diff --git a/README.md b/README.md
index 2440e75..c223930 100644
--- a/README.md
+++ b/README.md
@@ -41,12 +41,15 @@ Alternatively, you can view this and other files on GitHub at [https://github.co
| Ch 6: Finetuning for Text Classification | Q2 2024 | ... |
| Ch 7: Finetuning with Human Feedback | Q2 2024 | ... |
| Ch 8: Using Large Language Models in Practice | Q2/3 2024 | ... |
-| Appendix A: Introduction to PyTorch* | - [code-part1.ipynb](appendix-A/03_main-chapter-code/code-part1.ipynb)
- [code-part2.ipynb](appendix-A/03_main-chapter-code/code-part2.ipynb)
- [DDP-script.py](appendix-A/03_main-chapter-code/DDP-script.py)
- [exercise-solutions.ipynb](appendix-A/03_main-chapter-code/exercise-solutions.ipynb) | [./appendix-A](./appendix-A) |
+| Appendix A: Introduction to PyTorch | - [code-part1.ipynb](appendix-A/03_main-chapter-code/code-part1.ipynb)
- [code-part2.ipynb](appendix-A/03_main-chapter-code/code-part2.ipynb)
- [DDP-script.py](appendix-A/03_main-chapter-code/DDP-script.py)
- [exercise-solutions.ipynb](appendix-A/03_main-chapter-code/exercise-solutions.ipynb) | [./appendix-A](./appendix-A) |
+| Appendix B: References and Further Reading | No code | |
+| Appendix C: Exercises | No code | |
+
> [!TIP]
-> Please see [this](appendix-A/01_optional-python-setup-preferences) and [this](appendix-A/02_installing-python-libraries) folder if you need more guidance on installing Python and Python packages.)
+> Please see [this](appendix-A/01_optional-python-setup-preferences) and [this](appendix-A/02_installing-python-libraries) folder if you need more guidance on installing Python and Python packages.
diff --git a/ch03/01_main-chapter-code/ch03.ipynb b/ch03/01_main-chapter-code/ch03.ipynb
index 614ae08..734bcdf 100644
--- a/ch03/01_main-chapter-code/ch03.ipynb
+++ b/ch03/01_main-chapter-code/ch03.ipynb
@@ -1637,7 +1637,7 @@
"class MultiHeadAttention(nn.Module):\n",
" def __init__(self, d_in, d_out, block_size, dropout, num_heads, qkv_bias=False):\n",
" super().__init__()\n",
- " assert d_out % num_heads == 0, \"d_out must be divisible by n_heads\"\n",
+ " assert d_out % num_heads == 0, \"d_out must be divisible by num_heads\"\n",
"\n",
" self.d_out = d_out\n",
" self.num_heads = num_heads\n",
@@ -1865,7 +1865,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.4"
+ "version": "3.10.12"
}
},
"nbformat": 4,
diff --git a/ch03/01_main-chapter-code/multihead-attention.ipynb b/ch03/01_main-chapter-code/multihead-attention.ipynb
index c981b2b..2a072d3 100644
--- a/ch03/01_main-chapter-code/multihead-attention.ipynb
+++ b/ch03/01_main-chapter-code/multihead-attention.ipynb
@@ -243,7 +243,7 @@
"class MultiHeadAttention(nn.Module):\n",
" def __init__(self, d_in, d_out, block_size, dropout, num_heads, qkv_bias=False):\n",
" super().__init__()\n",
- " assert d_out % num_heads == 0, \"d_out must be divisible by n_heads\"\n",
+ " assert d_out % num_heads == 0, \"d_out must be divisible by num_heads\"\n",
"\n",
" self.d_out = d_out\n",
" self.num_heads = num_heads\n",
@@ -342,7 +342,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.4"
+ "version": "3.10.12"
}
},
"nbformat": 4,
diff --git a/ch03/02_bonus_efficient-multihead-attention/README.md b/ch03/02_bonus_efficient-multihead-attention/README.md
new file mode 100644
index 0000000..e76a634
--- /dev/null
+++ b/ch03/02_bonus_efficient-multihead-attention/README.md
@@ -0,0 +1,3 @@
+# More Efficient Multi-Head Attention Implementations
+
+- [mha-implementations.ipynb](mha-implementations.ipynb) contains and compares different implementations of multi-head attention
\ No newline at end of file
diff --git a/ch03/02_bonus_efficient-multihead-attention/ch03.py b/ch03/02_bonus_efficient-multihead-attention/ch03.py
new file mode 100644
index 0000000..f7343d7
--- /dev/null
+++ b/ch03/02_bonus_efficient-multihead-attention/ch03.py
@@ -0,0 +1,58 @@
+import torch
+import torch.nn as nn
+
+
+class MultiHeadAttention(nn.Module):
+ def __init__(self, d_in, d_out, block_size, dropout, num_heads, qkv_bias=False):
+ super().__init__()
+ assert d_out % num_heads == 0, "d_out must be divisible by num_heads"
+
+ self.d_out = d_out
+ self.num_heads = num_heads
+ self.head_dim = d_out // num_heads # Reduce the projection dim to match desired output dim
+
+ self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
