_class_noweights = MHAPyTorchClass(\n",
-    "    d_in=embed_dim,\n",
-    "    d_out=embed_dim,\n",
-    "    block_size=context_len,\n",
-    "    dropout=0.0,\n",
-    "    num_heads=12,\n",
-    "    qkv_bias=False,\n",
-    "    need_weights=False # NEW!\n",
-    ").to(device)\n",
-    "\n",
-    "out = mha_pytorch_class_noweights(embeddings)\n",
-    "print(out.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8877de71-f84f-4f6d-bc87-7552013b6301",
-   "metadata": {
-    "id": "8877de71-f84f-4f6d-bc87-7552013b6301"
-   },
-   "source": [
-    "## Quick speed comparison (M3 Macbook Air CPU)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "a97c0b2e-6593-49d8-98bc-2267b3aa610f",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "a97c0b2e-6593-49d8-98bc-2267b3aa610f",
-    "outputId": "ebe635b2-5c03-4e9b-da3a-951d308acf7b"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "194 ms ± 2.75 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "## 1) CausalAttention MHA wrapper class from chapter 3\n",
-    "%timeit mha_ch03_wrapper(embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "19db9c2c-8e75-431a-8eef-0b4d8284e6e6",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "19db9c2c-8e75-431a-8eef-0b4d8284e6e6",
-    "outputId": "c6e7bcff-661c-45a6-da82-b1e3f89cf761"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "198 ms ± 4.12 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "## 2) The multi-head attention class from chapter 3\n",
-    "%timeit mha_ch03(embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "aa526ee0-7a88-4f34-a49a-f8f97da83779",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "aa526ee0-7a88-4f34-a49a-f8f97da83779",
-    "outputId": "92b634f8-43f8-468f-87a1-bb774b64c212"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "234 ms ± 4.26 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "## 3) An alternative multi-head attention with combined weights\n",
-    "%timeit mha_combined_qkv(embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "cc2b4256-16d8-4c34-9fd0-d4b4af0e60fa",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "cc2b4256-16d8-4c34-9fd0-d4b4af0e60fa",
-    "outputId": "80c6e314-0771-470e-b090-628984ce2d85"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "71.7 ms ± 3.65 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "## 4) Multihead attention with PyTorch's scaled dot product attention\n",
-    "%timeit mha_pytorch_scaled(embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "0f209e70-ebb6-4a1a-b608-1ff42e41c01d",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "0f209e70-ebb6-4a1a-b608-1ff42e41c01d",
-    "outputId": "3cd37b53-04d4-4dd0-9450-6fc8ebaac083"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "211 ms ± 5.31 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "## 5) Using PyTorch's torch.nn.MultiheadAttention\n",
-    "%timeit mha_pytorch_class_default(embeddings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "3f4968c2-8d40-4ab9-8dba-052b4f77d756",
-   "metadata": {
-    "id": "3f4968c2-8d40-4ab9-8dba-052b4f77d756",
-    "outputId": "2e86bdb4-7fa0-4051-b000-4a2b591060a2",
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "207 ms ± 18.3 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "## 6) Using PyTorch's torch.nn.MultiheadAttention disabling `need_weights`\n",
-    "%timeit mha_pytorch_class_noweights(embeddings)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dabc6575-0316-4640-a729-e616d5c17b73",
-   "metadata": {
-    "id": "dabc6575-0316-4640-a729-e616d5c17b73"
-   },
-   "source": [
-    "## Speed comparison (Nvidia A100 GPU) with warmup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "29b63d3d-6d0b-43bb-9c68-d5514dc81000",
-   "metadata": {
-    "id": "29b63d3d-6d0b-43bb-9c68-d5514dc81000"
-   },
-   "outputs": [],
-   "source": [
-    "# CUDA benchmark code shared by Andrei Aksionov\n",
-    "# and based on code from\n",
-    "# https://github.com/cuda-mode/lectures/blob/main/lecture1/pytorch_square.py\n",
-    "\n",
-    "import time\n",
-    "\n",
-    "def time_pytorch_function(func, *input, num_repeats = 100):\n",
-    "    # CUDA IS ASYNC so can't use python time module\n",
-    "    #start = torch.cuda.Event(enable_timing=True)\n",
-    "    #end = torch.cuda.Event(enable_timing=True)\n",
-    "    start = time.time()\n",
-    "    # Warmup\n",
-    "    #for _ in range(5):\n",
-    "    #    func(*input)\n",
-    "    #torch.cuda.synchronize()\n",
-    "\n",
-    "    #start.record()\n",
-    "    for _ in range(num_repeats):\n",
-    "        func(*input)\n",
-    "        #torch.cuda.synchronize()\n",
-    "    #end.record()\n",
-    "    #torch.cuda.synchronize()\n",
-    "    return (time.time()-start) / num_repeats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "CDJAPZaszaqx",
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 489
-    },
-    "id": "CDJAPZaszaqx",
-    "outputId": "f23e9b83-7fd6-4011-9434-0e6934cf762a"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "#embeddings_cuda = embeddings.to(torch.device(\"cuda\"))\n",
-    "\n",
-    "functions = {\n",
-    "    \"1) MHA wrapper class\": mha_ch03_wrapper,\n",
-    "    \"2) MHA Ch03\": mha_ch03,\n",
-    "    \"3) MHA with combined QKV weights\": mha_combined_qkv,\n",
-    "    \"4) MHA with PyTorch scaled_dot_product_attention\": mha_pytorch_scaled,\n",
-    "    \"5) PyTorch MHA class defaults\": mha_pytorch_class_default,\n",
-    "    \"6) PyTorch MHA with need_weights=False\": mha_pytorch_class_noweights\n",
-    "}\n",
-    "execution_times = [time_pytorch_function(fn, embeddings) for name,fn in functions.items()]\n",
-    "\n",
-    "\n",
-    "# Plotting\n",
-    "\n",
-    "# Customize further for dark mode aesthetics\n",
-    "plt.rcParams['figure.facecolor'] = '#121212'  # Dark figure background\n",
-    "plt.rcParams['axes.facecolor'] = '#121212'    # Dark axes background\n",
-    "plt.rcParams['axes.edgecolor'] = 'white'      # White axes border\n",
-    "plt.rcParams['axes.labelcolor'] = 'white'     # White labels\n",
-    "plt.rcParams['text.color'] = 'white'          # White text\n",
-    "plt.rcParams['xtick.color'] = 'white'         # White x ticks\n",
-    "plt.rcParams['ytick.color'] = 'white'         # White y ticks\n",
-    "plt.rcParams['grid.color'] = '#444444'        # Lighter grid lines for contrast\n",
-    "plt.rcParams['lines.linewidth'] = 2           # Thicker plot lines for visibility\n",
-    "plt.rcParams['lines.markersize'] = 8          # Larger markers for visibility\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "bars = plt.bar(functions.keys(), execution_times)\n",
-    "\n",
-    "plt.ylabel('Execution time (ms)')\n",
-    "plt.xticks(rotation=45, ha=\"right\")\n",
-    "\n",
-    "# Calculate new ylim with a margin\n",
-    "max_execution_time = max(execution_times)\n",
-    "upper_ylim = max_execution_time + 0.2 * max_execution_time  # Adding a 20% margin\n",
-    "\n",
-    "plt.ylim(0, upper_ylim)  # Setting new ylim\n",
-    "\n",
-    "# Annotate bars with execution times\n",
-    "for bar in bars:\n",
-    "    yval = bar.get_height()\n",
-    "    plt.text(bar.get_x() + bar.get_width()/2, yval + (0.05 * upper_ylim), round(yval, 2), ha='center', va='bottom')\n",
-    "\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(\"2.pdf\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d3e1137b-9acc-4cc5-bcbf-0e8533839f06",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "gpuType": "A100",
-   "machine_shape": "hm",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/ch03/02_bonus_efficient-multihead-attention/mha-implementations.ipynb b/ch03/02_bonus_efficient-multihead-attention/mha-implementations.ipynb
index 1eda8cc..d3500e1 100644
--- a/ch03/02_bonus_efficient-multihead-attention/mha-implementations.ipynb
+++ b/ch03/02_bonus_efficient-multihead-attention/mha-implementations.ipynb
@@ -1,1000 +1,1000 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "id": "6f678e62-7bcb-4405-86ae-dce94f494303",
