update mmap section

ch05/08_memory_efficient_weight_loading/memory-efficient-state-dict.ipynb

@@ -752,7 +752,7 @@
    "metadata": {},
    "source": [
     " \n",
-    "## 6. Using `mmap=True`"
+    "## 6. Using `mmap=True` (recommmended)"
    ]
   },
   {
@@ -760,19 +760,20 @@
    "metadata": {},
    "source": [
     "- As an intermediate or advanced `torch.load` user, you may wonder how these approaches compare to the `mmap=True` setting in PyTorch\n",
-    "- The `mmap=True` setting in PyTorch enables memory-mapped file I/O, which allows the tensor to access data directly from disk storage, thus reducing memory usage by not loading the entire file into RAM\n",
-    "- However, in practice, I found it to be less efficient than the sequential approaches above"
+    "- The `mmap=True` setting in PyTorch enables memory-mapped file I/O, which allows the tensor to access data directly from disk storage, thus reducing memory usage by not loading the entire file into RAM if RAM is limited\n",
+    "- Also, see the helpful comment by [mikaylagawarecki](https://github.com/rasbt/LLMs-from-scratch/issues/402)\n",
+    "- At first glance, it may look less efficient than the sequential approaches above:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 37,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
-    "id": "7AX3vPrpv5c_",
-    "outputId": "e6fca10b-55c3-4e89-8674-075df5ce26e7"
+    "id": "GKwV0AMNemuR",
+    "outputId": "e207f2bf-5c87-498e-80fe-e8c4016ac711"
    },
    "outputs": [
     {
@@ -780,63 +781,32 @@
      "output_type": "stream",
      "text": [
       "Maximum GPU memory allocated: 6.4 GB\n",
-      "-> Maximum CPU memory allocated: 9.9 GB\n"
+      "-> Maximum CPU memory allocated: 5.9 GB\n"
      ]
     }
    ],
    "source": [
-    "def baseline_mmap():\n",
-    "    start_memory_tracking()\n",
-    "\n",
-    "    model = GPTModel(BASE_CONFIG)  # load model on CPU\n",
+    "def best_practices():\n",
+    "  with torch.device(\"meta\"):\n",
+    "      model = GPTModel(BASE_CONFIG)\n",
     "\n",
     "  model.load_state_dict(\n",
-    "        torch.load(\"model.pth\", map_location=\"cpu\", weights_only=True, mmap=True)\n",
+    "      torch.load(\"model.pth\", map_location=device, weights_only=True, mmap=True),\n",
+    "      assign=True\n",
     "  )\n",
-    "    model.to(device)  # Move model to GPU\n",
-    "    model.eval();\n",
     "\n",
     "  print_memory_usage()\n",
     "\n",
-    "peak_memory_used = memory_usage_in_gb(baseline_mmap)\n",
+    "peak_memory_used = memory_usage_in_gb(best_practices)\n",
     "print(f\"-> Maximum CPU memory allocated: {peak_memory_used:.1f} GB\")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "KUyK3QVRwmjR",
-    "outputId": "a77c191a-2f9e-4ae5-be19-8ce128e704e9"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Maximum GPU memory allocated: 12.8 GB\n",
-      "-> Maximum CPU memory allocated: 7.0 GB\n"
-     ]
-    }
-   ],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "def baseline_mmap_2():\n",
-    "    start_memory_tracking()\n",
-    "\n",
-    "    model = GPTModel(BASE_CONFIG).to(device)\n",
-    "\n",
-    "    model.load_state_dict(\n",
-    "        torch.load(\"model.pth\", map_location=device, weights_only=True, mmap=True)\n",
-    "    )\n",
-    "    model.eval();\n",
-    "\n",
-    "    print_memory_usage()\n",
-    "\n",
-    "peak_memory_used = memory_usage_in_gb(baseline_mmap_2)\n",
-    "print(f\"-> Maximum CPU memory allocated: {peak_memory_used:.1f} GB\")"
+    "- The reason why the CPU RAM usage is so high is that there's enough CPU RAM available on this machine\n",
+    "- However, if you were to run this on a machine with limited CPU RAM, the `mmap` approach would use less memory"
    ]
   },
   {
@@ -851,8 +821,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- This notebook is focused on simple, built-in methods for loading weights in PyTorch.\n",
-    "- In case none of these methods work because you (1) don't have enough CPU memory for the `load_sequentially` approach and don't have enough GPU VRAM to have 2 copies of the weights in memory (the `load_sequentially_with_meta` approach), one option is to save and load each weight tensor separately:"
+    "- This notebook is focused on simple, built-in methods for loading weights in PyTorch\n",
+    "- The recommended approach for limited CPU memory cases is the `mmap=True` approach explained enough\n",
+    "- Alternatively, one other option is a brute-force approach that saves and loads each weight tensor separately:"
    ]
   },
   {