+ self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
+ self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
+ self.out_proj = nn.Linear(d_out, d_out) # Linear layer to combine head outputs
+ self.dropout = nn.Dropout(dropout)
+ self.register_buffer('mask', torch.triu(torch.ones(block_size, block_size), diagonal=1))
+
+ def forward(self, x):
+ b, num_tokens, d_in = x.shape
+
+ keys = self.W_key(x) # Shape: (b, num_tokens, d_out)
+ queries = self.W_query(x)
+ values = self.W_value(x)
+
+ # We implicitly split the matrix by adding a `num_heads` dimension
+ # Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
+ keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
+ values = values.view(b, num_tokens, self.num_heads, self.head_dim)
+ queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
+
+ # Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
+ keys = keys.transpose(1, 2)
+ queries = queries.transpose(1, 2)
+ values = values.transpose(1, 2)
+
+ # Compute scaled dot-product attention (aka self-attention) with a causal mask
+ attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head
+ # Original mask truncated to the number of tokens and converted to boolean
+ mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
+ # Unsqueeze the mask to match dimensions
+ mask_unsqueezed = mask_bool.unsqueeze(0)
+ # Use the unsqueezed mask to fill attention scores
+ attn_scores.masked_fill_(mask_unsqueezed, -torch.inf)
+
+ attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
+ attn_weights = self.dropout(attn_weights)
+
+ # Shape: (b, num_tokens, num_heads, head_dim)
+ context_vec = (attn_weights @ values).transpose(1, 2)
+
+ # Combine heads, where self.d_out = self.num_heads * self.head_dim
+ context_vec = context_vec.contiguous().view(b, num_tokens, self.d_out)
+ context_vec = self.out_proj(context_vec) # optional projection
+
+ return context_vec
\ No newline at end of file
diff --git a/ch03/02_bonus_efficient-multihead-attention/mha-implementations.ipynb b/ch03/02_bonus_efficient-multihead-attention/mha-implementations.ipynb
new file mode 100644
index 0000000..89e1c40
--- /dev/null
+++ b/ch03/02_bonus_efficient-multihead-attention/mha-implementations.ipynb
@@ -0,0 +1,356 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6f678e62-7bcb-4405-86ae-dce94f494303",
+ "metadata": {},
+ "source": [
+ "# Appendix D: Efficient Multi-Head Attention Implementations"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2f9bb1b6-a1e5-4e0a-884d-0f31b374a8d6",
+ "metadata": {},
+ "source": [
+ "## Multi-head attention implementation from chapter 3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "7898551e-f582-48ac-9f66-3632abe2a93f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "\n",
+ "torch.manual_seed(123)\n",
+ "\n",
+ "batch_size = 8\n",
+ "context_len = 1024\n",
+ "embed_dim = 768\n",
+ "embeddings = torch.randn((batch_size, context_len, embed_dim))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "4ee6a61b-d25c-4a0c-8a59-f285544e3710",
+ "metadata": {},
+ "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",
+ ")\n",
+ "\n",
+ "out = mha_ch03(embeddings)\n",
+ "print(out.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "73cd11da-ea3b-4081-b483-c4965dfefbc4",
+ "metadata": {},
+ "source": [
+ "## An alternative multi-head attention with combined weights"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1fa1a5ea-eaff-4d2d-aaf0-b34cdb6fd4dd",
+ "metadata": {},
+ "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": 3,
+ "id": "9a6bd0a2-f27c-4602-afa0-c96cd295c1a6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch.Size([8, 1024, 768])\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch.nn as nn\n",
+ "\n",
+ "\n",
+ "class MultiHeadAttentionAlt(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_head, 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_alt = MultiHeadAttentionAlt(\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",
+ ")\n",
+ "\n",
+ "out = mha_alt(embeddings)\n",
+ "print(out.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "48a042d3-ee78-4c29-bf63-d92fe6706632",
+ "metadata": {},
+ "source": [
+ "## Multihead attention with PyTorch's scaled dot product attention"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f78e346f-3b85-44e6-9feb-f01131381148",