-      "metadata": {
-        "id": "6f678e62-7bcb-4405-86ae-dce94f494303"
-      },
-      "source": [
-        "# Efficient Multi-Head Attention Implementations"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "b742938a-4bfc-4527-a1f1-d5963508967d",
-      "metadata": {
-        "id": "b742938a-4bfc-4527-a1f1-d5963508967d"
-      },
-      "source": [
-        "This code notebook compares different ways to implement causal multi-head attention used in decoder-style LLMs like GPT, Llama, etc."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "7898551e-f582-48ac-9f66-3632abe2a93f",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7898551e-f582-48ac-9f66-3632abe2a93f",
-        "outputId": "7d088260-3fa1-44f2-bd65-2a46e289f9d4"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "PyTorch version: 2.2.1+cu121\n",
-            "Running on cuda\n"
-          ]
-        }
-      ],
-      "source": [
-        "import torch\n",
-        "\n",
-        "torch.manual_seed(123)\n",
-        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-        "print(f\"PyTorch version: {torch.__version__}\")\n",
-        "print(f\"Running on {device}\")\n",
-        "\n",
-        "batch_size = 8\n",
-        "context_len = 1024\n",
-        "embed_dim = 768\n",
-        "embeddings = torch.randn((batch_size, context_len, embed_dim), device=device)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "2f9bb1b6-a1e5-4e0a-884d-0f31b374a8d6",
-      "metadata": {
-        "id": "2f9bb1b6-a1e5-4e0a-884d-0f31b374a8d6"
-      },
-      "source": [
-        "## 1) CausalAttention MHA wrapper class from chapter 3"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "297c93ed-aec0-4896-bb89-42c4b294d3d1",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "297c93ed-aec0-4896-bb89-42c4b294d3d1",
-        "outputId": "f8a33752-2cd6-4101-8feb-9d1699984719"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "torch.Size([8, 1024, 768])\n"
-          ]
-        }
-      ],
-      "source": [
-        "from ch03 import MultiHeadAttentionWrapper as Ch03_MHA_Wrapper\n",
-        "\n",
-        "mha_ch03_wrapper = Ch03_MHA_Wrapper(\n",
-        "    d_in=embed_dim,\n",
-        "    d_out=embed_dim//12,\n",
-        "    block_size=context_len,\n",
-        "    dropout=0.0,\n",
-        "    num_heads=12,\n",
-        "    qkv_bias=False\n",
-        ").to(device)\n",
-        "\n",
-        "out = mha_ch03_wrapper(embeddings)\n",
-        "print(out.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "21930804-b327-40b1-8e63-94dcad39ce7b",
-      "metadata": {
-        "id": "21930804-b327-40b1-8e63-94dcad39ce7b"
-      },
-      "source": [
-        "## 2) The multi-head attention class from chapter 3"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "4ee6a61b-d25c-4a0c-8a59-f285544e3710",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "4ee6a61b-d25c-4a0c-8a59-f285544e3710",
-        "outputId": "b704a040-3547-422c-ecda-df9982a2da35"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "torch.Size([8, 1024, 768])\n"
-          ]
-        }
-      ],
-      "source": [
-        "from ch03 import MultiHeadAttention as Ch03_MHA\n",
-        "\n",
-        "mha_ch03 = Ch03_MHA(\n",
-        "    d_in=embed_dim,\n",
-        "    d_out=embed_dim,\n",
-        "    block_size=context_len,\n",
-        "    dropout=0.0,\n",
-        "    num_heads=12,\n",
-        "    qkv_bias=False\n",
-        ").to(device)\n",
-        "\n",
-        "out = mha_ch03(embeddings)\n",
-        "print(out.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "73cd11da-ea3b-4081-b483-c4965dfefbc4",
-      "metadata": {
-        "id": "73cd11da-ea3b-4081-b483-c4965dfefbc4"
-      },
-      "source": [
-        "## 3) An alternative multi-head attention with combined weights"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "1fa1a5ea-eaff-4d2d-aaf0-b34cdb6fd4dd",
-      "metadata": {
-        "id": "1fa1a5ea-eaff-4d2d-aaf0-b34cdb6fd4dd"
-      },
-      "source": [
-        "- The code for the `MultiHeadAttentionAlt` class below is based on code that was kindly shared by [Rayed Bin Wahed](https://github.com/rasbt/LLMs-from-scratch/discussions/51)\n",
-        "- The main difference between the `MultiHeadAttentionAlt` class and the `MultiHeadAttention` class used in chapter 3 is that `MultiHeadAttentionAlt` uses a single weight matrix, `self.qkv = nn.Linear(d_in, 3 * d_out, bias=qkv_bias)` instead of separate weight matrices:\n",
-        "\n",
-        "  - `self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)`\n",
-        "  - `self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)`\n",
-        "  - `self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)`\n",
-        "\n",
-        "- Here, `self.qkv` combines all three weight matrices `self.W_query`, `self.W_key`, and `self.W_value` to carry out the query, key, and value computation in a single step\n",
-        "- Using `q, k, v = qkv.unbind(0)`, we obtain the individual query, key, and value tensors, which are then used similarly to the query, key, and value tensors in the `MultiHeadAttention` class in chapter 3"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "9a6bd0a2-f27c-4602-afa0-c96cd295c1a6",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "9a6bd0a2-f27c-4602-afa0-c96cd295c1a6",
-        "outputId": "5d948671-176f-4633-bede-97767e36becc"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "torch.Size([8, 1024, 768])\n"
-          ]
-        }
-      ],
-      "source": [
-        "import torch.nn as nn\n",
-        "\n",
-        "\n",
-        "class MultiHeadAttentionCombinedQKV(nn.Module):\n",
-        "    def __init__(self, d_in, d_out, num_heads, block_size, dropout=0.0, qkv_bias=False):\n",
-        "        super().__init__()\n",
-        "\n",
-        "        assert d_out % num_heads == 0, \"embed_dim is indivisible by num_heads\"\n",
-        "\n",
-        "        self.num_heads = num_heads\n",
-        "        self.block_size = block_size\n",
-        "        self.head_dim = d_out // num_heads\n",
-        "\n",
-        "        self.qkv = nn.Linear(d_in, 3 * d_out, bias=qkv_bias)\n",
-        "        self.proj = nn.Linear(d_in, d_out)\n",
-        "        self.dropout = nn.Dropout(dropout)\n",
-        "\n",
-        "        self.register_buffer(\n",
-        "            \"mask\", torch.triu(torch.ones(block_size, block_size), diagonal=1)\n",
-        "        )\n",
-        "\n",
-        "    def forward(self, x):\n",
-        "        batch_size, num_tokens, embed_dim = x.shape\n",
-        "\n",
-        "        # (b, num_tokens, embed_dim) --> (b, num_tokens, 3 * embed_dim)\n",
-        "        qkv = self.qkv(x)\n",
-        "\n",
-        "        # (b, num_tokens, 3 * embed_dim) --> (b, num_tokens, 3, num_heads, head_dim)\n",
-        "        qkv = qkv.reshape(batch_size, num_tokens, 3, self.num_heads, self.head_dim)\n",
-        "\n",
-        "        # (b, num_tokens, 3, num_heads, head_dim) --> (3, b, num_heads, num_tokens, head_dim)\n",
-        "        qkv = qkv.permute(2, 0, 3, 1, 4)\n",
-        "\n",
-        "        # (3, b, num_heads, num_tokens, head_dim) -> 3 times (b, num_head, num_tokens, head_dim)\n",
-        "        queries, keys, values = qkv.unbind(0)\n",
-        "\n",
-        "        # (b, num_heads, num_tokens, head_dim) --> (b, num_heads, num_tokens, num_tokens)\n",
-        "        attn_scores = queries @ keys.transpose(-2, -1)\n",
-        "        attn_scores = attn_scores.masked_fill(\n",
-        "            self.mask.bool()[:num_tokens, :num_tokens], -torch.inf\n",
-        "        )\n",
-        "\n",
-        "        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**-0.5, dim=-1)\n",
-        "        attn_weights = self.dropout(attn_weights)\n",
-        "\n",
-        "        # (b, num_heads, num_tokens, num_tokens) --> (b, num_heads, num_tokens, head_dim)\n",
