445 lines
432 KiB
Plaintext
445 lines
432 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
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"plt.rcParams[\"font.size\"] = 16\n",
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"\n",
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"g_label_fontsize = 16\n",
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"\n",
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"colors = [\n",
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" \"#455D7A\",\n",
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" \"#233142\",\n",
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" \"#F95959\",\n",
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"]\n",
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"\n",
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"edgecolors = [\n",
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" \"#FFFFFF\",\n",
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" \"#FFFFFF\",\n",
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" \"#FFFFFF\",\n",
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"]\n",
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"\n",
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"hatches = [\"\\\\\\\\\", \"\", \"\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 2100x2470.59 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, ax = plt.subplots(\n",
|
|
" figsize=(7, 14 / 1.7), ncols=1, nrows=3, constrained_layout=True, dpi=300\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"labels_name = [\n",
|
|
" \"ModelA\\n8FSDP\",\n",
|
|
" \"ModelA\\n16FSDP\",\n",
|
|
" \"ModelB\\n8FSDP\",\n",
|
|
" \"ModelB\\n16FSDP\",\n",
|
|
" \"ModelC\\n8FSDP\",\n",
|
|
" \"ModelC\\n16FSDP\",\n",
|
|
" \"ModelD\\n8FSDP\",\n",
|
|
" \"ModelD\\n16FSDP\",\n",
|
|
"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# MFU\n",
|
|
"data_a = {\n",
|
|
" labels_name[0]: [53, 53, 65], # 22.64\n",
|
|
" labels_name[1]: [53, 53, 65], # 22.64\n",
|
|
" labels_name[2]: [35, 35, 48], # 37\n",
|
|
" labels_name[3]: [35, 35, 48], # 37\n",
|
|
" labels_name[4]: [53, 53, 65], # 22.64\n",
|
|
" labels_name[5]: [53, 53, 65], # 22.64\n",
|
|
" labels_name[6]: [53, 53, 65], # 22.64\n",
|
|
" labels_name[7]: [53, 53, 65], # 22.64\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 48,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# overlapping rate\n",
|
|
"data_b = {\n",
|
|
" labels_name[0]: [54, 62, 12],\n",
|
|
" labels_name[1]: [54, 62, 12],\n",
|
|
" labels_name[2]: [52, 62, 12],\n",
|
|
" labels_name[3]: [52, 62, 12],\n",
|
|
" labels_name[4]: [52, 62, 12],\n",
|
|
" labels_name[5]: [52, 62, 12],\n",
|
|
" labels_name[6]: [53, 62, 12],\n",
|
|
" labels_name[7]: [52, 62, 12],\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 49,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# communication baseline\n",
|
|
"data_c = {\n",
|
|
" labels_name[0]: 70,\n",
|
|
" labels_name[1]: 70,\n",
|
|
" labels_name[2]: 70,\n",
|
|
" labels_name[3]: 70,\n",
|
|
" labels_name[4]: 70,\n",
|
|
" labels_name[5]: 70,\n",
|
|
" labels_name[6]: 70,\n",
|
|
" labels_name[7]: 70,\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 50,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"data_d = {\n",
|
|
" labels_name[0]: [124, 124, 128], # 22.64\n",
|
|
" labels_name[1]: [124, 124, 128], # 22.64\n",
|
|
" labels_name[2]: [124, 124, 128], # 37\n",
|
|