+ "metadata": {},
+ "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": 4,
+ "id": "1b8e5a0d-1f65-4a03-bf6e-723f0cc428f5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class MultiHeadAttentionPyTorch(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_head, num_tokens, head_dim)\n",
+ " q, k, v = qkv.unbind(0)\n",
+ "\n",
+ " use_dropout = 0. if not self.training else self.dropout\n",
+ " context_vec = torch.nn.functional.scaled_dot_product_attention(q, k, v, \n",
+ " 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": 5,
+ "id": "fbc8ba92-3471-41cb-b1b2-4c0ef5be392b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch.Size([8, 1024, 768])\n"
+ ]
+ }
+ ],
+ "source": [
+ "mha_pytorch = MultiHeadAttentionPyTorch(\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",
+ ")\n",
+ "\n",
+ "out = mha_pytorch(embeddings)\n",
+ "print(out.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8877de71-f84f-4f6d-bc87-7552013b6301",
+ "metadata": {},
+ "source": [
+ "## Speed comparison"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "19db9c2c-8e75-431a-8eef-0b4d8284e6e6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "253 ms ± 9.85 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "%timeit mha_ch03(embeddings)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "aa526ee0-7a88-4f34-a49a-f8f97da83779",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "309 ms ± 26.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "%timeit mha_alt(embeddings)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "cc2b4256-16d8-4c34-9fd0-d4b4af0e60fa",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "90.4 ms ± 719 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "%timeit mha_pytorch(embeddings)"
+ ]
+ }
+ ],
+ "metadata": {
+ "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": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ch03/README.md b/ch03/README.md
index 846044b..b781e2e 100644
--- a/ch03/README.md
+++ b/ch03/README.md
@@ -1,3 +1,4 @@
# Chapter 3: Coding Attention Mechanisms
-- [01_main-chapter-code](01_main-chapter-code) contains the main chapter code.
\ No newline at end of file
+- [01_main-chapter-code](01_main-chapter-code) contains the main chapter code.
+- [02_bonus_efficient-multihead-attention](02_bonus_efficient-multihead-attention) implements and compares different implementation variants of multihead-attention
\ No newline at end of file
diff --git a/ch04/01_main-chapter-code/gpt.py b/ch04/01_main-chapter-code/gpt.py
index f6dde98..8390ddb 100644
--- a/ch04/01_main-chapter-code/gpt.py
+++ b/ch04/01_main-chapter-code/gpt.py
@@ -56,7 +56,7 @@ def create_dataloader_v1(txt, batch_size=4, max_length=256,
class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, block_size, dropout, num_heads, qkv_bias=False):
super().__init__()
- assert d_out % num_heads == 0, "d_out must be divisible by n_heads"
+ assert d_out % num_heads == 0, "d_out must be divisible by num_heads"
self.d_out = d_out
self.num_heads = num_heads
diff --git a/ch04/01_main-chapter-code/previous_chapters.py b/ch04/01_main-chapter-code/previous_chapters.py
index 926dba4..21b2edf 100644
--- a/ch04/01_main-chapter-code/previous_chapters.py
+++ b/ch04/01_main-chapter-code/previous_chapters.py
@@ -45,7 +45,7 @@ def create_dataloader_v1(txt, batch_size=4, max_length=256,
class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, block_size, dropout, num_heads, qkv_bias=False):
super().__init__()
- assert d_out % num_heads == 0, "d_out must be divisible by n_heads"
+ assert d_out % num_heads == 0, "d_out must be divisible by num_heads"
self.d_out = d_out
self.num_heads = num_heads
diff --git a/ch05/02_hparam_tuning/previous_chapters.py b/ch05/02_hparam_tuning/previous_chapters.py
index 6b1a00e..fc8f64b 100644
--- a/ch05/02_hparam_tuning/previous_chapters.py
+++ b/ch05/02_hparam_tuning/previous_chapters.py
@@ -56,7 +56,7 @@ def create_dataloader_v1(txt, batch_size=4, max_length=256,
class MultiHeadAttention(nn.Module):
def __init__(self, d_in, d_out, block_size, dropout, num_heads, qkv_bias=False):
super().__init__()
- assert d_out % num_heads == 0, "d_out must be divisible by n_heads"
+ assert d_out % num_heads == 0, "d_out must be divisible by num_heads"
self.d_out = d_out
self.num_heads = num_heads