-        "        context_vec = attn_weights @ values\n",
-        "\n",
-        "        # (b, num_heads, num_tokens, head_dim) --> (b, num_tokens, num_heads, head_dim)\n",
-        "        context_vec = context_vec.transpose(1, 2)\n",
-        "\n",
-        "        # (b, num_tokens, num_heads, head_dim) --> (b, num_tokens, embed_dim)\n",
-        "        context_vec = context_vec.reshape(batch_size, num_tokens, embed_dim)\n",
-        "\n",
-        "        context_vec = self.proj(context_vec)\n",
-        "\n",
-        "        return context_vec\n",
-        "\n",
-        "\n",
-        "mha_combined_qkv = MultiHeadAttentionCombinedQKV(\n",
-        "    d_in=embed_dim,\n",
-        "    d_out=embed_dim,\n",
-        "    block_size=context_len,\n",
-        "    dropout=0.0,\n",
-        "    num_heads=12,\n",
-        "    qkv_bias=False\n",
-        ").to(device)\n",
-        "\n",
-        "out = mha_combined_qkv(embeddings)\n",
-        "print(out.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "48a042d3-ee78-4c29-bf63-d92fe6706632",
-      "metadata": {
-        "id": "48a042d3-ee78-4c29-bf63-d92fe6706632"
-      },
-      "source": [
-        "## 4) Multihead attention with PyTorch's scaled dot product attention"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "f78e346f-3b85-44e6-9feb-f01131381148",
-      "metadata": {
-        "id": "f78e346f-3b85-44e6-9feb-f01131381148"
-      },
-      "source": [
-        "- The implementation below uses PyTorch's [`scaled_dot_product_attention`](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) function, which implements a memory-optimized version of self-attention calld [flash attention](https://arxiv.org/abs/2205.14135)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "1b8e5a0d-1f65-4a03-bf6e-723f0cc428f5",
-      "metadata": {
-        "id": "1b8e5a0d-1f65-4a03-bf6e-723f0cc428f5"
-      },
-      "outputs": [],
-      "source": [
-        "class MHAPyTorchScaledDotProduct(nn.Module):\n",
-        "    def __init__(self, d_in, d_out, num_heads, block_size, dropout=0.0, qkv_bias=False):\n",
-        "        super().__init__()\n",
-        "\n",
-        "        assert d_out % num_heads == 0, \"embed_dim is indivisible by num_heads\"\n",
-        "\n",
-        "        self.num_heads = num_heads\n",
-        "        self.block_size = block_size\n",
-        "        self.head_dim = d_out // num_heads\n",
-        "        self.d_out = d_out\n",
-        "\n",
-        "        self.qkv = nn.Linear(d_in, 3 * d_out, bias=qkv_bias)\n",
-        "        self.proj = nn.Linear(d_in, d_out)\n",
-        "        self.dropout = dropout\n",
-        "\n",
-        "        self.register_buffer(\n",
-        "            \"mask\", torch.triu(torch.ones(block_size, block_size), diagonal=1)\n",
-        "        )\n",
-        "\n",
-        "    def forward(self, x):\n",
-        "        batch_size, num_tokens, embed_dim = x.shape\n",
-        "\n",
-        "        # (b, num_tokens, embed_dim) --> (b, num_tokens, 3 * embed_dim)\n",
-        "        qkv = self.qkv(x)\n",
-        "\n",
-        "        # (b, num_tokens, 3 * embed_dim) --> (b, num_tokens, 3, num_heads, head_dim)\n",
-        "        qkv = qkv.reshape(batch_size, num_tokens, 3, self.num_heads, self.head_dim)\n",
-        "\n",
-        "        # (b, num_tokens, 3, num_heads, head_dim) --> (3, b, num_heads, num_tokens, head_dim)\n",
-        "        qkv = qkv.permute(2, 0, 3, 1, 4)\n",
-        "\n",
-        "        # (3, b, num_heads, num_tokens, head_dim) -> 3 times (b, num_heads, num_tokens, head_dim)\n",
-        "        queries, keys, values = qkv.unbind(0)\n",
-        "\n",
-        "        use_dropout = 0. if not self.training else self.dropout\n",
-        "        context_vec = nn.functional.scaled_dot_product_attention(\n",
-        "            queries, keys, values, attn_mask=None, dropout_p=use_dropout, is_causal=True)\n",
-        "\n",
-        "        # Combine heads, where self.d_out = self.num_heads * self.head_dim\n",
-        "        context_vec = context_vec.transpose(1, 2).contiguous().view(batch_size, num_tokens, self.d_out)\n",
-        "\n",
-        "        return context_vec"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "fbc8ba92-3471-41cb-b1b2-4c0ef5be392b",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "fbc8ba92-3471-41cb-b1b2-4c0ef5be392b",
-        "outputId": "af9e4855-7f20-4d61-8532-4827df8dfb30"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "torch.Size([8, 1024, 768])\n"
-          ]
-        }
-      ],
-      "source": [
-        "mha_pytorch_scaled = MHAPyTorchScaledDotProduct(\n",
-        "    d_in=embed_dim,\n",
-        "    d_out=embed_dim,\n",
-        "    block_size=context_len,\n",
-        "    dropout=0.0,\n",
-        "    num_heads=12,\n",
-        "    qkv_bias=False\n",
-        ").to(device)\n",
-        "\n",
-        "out = mha_pytorch_scaled(embeddings)\n",
-        "print(out.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "351c318f-4835-4d74-8d58-a070222447c4",
-      "metadata": {
-        "id": "351c318f-4835-4d74-8d58-a070222447c4"
-      },
-      "source": [
-        "## 5) Using PyTorch's torch.nn.MultiheadAttention"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "74a6d060-6324-48fa-a35c-cb09f2a48965",
-      "metadata": {
-        "id": "74a6d060-6324-48fa-a35c-cb09f2a48965"
-      },
-      "source": [
-        "- Below, we use PyTorch's [torch.nn.MultiheadAttention](https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html) implementation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "id": "3799c7ef-3155-42c6-a829-f95656453ae0",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "3799c7ef-3155-42c6-a829-f95656453ae0",
-        "outputId": "2a085df8-0445-4818-9978-6dc74469f568"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "torch.Size([8, 1024, 768])\n"
-          ]
-        }
-      ],
-      "source": [
-        "import torch.nn as nn\n",
-        "\n",
-        "\n",
-        "class MHAPyTorchClass(nn.Module):\n",
-        "    def __init__(self, d_in, d_out, num_heads, block_size, dropout=0.0, qkv_bias=False, need_weights=True):\n",
-        "        super().__init__()\n",
-        "\n",
-        "        self.block_size = block_size\n",
-        "        self.multihead_attn = nn.MultiheadAttention(\n",
-        "            embed_dim=d_out,\n",
-        "            num_heads=num_heads,\n",
-        "            dropout=dropout,\n",
-        "            bias=qkv_bias,\n",
-        "            add_bias_kv=qkv_bias,\n",
-        "            batch_first=True,\n",
-        "        )\n",
-        "\n",
-        "        self.need_weights = need_weights\n",
-        "        self.proj = nn.Linear(d_out, d_out)\n",
-        "        self.register_buffer(\"mask\", torch.triu(torch.ones(block_size, block_size), diagonal=1).bool())\n",
-        "\n",
-        "    def forward(self, x):\n",
-        "        batch_size, num_tokens, _ = x.shape\n",
-        "\n",
-        "        # Ensure attn_mask is compatible with expected shape and `batch_first=True`\n",
-        "        # No need to manually adjust for num_heads; ensure it's right for the sequence\n",
-        "        if self.block_size >= num_tokens:\n",
-        "            attn_mask = self.mask[:num_tokens, :num_tokens]\n",
-        "        else:\n",
-        "            attn_mask = self.mask[:self.block_size, :self.block_size]\n",
-        "\n",
-        "        # attn_mask broadcasting will handle batch_size dimension implicitly\n",
-        "        attn_output, _ = self.multihead_attn(\n",
-        "            x, x, x, attn_mask=attn_mask, need_weights=self.need_weights\n",
-        "        )\n",
-        "\n",
-        "        output = self.proj(attn_output)\n",
-        "\n",
-        "        return output\n",
-        "\n",
-        "\n",
-        "mha_pytorch_class_default = MHAPyTorchClass(\n",