" labels_name[3]: [124, 124, 128], # 37\n",
|
|
" labels_name[4]: [124, 124, 128], # 22.64\n",
|
|
" labels_name[5]: [124, 124, 128], # 22.64\n",
|
|
" labels_name[6]: [124, 124, 128], # 22.64\n",
|
|
" labels_name[7]: [124, 124, 128], # 22.64\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 51,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"legend_labels = [\"FSDP\", \"XLA\", \"DLRover-Lynx\"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 52,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"bar_width = 0.2\n",
|
|
"group_spaing = 0.15\n",
|
|
"\n",
|
|
"group_positions = {}\n",
|
|
"current_pos = 0\n",
|
|
"\n",
|
|
"for x_label, y_data in data_a.items():\n",
|
|
" group_positions[x_label] = []\n",
|
|
" for i in range(len(y_data)):\n",
|
|
" group_positions[x_label].append(current_pos)\n",
|
|
" current_pos += bar_width\n",
|
|
" current_pos += group_spaing\n",
|
|
"\n",
|
|
"group_centers = {}\n",
|
|
"for x_label, positions in group_positions.items():\n",
|
|
" group_centers[x_label] = sum(positions) / len(positions)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 53,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0.5, 1.0, '(a)')"
|
|
]
|
|
},
|
|
"execution_count": 53,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"label_set = set()\n",
|
|
"for x_label, y_data in data_a.items():\n",
|
|
" positions = group_positions[x_label]\n",
|
|
" for i, (pos, value, color, edgecolor, hatch, label) in enumerate(\n",
|
|
" zip(\n",
|
|
" positions,\n",
|
|
" y_data,\n",
|
|
" colors,\n",
|
|
" edgecolors,\n",
|
|
" hatches,\n",
|
|
" legend_labels,\n",
|
|
" )\n",
|
|
" ):\n",
|
|
" if label in label_set:\n",
|
|
" local_label = None\n",
|
|
" else:\n",
|
|
" local_label = label\n",
|
|
" label_set.add(local_label)\n",
|
|
" ax[0].bar(\n",
|
|
" pos,\n",
|
|
" value,\n",
|
|
" width=bar_width,\n",
|
|
" color=color,\n",
|
|
" label=local_label,\n",
|
|
" hatch=hatch,\n",
|
|
" edgecolor=edgecolor,\n",
|
|
" )\n",
|
|
"\n",
|
|
"ax[0].set_xticks(list(group_centers.values()))\n",
|
|
"ax[0].set_xticklabels(list(data_a.keys()))\n",
|
|
"\n",
|
|
"ax[0].set_ylim(0, 100)\n",
|
|
"ax[0].set_yticks([0, 50, 100])\n",
|
|
"ax[0].set_yticklabels([\"0\", \"50\", \"100\"], rotation=90, ha=\"center\", va=\"center\")\n",
|
|
"\n",
|
|
"ax[0].tick_params(axis=\"x\", bottom=False, labelsize=11, pad=1)\n",
|
|
"ax[0].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
|
|
"\n",
|
|
"ax[0].set_ylabel(\"MFU (%)\", fontsize=g_label_fontsize)\n",
|
|
"ax[0].set_title(\"(a)\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 54,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0.5, 1.0, '(b)')"
|
|
]
|
|
},
|
|
"execution_count": 54,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"for x_label, y_data in data_d.items():\n",
|
|
" positions = group_positions[x_label]\n",
|
|
" for i, (pos, value, color, edgecolor, hatch) in enumerate(\n",
|
|
" zip(\n",
|
|
" positions,\n",
|
|
" y_data,\n",
|
|
" colors,\n",
|
|
" edgecolors,\n",
|
|
" hatches,\n",
|
|
" )\n",
|
|
" ):\n",
|
|
" ax[1].bar(\n",
|
|
" pos,\n",
|
|
" value,\n",
|
|
" width=bar_width,\n",
|
|
" color=color,\n",
|
|
" edgecolor=edgecolor,\n",
|
|
" hatch=hatch,\n",
|
|
" label=local_label,\n",
|
|
" )\n",
|
|
"\n",
|
|
"\n",
|
|
"ax[1].set_xticks(list(group_centers.values()))\n",
|
|
"ax[1].set_xticklabels(list(data_a.keys()))\n",
|
|
"\n",
|
|
"ax[1].set_ylim(0, 150)\n",
|
|
"ax[1].set_yticks([0, 50, 100])\n",
