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Add and link bonus material (#84)
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@@ -18,7 +18,7 @@
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"id": "6f678e62-7bcb-4405-86ae-dce94f494303"
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},
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"source": [
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"# Efficient Multi-Head Attention Implementations"
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"# Comparing Efficient Multi-Head Attention Implementations"
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]
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},
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{
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@@ -73,6 +73,9 @@
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"id": "2f9bb1b6-a1e5-4e0a-884d-0f31b374a8d6"
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},
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"source": [
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"<br>\n",
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" \n",
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"\n",
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"## 1) CausalAttention MHA wrapper class from chapter 3"
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]
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},
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@@ -119,6 +122,9 @@
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"id": "21930804-b327-40b1-8e63-94dcad39ce7b"
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},
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"source": [
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"<br>\n",
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" \n",
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"\n",
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"## 2) The multi-head attention class from chapter 3"
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]
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},
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@@ -165,6 +171,9 @@
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"id": "73cd11da-ea3b-4081-b483-c4965dfefbc4"
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},
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"source": [
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"<br>\n",
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" \n",
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"\n",
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"## 3) An alternative multi-head attention with combined weights"
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]
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},
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@@ -286,6 +295,9 @@
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"id": "48a042d3-ee78-4c29-bf63-d92fe6706632"
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},
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"source": [
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"<br>\n",
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" \n",
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"\n",
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"## 4) Multihead attention with PyTorch's scaled dot product attention"
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]
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},
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@@ -393,6 +405,9 @@
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"id": "351c318f-4835-4d74-8d58-a070222447c4"
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},
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"source": [
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"<br>\n",
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" \n",
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"\n",
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"## 5) Using PyTorch's torch.nn.MultiheadAttention"
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]
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},
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@@ -488,6 +503,9 @@
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"id": "a3953bff-1056-4de2-bfd1-dfccf659eee4"
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},
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"source": [
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"<br>\n",
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" \n",
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"\n",
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"## 6) Using PyTorch's torch.nn.MultiheadAttention with `scaled_dot_product_attention`"
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]
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},
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@@ -548,6 +566,9 @@
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"id": "8877de71-f84f-4f6d-bc87-7552013b6301"
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},
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"source": [
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"<br>\n",
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" \n",
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"\n",
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"## Quick speed comparison (M3 Macbook Air CPU)"
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]
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},
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@@ -706,6 +727,9 @@
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"id": "a78ff594-6cc2-496d-a302-789fa104c3c9"
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},
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"source": [
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"<br>\n",
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" \n",
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"\n",
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"## Quick speed comparison (Nvidia A100 GPU)"
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]
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},
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@@ -866,6 +890,10 @@
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"id": "dabc6575-0316-4640-a729-e616d5c17b73"
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},
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"source": [
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"<br>\n",
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" \n",
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"\n",
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"\n",
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"## Speed comparison (Nvidia A100 GPU) with warmup"
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]
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},
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@@ -1003,7 +1031,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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