-        "    d_in=embed_dim,\n",
-        "    d_out=embed_dim,\n",
-        "    block_size=context_len,\n",
-        "    dropout=0.0,\n",
-        "    num_heads=12,\n",
-        "    qkv_bias=False\n",
-        ").to(device)\n",
-        "\n",
-        "out = mha_pytorch_class_default(embeddings)\n",
-        "print(out.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "a3953bff-1056-4de2-bfd1-dfccf659eee4",
-      "metadata": {
-        "id": "a3953bff-1056-4de2-bfd1-dfccf659eee4"
-      },
-      "source": [
-        "## 6) Using PyTorch's torch.nn.MultiheadAttention with `scaled_dot_product_attention`"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "d2164859-31a0-4537-b4fb-27d57675ba77",
-      "metadata": {
-        "id": "d2164859-31a0-4537-b4fb-27d57675ba77"
-      },
-      "source": [
-        "- Set `need_weights` (default `True`) to need_weights=False so that MultiheadAttention uses `scaled_dot_product_attention` [according to the documentation](https://github.com/pytorch/pytorch/blob/71d020262793542974cf13b30f2a9099773f015c/torch/nn/modules/activation.py#L1096)\n",
-        "\n",
-        ">  need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``.\n",
-        "            Set ``need_weights=False`` to use the optimized ``scaled_dot_product_attention``\n",
-        "            and achieve the best performance for MHA.\n",
-        "            Default: ``True``."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "4a4c2afe-5e1f-4bd7-a118-67031176f147",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "4a4c2afe-5e1f-4bd7-a118-67031176f147",
-        "outputId": "234771f4-8a53-4478-8a9b-cf19f79a5e07"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "torch.Size([8, 1024, 768])\n"
-          ]
-        }
-      ],
-      "source": [
-        "mha_pytorch_class_noweights = MHAPyTorchClass(\n",
-        "    d_in=embed_dim,\n",
-        "    d_out=embed_dim,\n",
-        "    block_size=context_len,\n",
-        "    dropout=0.0,\n",
-        "    num_heads=12,\n",
-        "    qkv_bias=False,\n",
-        "    need_weights=False # NEW!\n",
-        ").to(device)\n",
-        "\n",
-        "out = mha_pytorch_class_noweights(embeddings)\n",
-        "print(out.shape)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "8877de71-f84f-4f6d-bc87-7552013b6301",
-      "metadata": {
-        "id": "8877de71-f84f-4f6d-bc87-7552013b6301"
-      },
-      "source": [
-        "## Quick speed comparison (M3 Macbook Air CPU)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "a97c0b2e-6593-49d8-98bc-2267b3aa610f",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "a97c0b2e-6593-49d8-98bc-2267b3aa610f",
-        "outputId": "ebe635b2-5c03-4e9b-da3a-951d308acf7b"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "200 ms ± 5.98 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "## 1) CausalAttention MHA wrapper class from chapter 3\n",
-        "%timeit mha_ch03_wrapper(embeddings)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "19db9c2c-8e75-431a-8eef-0b4d8284e6e6",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "19db9c2c-8e75-431a-8eef-0b4d8284e6e6",
-        "outputId": "c6e7bcff-661c-45a6-da82-b1e3f89cf761"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "198 ms ± 6.66 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "## 2) The multi-head attention class from chapter 3\n",
-        "%timeit mha_ch03(embeddings)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "aa526ee0-7a88-4f34-a49a-f8f97da83779",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "aa526ee0-7a88-4f34-a49a-f8f97da83779",
-        "outputId": "92b634f8-43f8-468f-87a1-bb774b64c212"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "236 ms ± 13.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "## 3) An alternative multi-head attention with combined weights\n",
-        "%timeit mha_combined_qkv(embeddings)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "cc2b4256-16d8-4c34-9fd0-d4b4af0e60fa",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "cc2b4256-16d8-4c34-9fd0-d4b4af0e60fa",
-        "outputId": "80c6e314-0771-470e-b090-628984ce2d85"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "71.6 ms ± 3.32 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "## 4) Multihead attention with PyTorch's scaled dot product attention\n",
-        "%timeit mha_pytorch_scaled(embeddings)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "0f209e70-ebb6-4a1a-b608-1ff42e41c01d",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "0f209e70-ebb6-4a1a-b608-1ff42e41c01d",
-        "outputId": "3cd37b53-04d4-4dd0-9450-6fc8ebaac083"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "217 ms ± 4.27 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "## 5) Using PyTorch's torch.nn.MultiheadAttention\n",
-        "%timeit mha_pytorch_class_default(embeddings)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "3f4968c2-8d40-4ab9-8dba-052b4f77d756",
-      "metadata": {
-        "id": "3f4968c2-8d40-4ab9-8dba-052b4f77d756",
-        "outputId": "2e86bdb4-7fa0-4051-b000-4a2b591060a2",
-        "tags": []
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "205 ms ± 3.9 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "## 6) Using PyTorch's torch.nn.MultiheadAttention disabling `need_weights`\n",
-        "%timeit mha_pytorch_class_noweights(embeddings)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "a78ff594-6cc2-496d-a302-789fa104c3c9",
-      "metadata": {
-        "id": "a78ff594-6cc2-496d-a302-789fa104c3c9"
-      },
-      "source": [
-        "## Quick speed comparison (Nvidia A100 GPU)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "id": "707a2a14-a089-48a8-88aa-d328e1e0a9d0",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "707a2a14-a089-48a8-88aa-d328e1e0a9d0",
-        "outputId": "e99a17e9-8139-4b04-dac8-fa1dd5027735"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "8.35 ms ± 1.44 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "## 1) CausalAttention MHA wrapper class from chapter 3\n",
-        "%timeit mha_ch03_wrapper(embeddings)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "id": "8686dd69-3655-40e4-a57b-a2c55532a010",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "8686dd69-3655-40e4-a57b-a2c55532a010",
-        "outputId": "5553b42c-b709-41a4-8a8b-be36dae408ab"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "6.59 ms ± 231 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "## 2) The multi-head attention class from chapter 3\n",
-        "%timeit mha_ch03(embeddings)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "id": "2209d7df-e54b-4910-ae2b-c78cf684d9bf",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "2209d7df-e54b-4910-ae2b-c78cf684d9bf",
-        "outputId": "01b0da88-510b-4b21-919a-0a7519a55ed8"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "7.21 ms ± 716 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "## 3) An alternative multi-head attention with combined weights\n",
-        "%timeit mha_combined_qkv(embeddings)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "id": "1075abe2-4839-4fd6-af3e-c09bb3651e26",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "1075abe2-4839-4fd6-af3e-c09bb3651e26",
-        "outputId": "542706db-5041-45ca-f667-9e1bd1c2c7aa"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "2.38 ms ± 362 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "## 4) Multihead attention with PyTorch's scaled dot product attention\n",
-        "%timeit mha_pytorch_scaled(embeddings)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "id": "868e3670-8edc-47bc-9e06-eb505e44dc9d",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "868e3670-8edc-47bc-9e06-eb505e44dc9d",
-        "outputId": "13cfc808-2b11-4041-fe67-e5a63abe4f28"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "6.67 ms ± 408 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "## 5) Using PyTorch's torch.nn.MultiheadAttention\n",