|
|
"ax[1].set_yticklabels([\"0\", \"50\", \"100\"], rotation=90, ha=\"center\", va=\"center\")\n",
|
|
"\n",
|
|
"ax[1].tick_params(axis=\"x\", bottom=False, labelsize=11, pad=1)\n",
|
|
"ax[1].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
|
|
"\n",
|
|
"ax[1].set_ylabel(\"Throughput (tokens/s)\", fontsize=g_label_fontsize)\n",
|
|
"ax[1].set_title(\"(b)\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 55,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Text(0.5, 1.0, '(c)')"
|
|
]
|
|
},
|
|
"execution_count": 55,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"label_set = set()\n",
|
|
"total_label = \"Non-Overlap Communication\"\n",
|
|
"\n",
|
|
"for x_label, y_data in data_b.items():\n",
|
|
" positions = group_positions[x_label]\n",
|
|
" for i, (pos, value, diff_value, color, edgecolor, label, hatch) in enumerate(\n",
|
|
" zip(\n",
|
|
" positions,\n",
|
|
" y_data,\n",
|
|
" list(data_c.values()),\n",
|
|
" colors,\n",
|
|
" edgecolors,\n",
|
|
" legend_labels,\n",
|
|
" hatches,\n",
|
|
" )\n",
|
|
" ):\n",
|
|
" if label in label_set:\n",
|
|
" local_label = None\n",
|
|
" else:\n",
|
|
" local_label = label\n",
|
|
" label_set.add(local_label)\n",
|
|
"\n",
|
|
" ax[2].bar(\n",
|
|
" pos,\n",
|
|
" value,\n",
|
|
" width=bar_width,\n",
|
|
" color=color,\n",
|
|
" edgecolor=edgecolor,\n",
|
|
" hatch=hatch,\n",
|
|
" #label=local_label,\n",
|
|
" )\n",
|
|
"\n",
|
|
" ax[2].bar(\n",
|
|
" pos,\n",
|
|
" diff_value - value,\n",
|
|
" bottom=value,\n",
|
|
" color=\"#E3E3E3\",\n",
|
|
" edgecolor=edgecolor,\n",
|
|
" width=bar_width,\n",
|
|
" label=total_label,\n",
|
|
" )\n",
|
|
" if total_label is not None:\n",
|
|
" total_label = None\n",
|
|
"\n",
|
|
"ax[2].set_xticks(list(group_centers.values()))\n",
|
|
"ax[2].set_xticklabels(list(data_a.keys()))\n",
|
|
"\n",
|
|
"ax[2].set_ylim(0, 100)\n",
|
|
"ax[2].set_yticks([0, 50, 100])\n",
|
|
"ax[2].set_yticklabels([\"0\", \"50\", \"100\"], rotation=90, ha=\"center\", va=\"center\")\n",
|
|
"\n",
|
|
"ax[2].tick_params(axis=\"x\", bottom=False, labelsize=11, pad=1)\n",
|
|
"ax[2].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
|
|
"\n",
|
|
"ax[2].set_ylabel(\"Comm-Comp Overlap Ratio (%)\", fontsize=g_label_fontsize)\n",
|
|
"ax[2].set_title(\"(c)\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 56,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.legend.Legend at 0x7f47dd28e210>"
|
|
]
|
|
},
|
|
"execution_count": 56,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"ax[0].legend(\n",
|
|
" ncol=3,\n",
|
|
" loc=\"upper center\",\n",
|
|
" frameon=False,\n",
|
|
" shadow=False,\n",
|
|
" fontsize=g_label_fontsize,\n",
|
|
")\n",
|
|
"\n",
|
|
"ax[2].legend(\n",
|
|
" ncol=1,\n",
|
|
" loc=\"upper center\",\n",
|
|
" frameon=False,\n",
|
|
" shadow=False,\n",
|
|
" fontsize=g_label_fontsize,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 57,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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|
|
"text/plain": [
|
|
"<Figure size 2100x2470.59 with 3 Axes>"
|
|
]
|
|
},
|
|
"execution_count": 57,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig.savefig(\"end_to_end_fsdp.pdf\", bbox_inches=\"tight\", dpi=1000)\n",
|
|
"fig"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".venv",
|
|
"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": "3.12.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|