-        "%timeit mha_pytorch_class_default(embeddings)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "id": "944870e6-de54-4e3b-a455-b8f21f6f92c8",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "944870e6-de54-4e3b-a455-b8f21f6f92c8",
-        "outputId": "c52858e7-999c-4782-adc9-731f8d69dfa6"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "4.54 ms ± 7.17 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-          ]
-        }
-      ],
-      "source": [
-        "## 6) Using PyTorch's torch.nn.MultiheadAttention disabling `need_weights`\n",
-        "%timeit mha_pytorch_class_noweights(embeddings)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "dabc6575-0316-4640-a729-e616d5c17b73",
-      "metadata": {
-        "id": "dabc6575-0316-4640-a729-e616d5c17b73"
-      },
-      "source": [
-        "## Speed comparison (Nvidia A100 GPU) with warmup"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 15,
-      "id": "29b63d3d-6d0b-43bb-9c68-d5514dc81000",
-      "metadata": {
-        "id": "29b63d3d-6d0b-43bb-9c68-d5514dc81000"
-      },
-      "outputs": [],
-      "source": [
-        "# CUDA benchmark code shared by Andrei Aksionov\n",
-        "# and based on code from\n",
-        "# https://github.com/cuda-mode/lectures/blob/main/lecture1/pytorch_square.py\n",
-        "\n",
-        "def time_pytorch_function(func, *input, num_repeats = 1_000):\n",
-        "    # CUDA IS ASYNC so can't use python time module\n",
-        "    start = torch.cuda.Event(enable_timing=True)\n",
-        "    end = torch.cuda.Event(enable_timing=True)\n",
-        "\n",
-        "    # Warmup\n",
-        "    for _ in range(5):\n",
-        "        func(*input)\n",
-        "    torch.cuda.synchronize()\n",
-        "\n",
-        "    start.record()\n",
-        "    for _ in range(num_repeats):\n",
-        "        func(*input)\n",
-        "        torch.cuda.synchronize()\n",
-        "    end.record()\n",
-        "    torch.cuda.synchronize()\n",
-        "    return start.elapsed_time(end) / num_repeats"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "id": "CDJAPZaszaqx",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 489
-        },
-        "id": "CDJAPZaszaqx",
-        "outputId": "f23e9b83-7fd6-4011-9434-0e6934cf762a"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "\n",
-        "embeddings_cuda = embeddings.to(torch.device(\"cuda\"))\n",
-        "\n",
-        "functions = {\n",
-        "    \"1) MHA wrapper class\": mha_ch03_wrapper,\n",
-        "    \"2) MHA Ch03\": mha_ch03,\n",
-        "    \"3) MHA with combined QKV weights\": mha_combined_qkv,\n",
-        "    \"4) MHA with PyTorch scaled_dot_product_attention\": mha_pytorch_scaled,\n",
-        "    \"5) PyTorch MHA class defaults\": mha_pytorch_class_default,\n",
-        "    \"6) PyTorch MHA with need_weights=False\": mha_pytorch_class_noweights\n",
-        "}\n",
-        "execution_times = [time_pytorch_function(fn, embeddings_cuda) for name,fn in functions.items()]\n",
-        "\n",
-        "\n",
-        "# Plotting\n",
-        "\n",
-        "# Customize further for dark mode aesthetics\n",
-        "plt.rcParams['figure.facecolor'] = '#121212'  # Dark figure background\n",
-        "plt.rcParams['axes.facecolor'] = '#121212'    # Dark axes background\n",
-        "plt.rcParams['axes.edgecolor'] = 'white'      # White axes border\n",
-        "plt.rcParams['axes.labelcolor'] = 'white'     # White labels\n",
-        "plt.rcParams['text.color'] = 'white'          # White text\n",
-        "plt.rcParams['xtick.color'] = 'white'         # White x ticks\n",
-        "plt.rcParams['ytick.color'] = 'white'         # White y ticks\n",
-        "plt.rcParams['grid.color'] = '#444444'        # Lighter grid lines for contrast\n",
-        "plt.rcParams['lines.linewidth'] = 2           # Thicker plot lines for visibility\n",
-        "plt.rcParams['lines.markersize'] = 8          # Larger markers for visibility\n",
-        "\n",
-        "fig, ax = plt.subplots()\n",
-        "bars = plt.bar(functions.keys(), execution_times)\n",
-        "\n",
-        "plt.ylabel('Execution time (ms)')\n",
-        "plt.xticks(rotation=45, ha=\"right\")\n",
-        "\n",
-        "# Calculate new ylim with a margin\n",
-        "max_execution_time = max(execution_times)\n",
-        "upper_ylim = max_execution_time + 0.2 * max_execution_time  # Adding a 20% margin\n",
-        "\n",
-        "plt.ylim(0, upper_ylim)  # Setting new ylim\n",
-        "\n",
-        "# Annotate bars with execution times\n",
-        "for bar in bars:\n",
-        "    yval = bar.get_height()\n",
-        "    plt.text(bar.get_x() + bar.get_width()/2, yval + (0.05 * upper_ylim), round(yval, 2), ha='center', va='bottom')\n",
-        "\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "plt.savefig(\"1.pdf\")\n",
-        "plt.show()\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "A100",
-      "machine_shape": "hm",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "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": "3.10.6"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6f678e62-7bcb-4405-86ae-dce94f494303",
+   "metadata": {
+    "id": "6f678e62-7bcb-4405-86ae-dce94f494303"
+   },
+   "source": [
+    "# Efficient Multi-Head Attention Implementations"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
-}
\ No newline at end of file
+  {
+   "cell_type": "markdown",
+   "id": "b742938a-4bfc-4527-a1f1-d5963508967d",
+   "metadata": {
+    "id": "b742938a-4bfc-4527-a1f1-d5963508967d"
+   },
+   "source": [
+    "This code notebook compares different ways to implement causal multi-head attention used in decoder-style LLMs like GPT, Llama, etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7898551e-f582-48ac-9f66-3632abe2a93f",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "7898551e-f582-48ac-9f66-3632abe2a93f",
+    "outputId": "7d088260-3fa1-44f2-bd65-2a46e289f9d4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PyTorch version: 2.2.1+cu121\n",
+      "Running on cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "\n",
+    "torch.manual_seed(123)\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"PyTorch version: {torch.__version__}\")\n",
+    "print(f\"Running on {device}\")\n",
+    "\n",
+    "batch_size = 8\n",
+    "context_len = 1024\n",
+    "embed_dim = 768\n",
+    "embeddings = torch.randn((batch_size, context_len, embed_dim), device=device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f9bb1b6-a1e5-4e0a-884d-0f31b374a8d6",
+   "metadata": {
+    "id": "2f9bb1b6-a1e5-4e0a-884d-0f31b374a8d6"
+   },
+   "source": [
+    "## 1) CausalAttention MHA wrapper class from chapter 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "297c93ed-aec0-4896-bb89-42c4b294d3d1",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "297c93ed-aec0-4896-bb89-42c4b294d3d1",
+    "outputId": "f8a33752-2cd6-4101-8feb-9d1699984719"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([8, 1024, 768])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from ch03 import MultiHeadAttentionWrapper as Ch03_MHA_Wrapper\n",
+    "\n",
+    "mha_ch03_wrapper = Ch03_MHA_Wrapper(\n",
+    "    d_in=embed_dim,\n",
+    "    d_out=embed_dim//12,\n",
+    "    block_size=context_len,\n",
+    "    dropout=0.0,\n",
+    "    num_heads=12,\n",
+    "    qkv_bias=False\n",
+    ").to(device)\n",
+    "\n",
+    "out = mha_ch03_wrapper(embeddings)\n",
+    "print(out.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21930804-b327-40b1-8e63-94dcad39ce7b",
+   "metadata": {
+    "id": "21930804-b327-40b1-8e63-94dcad39ce7b"
+   },
+   "source": [
+    "## 2) The multi-head attention class from chapter 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4ee6a61b-d25c-4a0c-8a59-f285544e3710",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4ee6a61b-d25c-4a0c-8a59-f285544e3710",
+    "outputId": "b704a040-3547-422c-ecda-df9982a2da35"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([8, 1024, 768])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from ch03 import MultiHeadAttention as Ch03_MHA\n",
+    "\n",
+    "mha_ch03 = Ch03_MHA(\n",
+    "    d_in=embed_dim,\n",
+    "    d_out=embed_dim,\n",
+    "    block_size=context_len,\n",
+    "    dropout=0.0,\n",
+    "    num_heads=12,\n",
+    "    qkv_bias=False\n",
+    ").to(device)\n",
+    "\n",
+    "out = mha_ch03(embeddings)\n",
+    "print(out.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73cd11da-ea3b-4081-b483-c4965dfefbc4",
+   "metadata": {
+    "id": "73cd11da-ea3b-4081-b483-c4965dfefbc4"
+   },
+   "source": [
+    "## 3) An alternative multi-head attention with combined weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fa1a5ea-eaff-4d2d-aaf0-b34cdb6fd4dd",
+   "metadata": {
+    "id": "1fa1a5ea-eaff-4d2d-aaf0-b34cdb6fd4dd"
+   },
+   "source": [
+    "- The code for the `MultiHeadAttentionAlt` class below is based on code that was kindly shared by [Rayed Bin Wahed](https://github.com/rasbt/LLMs-from-scratch/discussions/51)\n",
+    "- The main difference between the `MultiHeadAttentionAlt` class and the `MultiHeadAttention` class used in chapter 3 is that `MultiHeadAttentionAlt` uses a single weight matrix, `self.qkv = nn.Linear(d_in, 3 * d_out, bias=qkv_bias)` instead of separate weight matrices:\n",
+    "\n",
+    "  - `self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)`\n",
+    "  - `self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)`\n",
+    "  - `self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)`\n",
+    "\n",
+    "- Here, `self.qkv` combines all three weight matrices `self.W_query`, `self.W_key`, and `self.W_value` to carry out the query, key, and value computation in a single step\n",
+    "- Using `q, k, v = qkv.unbind(0)`, we obtain the individual query, key, and value tensors, which are then used similarly to the query, key, and value tensors in the `MultiHeadAttention` class in chapter 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9a6bd0a2-f27c-4602-afa0-c96cd295c1a6",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9a6bd0a2-f27c-4602-afa0-c96cd295c1a6",
+    "outputId": "5d948671-176f-4633-bede-97767e36becc"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([8, 1024, 768])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch.nn as nn\n",
+    "\n",
+    "\n",
+    "class MultiHeadAttentionCombinedQKV(nn.Module):\n",
+    "    def __init__(self, d_in, d_out, num_heads, block_size, dropout=0.0, qkv_bias=False):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        assert d_out % num_heads == 0, \"embed_dim is indivisible by num_heads\"\n",
+    "\n",
+    "        self.num_heads = num_heads\n",
+    "        self.block_size = block_size\n",
+    "        self.head_dim = d_out // num_heads\n",
+    "\n",
+    "        self.qkv = nn.Linear(d_in, 3 * d_out, bias=qkv_bias)\n",
+    "        self.proj = nn.Linear(d_in, d_out)\n",
+    "        self.dropout = nn.Dropout(dropout)\n",
+    "\n",
+    "        self.register_buffer(\n",
+    "            \"mask\", torch.triu(torch.ones(block_size, block_size), diagonal=1)\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        batch_size, num_tokens, embed_dim = x.shape\n",
+    "\n",
+    "        # (b, num_tokens, embed_dim) --> (b, num_tokens, 3 * embed_dim)\n",
+    "        qkv = self.qkv(x)\n",
+    "\n",
+    "        # (b, num_tokens, 3 * embed_dim) --> (b, num_tokens, 3, num_heads, head_dim)\n",
+    "        qkv = qkv.reshape(batch_size, num_tokens, 3, self.num_heads, self.head_dim)\n",
+    "\n",
+    "        # (b, num_tokens, 3, num_heads, head_dim) --> (3, b, num_heads, num_tokens, head_dim)\n",
+    "        qkv = qkv.permute(2, 0, 3, 1, 4)\n",
+    "\n",
+    "        # (3, b, num_heads, num_tokens, head_dim) -> 3 times (b, num_head, num_tokens, head_dim)\n",
+    "        queries, keys, values = qkv.unbind(0)\n",
+    "\n",
+    "        # (b, num_heads, num_tokens, head_dim) --> (b, num_heads, num_tokens, num_tokens)\n",
+    "        attn_scores = queries @ keys.transpose(-2, -1)\n",
+    "        attn_scores = attn_scores.masked_fill(\n",
+    "            self.mask.bool()[:num_tokens, :num_tokens], -torch.inf\n",
+    "        )\n",
+    "\n",
+    "        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**-0.5, dim=-1)\n",
+    "        attn_weights = self.dropout(attn_weights)\n",
+    "\n",
+    "        # (b, num_heads, num_tokens, num_tokens) --> (b, num_heads, num_tokens, head_dim)\n",
+    "        context_vec = attn_weights @ values\n",
+    "\n",
+    "        # (b, num_heads, num_tokens, head_dim) --> (b, num_tokens, num_heads, head_dim)\n",
+    "        context_vec = context_vec.transpose(1, 2)\n",
+    "\n",
+    "        # (b, num_tokens, num_heads, head_dim) --> (b, num_tokens, embed_dim)\n",
+    "        context_vec = context_vec.reshape(batch_size, num_tokens, embed_dim)\n",
+    "\n",
+    "        context_vec = self.proj(context_vec)\n",
+    "\n",
+    "        return context_vec\n",
+    "\n",
+    "\n",
+    "mha_combined_qkv = MultiHeadAttentionCombinedQKV(\n",
+    "    d_in=embed_dim,\n",
+    "    d_out=embed_dim,\n",
+    "    block_size=context_len,\n",
+    "    dropout=0.0,\n",
+    "    num_heads=12,\n",
+    "    qkv_bias=False\n",
+    ").to(device)\n",
+    "\n",
+    "out = mha_combined_qkv(embeddings)\n",
+    "print(out.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48a042d3-ee78-4c29-bf63-d92fe6706632",
+   "metadata": {
+    "id": "48a042d3-ee78-4c29-bf63-d92fe6706632"
+   },
+   "source": [
+    "## 4) Multihead attention with PyTorch's scaled dot product attention"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f78e346f-3b85-44e6-9feb-f01131381148",
+   "metadata": {
+    "id": "f78e346f-3b85-44e6-9feb-f01131381148"
+   },
+   "source": [
+    "- The implementation below uses PyTorch's [`scaled_dot_product_attention`](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) function, which implements a memory-optimized version of self-attention calld [flash attention](https://arxiv.org/abs/2205.14135)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1b8e5a0d-1f65-4a03-bf6e-723f0cc428f5",
+   "metadata": {
+    "id": "1b8e5a0d-1f65-4a03-bf6e-723f0cc428f5"
+   },
+   "outputs": [],
+   "source": [
+    "class MHAPyTorchScaledDotProduct(nn.Module):\n",
+    "    def __init__(self, d_in, d_out, num_heads, block_size, dropout=0.0, qkv_bias=False):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        assert d_out % num_heads == 0, \"embed_dim is indivisible by num_heads\"\n",
+    "\n",
+    "        self.num_heads = num_heads\n",
+    "        self.block_size = block_size\n",
+    "        self.head_dim = d_out // num_heads\n",
+    "        self.d_out = d_out\n",
+    "\n",
+    "        self.qkv = nn.Linear(d_in, 3 * d_out, bias=qkv_bias)\n",
+    "        self.proj = nn.Linear(d_in, d_out)\n",
+    "        self.dropout = dropout\n",
+    "\n",
+    "        self.register_buffer(\n",
+    "            \"mask\", torch.triu(torch.ones(block_size, block_size), diagonal=1)\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        batch_size, num_tokens, embed_dim = x.shape\n",
+    "\n",
+    "        # (b, num_tokens, embed_dim) --> (b, num_tokens, 3 * embed_dim)\n",
+    "        qkv = self.qkv(x)\n",
+    "\n",
+    "        # (b, num_tokens, 3 * embed_dim) --> (b, num_tokens, 3, num_heads, head_dim)\n",
+    "        qkv = qkv.reshape(batch_size, num_tokens, 3, self.num_heads, self.head_dim)\n",
+    "\n",
+    "        # (b, num_tokens, 3, num_heads, head_dim) --> (3, b, num_heads, num_tokens, head_dim)\n",
+    "        qkv = qkv.permute(2, 0, 3, 1, 4)\n",
+    "\n",
+    "        # (3, b, num_heads, num_tokens, head_dim) -> 3 times (b, num_heads, num_tokens, head_dim)\n",
+    "        queries, keys, values = qkv.unbind(0)\n",
+    "\n",
+    "        use_dropout = 0. if not self.training else self.dropout\n",
+    "        context_vec = nn.functional.scaled_dot_product_attention(\n",
+    "            queries, keys, values, attn_mask=None, dropout_p=use_dropout, is_causal=True)\n",
+    "\n",
+    "        # Combine heads, where self.d_out = self.num_heads * self.head_dim\n",
+    "        context_vec = context_vec.transpose(1, 2).contiguous().view(batch_size, num_tokens, self.d_out)\n",
+    "\n",
+    "        return context_vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fbc8ba92-3471-41cb-b1b2-4c0ef5be392b",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "fbc8ba92-3471-41cb-b1b2-4c0ef5be392b",
+    "outputId": "af9e4855-7f20-4d61-8532-4827df8dfb30"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([8, 1024, 768])\n"
+     ]
+    }
+   ],
+   "source": [
+    "mha_pytorch_scaled = MHAPyTorchScaledDotProduct(\n",
+    "    d_in=embed_dim,\n",
+    "    d_out=embed_dim,\n",
+    "    block_size=context_len,\n",
+    "    dropout=0.0,\n",
+    "    num_heads=12,\n",
+    "    qkv_bias=False\n",
+    ").to(device)\n",
+    "\n",
+    "out = mha_pytorch_scaled(embeddings)\n",
+    "print(out.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "351c318f-4835-4d74-8d58-a070222447c4",
+   "metadata": {
+    "id": "351c318f-4835-4d74-8d58-a070222447c4"
+   },
+   "source": [
+    "## 5) Using PyTorch's torch.nn.MultiheadAttention"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74a6d060-6324-48fa-a35c-cb09f2a48965",
+   "metadata": {
+    "id": "74a6d060-6324-48fa-a35c-cb09f2a48965"
+   },
+   "source": [
+    "- Below, we use PyTorch's [torch.nn.MultiheadAttention](https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html) implementation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3799c7ef-3155-42c6-a829-f95656453ae0",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3799c7ef-3155-42c6-a829-f95656453ae0",
+    "outputId": "2a085df8-0445-4818-9978-6dc74469f568"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([8, 1024, 768])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch.nn as nn\n",
+    "\n",
+    "\n",
+    "class MHAPyTorchClass(nn.Module):\n",
+    "    def __init__(self, d_in, d_out, num_heads, block_size, dropout=0.0, qkv_bias=False, need_weights=True):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        self.block_size = block_size\n",
+    "        self.multihead_attn = nn.MultiheadAttention(\n",
+    "            embed_dim=d_out,\n",
+    "            num_heads=num_heads,\n",
+    "            dropout=dropout,\n",
+    "            bias=qkv_bias,\n",
+    "            add_bias_kv=qkv_bias,\n",
+    "            batch_first=True,\n",
+    "        )\n",
+    "\n",
+    "        self.need_weights = need_weights\n",
+    "        self.proj = nn.Linear(d_out, d_out)\n",
+    "        self.register_buffer(\"mask\", torch.triu(torch.ones(block_size, block_size), diagonal=1).bool())\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        batch_size, num_tokens, _ = x.shape\n",
+    "\n",
+    "        # Ensure attn_mask is compatible with expected shape and `batch_first=True`\n",
+    "        # No need to manually adjust for num_heads; ensure it's right for the sequence\n",
+    "        if self.block_size >= num_tokens:\n",
+    "            attn_mask = self.mask[:num_tokens, :num_tokens]\n",
+    "        else:\n",
+    "            attn_mask = self.mask[:self.block_size, :self.block_size]\n",
+    "\n",
+    "        # attn_mask broadcasting will handle batch_size dimension implicitly\n",
+    "        attn_output, _ = self.multihead_attn(\n",
+    "            x, x, x, attn_mask=attn_mask, need_weights=self.need_weights\n",
+    "        )\n",
+    "\n",
+    "        output = self.proj(attn_output)\n",
+    "\n",
+    "        return output\n",
+    "\n",
+    "\n",
+    "mha_pytorch_class_default = MHAPyTorchClass(\n",
+    "    d_in=embed_dim,\n",
+    "    d_out=embed_dim,\n",
+    "    block_size=context_len,\n",
+    "    dropout=0.0,\n",
+    "    num_heads=12,\n",
+    "    qkv_bias=False\n",
+    ").to(device)\n",
+    "\n",
+    "out = mha_pytorch_class_default(embeddings)\n",
+    "print(out.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3953bff-1056-4de2-bfd1-dfccf659eee4",
+   "metadata": {
+    "id": "a3953bff-1056-4de2-bfd1-dfccf659eee4"
+   },
+   "source": [
+    "## 6) Using PyTorch's torch.nn.MultiheadAttention with `scaled_dot_product_attention`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2164859-31a0-4537-b4fb-27d57675ba77",
+   "metadata": {
+    "id": "d2164859-31a0-4537-b4fb-27d57675ba77"
+   },
+   "source": [
+    "- Set `need_weights` (default `True`) to need_weights=False so that MultiheadAttention uses `scaled_dot_product_attention` [according to the documentation](https://github.com/pytorch/pytorch/blob/71d020262793542974cf13b30f2a9099773f015c/torch/nn/modules/activation.py#L1096)\n",
+    "\n",
+    ">  need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``.\n",
+    "            Set ``need_weights=False`` to use the optimized ``scaled_dot_product_attention``\n",
+    "            and achieve the best performance for MHA.\n",
+    "            Default: ``True``."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4a4c2afe-5e1f-4bd7-a118-67031176f147",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4a4c2afe-5e1f-4bd7-a118-67031176f147",
+    "outputId": "234771f4-8a53-4478-8a9b-cf19f79a5e07"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([8, 1024, 768])\n"
+     ]
+    }
+   ],
+   "source": [
+    "mha_pytorch_class_noweights = MHAPyTorchClass(\n",
+    "    d_in=embed_dim,\n",
+    "    d_out=embed_dim,\n",
+    "    block_size=context_len,\n",
+    "    dropout=0.0,\n",
+    "    num_heads=12,\n",
+    "    qkv_bias=False,\n",
+    "    need_weights=False # NEW!\n",
+    ").to(device)\n",
+    "\n",
+    "out = mha_pytorch_class_noweights(embeddings)\n",
+    "print(out.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8877de71-f84f-4f6d-bc87-7552013b6301",
+   "metadata": {
+    "id": "8877de71-f84f-4f6d-bc87-7552013b6301"
+   },
+   "source": [
+    "## Quick speed comparison (M3 Macbook Air CPU)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a97c0b2e-6593-49d8-98bc-2267b3aa610f",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a97c0b2e-6593-49d8-98bc-2267b3aa610f",
+    "outputId": "ebe635b2-5c03-4e9b-da3a-951d308acf7b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "200 ms ± 5.98 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "## 1) CausalAttention MHA wrapper class from chapter 3\n",
+    "%timeit mha_ch03_wrapper(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19db9c2c-8e75-431a-8eef-0b4d8284e6e6",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "19db9c2c-8e75-431a-8eef-0b4d8284e6e6",
+    "outputId": "c6e7bcff-661c-45a6-da82-b1e3f89cf761"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "198 ms ± 6.66 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "## 2) The multi-head attention class from chapter 3\n",
+    "%timeit mha_ch03(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa526ee0-7a88-4f34-a49a-f8f97da83779",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "aa526ee0-7a88-4f34-a49a-f8f97da83779",
+    "outputId": "92b634f8-43f8-468f-87a1-bb774b64c212"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "236 ms ± 13.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "## 3) An alternative multi-head attention with combined weights\n",
+    "%timeit mha_combined_qkv(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc2b4256-16d8-4c34-9fd0-d4b4af0e60fa",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cc2b4256-16d8-4c34-9fd0-d4b4af0e60fa",
+    "outputId": "80c6e314-0771-470e-b090-628984ce2d85"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "71.6 ms ± 3.32 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "## 4) Multihead attention with PyTorch's scaled dot product attention\n",
+    "%timeit mha_pytorch_scaled(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f209e70-ebb6-4a1a-b608-1ff42e41c01d",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0f209e70-ebb6-4a1a-b608-1ff42e41c01d",
+    "outputId": "3cd37b53-04d4-4dd0-9450-6fc8ebaac083"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "217 ms ± 4.27 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "## 5) Using PyTorch's torch.nn.MultiheadAttention\n",
+    "%timeit mha_pytorch_class_default(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f4968c2-8d40-4ab9-8dba-052b4f77d756",
+   "metadata": {
+    "id": "3f4968c2-8d40-4ab9-8dba-052b4f77d756",
+    "outputId": "2e86bdb4-7fa0-4051-b000-4a2b591060a2",
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "205 ms ± 3.9 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "## 6) Using PyTorch's torch.nn.MultiheadAttention disabling `need_weights`\n",
+    "%timeit mha_pytorch_class_noweights(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a78ff594-6cc2-496d-a302-789fa104c3c9",
+   "metadata": {
+    "id": "a78ff594-6cc2-496d-a302-789fa104c3c9"
+   },
+   "source": [
+    "## Quick speed comparison (Nvidia A100 GPU)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "707a2a14-a089-48a8-88aa-d328e1e0a9d0",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "707a2a14-a089-48a8-88aa-d328e1e0a9d0",
+    "outputId": "e99a17e9-8139-4b04-dac8-fa1dd5027735"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8.35 ms ± 1.44 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "## 1) CausalAttention MHA wrapper class from chapter 3\n",
+    "%timeit mha_ch03_wrapper(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8686dd69-3655-40e4-a57b-a2c55532a010",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8686dd69-3655-40e4-a57b-a2c55532a010",
+    "outputId": "5553b42c-b709-41a4-8a8b-be36dae408ab"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6.59 ms ± 231 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "## 2) The multi-head attention class from chapter 3\n",
+    "%timeit mha_ch03(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2209d7df-e54b-4910-ae2b-c78cf684d9bf",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "2209d7df-e54b-4910-ae2b-c78cf684d9bf",
+    "outputId": "01b0da88-510b-4b21-919a-0a7519a55ed8"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7.21 ms ± 716 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "## 3) An alternative multi-head attention with combined weights\n",
+    "%timeit mha_combined_qkv(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1075abe2-4839-4fd6-af3e-c09bb3651e26",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1075abe2-4839-4fd6-af3e-c09bb3651e26",
+    "outputId": "542706db-5041-45ca-f667-9e1bd1c2c7aa"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.38 ms ± 362 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "## 4) Multihead attention with PyTorch's scaled dot product attention\n",
+    "%timeit mha_pytorch_scaled(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "868e3670-8edc-47bc-9e06-eb505e44dc9d",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "868e3670-8edc-47bc-9e06-eb505e44dc9d",
+    "outputId": "13cfc808-2b11-4041-fe67-e5a63abe4f28"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6.67 ms ± 408 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "## 5) Using PyTorch's torch.nn.MultiheadAttention\n",
+    "%timeit mha_pytorch_class_default(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "944870e6-de54-4e3b-a455-b8f21f6f92c8",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "944870e6-de54-4e3b-a455-b8f21f6f92c8",
+    "outputId": "c52858e7-999c-4782-adc9-731f8d69dfa6"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.54 ms ± 7.17 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "## 6) Using PyTorch's torch.nn.MultiheadAttention disabling `need_weights`\n",
+    "%timeit mha_pytorch_class_noweights(embeddings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dabc6575-0316-4640-a729-e616d5c17b73",
+   "metadata": {
+    "id": "dabc6575-0316-4640-a729-e616d5c17b73"
+   },
+   "source": [
+    "## Speed comparison (Nvidia A100 GPU) with warmup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "29b63d3d-6d0b-43bb-9c68-d5514dc81000",
+   "metadata": {
+    "id": "29b63d3d-6d0b-43bb-9c68-d5514dc81000"
+   },
+   "outputs": [],
+   "source": [
+    "# CUDA benchmark code shared by Andrei Aksionov\n",
+    "# and based on code from\n",
+    "# https://github.com/cuda-mode/lectures/blob/main/lecture1/pytorch_square.py\n",
+    "\n",
+    "def time_pytorch_function(func, *input, num_repeats = 1_000):\n",
+    "    # CUDA IS ASYNC so can't use python time module\n",
+    "    start = torch.cuda.Event(enable_timing=True)\n",
+    "    end = torch.cuda.Event(enable_timing=True)\n",
+    "\n",
+    "    # Warmup\n",
+    "    for _ in range(5):\n",
+    "        func(*input)\n",
+    "    torch.cuda.synchronize()\n",
+    "\n",
+    "    start.record()\n",
+    "    for _ in range(num_repeats):\n",
+    "        func(*input)\n",
+    "        torch.cuda.synchronize()\n",
+    "    end.record()\n",
+    "    torch.cuda.synchronize()\n",
+    "    return start.elapsed_time(end) / num_repeats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "CDJAPZaszaqx",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 489
+    },
+    "id": "CDJAPZaszaqx",
+    "outputId": "f23e9b83-7fd6-4011-9434-0e6934cf762a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "embeddings_cuda = embeddings.to(torch.device(\"cuda\"))\n",
+    "\n",
+    "functions = {\n",
+    "    \"1) MHA wrapper class\": mha_ch03_wrapper,\n",
+    "    \"2) MHA Ch03\": mha_ch03,\n",
+    "    \"3) MHA with combined QKV weights\": mha_combined_qkv,\n",
+    "    \"4) MHA with PyTorch scaled_dot_product_attention\": mha_pytorch_scaled,\n",
+    "    \"5) PyTorch MHA class defaults\": mha_pytorch_class_default,\n",
+    "    \"6) PyTorch MHA with need_weights=False\": mha_pytorch_class_noweights\n",
+    "}\n",
+    "execution_times = [time_pytorch_function(fn, embeddings_cuda) for name,fn in functions.items()]\n",
+    "\n",
+    "\n",
+    "# Plotting\n",
+    "\n",
+    "# Customize further for dark mode aesthetics\n",
+    "plt.rcParams['figure.facecolor'] = '#121212'  # Dark figure background\n",
+    "plt.rcParams['axes.facecolor'] = '#121212'    # Dark axes background\n",
+    "plt.rcParams['axes.edgecolor'] = 'white'      # White axes border\n",
+    "plt.rcParams['axes.labelcolor'] = 'white'     # White labels\n",
+    "plt.rcParams['text.color'] = 'white'          # White text\n",
+    "plt.rcParams['xtick.color'] = 'white'         # White x ticks\n",
+    "plt.rcParams['ytick.color'] = 'white'         # White y ticks\n",
+    "plt.rcParams['grid.color'] = '#444444'        # Lighter grid lines for contrast\n",
+    "plt.rcParams['lines.linewidth'] = 2           # Thicker plot lines for visibility\n",
+    "plt.rcParams['lines.markersize'] = 8          # Larger markers for visibility\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "bars = plt.bar(functions.keys(), execution_times)\n",
+    "\n",
+    "plt.ylabel('Execution time (ms)')\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "\n",
+    "# Calculate new ylim with a margin\n",
+    "max_execution_time = max(execution_times)\n",
+    "upper_ylim = max_execution_time + 0.2 * max_execution_time  # Adding a 20% margin\n",
+    "\n",
+    "plt.ylim(0, upper_ylim)  # Setting new ylim\n",
+    "\n",
+    "# Annotate bars with execution times\n",
+    "for bar in bars:\n",
+    "    yval = bar.get_height()\n",
+    "    plt.text(bar.get_x() + bar.get_width()/2, yval + (0.05 * upper_ylim), round(yval, 2), ha='center', va='bottom')\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"1.pdf\")\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "A100",
+   "machine_shape": "hm",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}