debug.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import tempfile\n",
    "import json\n",
    "\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "import ray\n",
    "import click\n",
    "import torch\n",
    "\n",
    "from srtml import serve\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_formats = ['svg']\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style='whitegrid', palette='muted')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-12-04 22:30:09,479\tINFO resource_spec.py:212 -- Starting Ray with 330.03 GiB memory available for workers and up to 0.09 GiB for objects. You can adjust these settings with ray.init(memory=<bytes>, object_store_memory=<bytes>).\n",
      "2020-12-04 22:30:09,866\tINFO services.py:1170 -- View the Ray dashboard at \u001b[1m\u001b[32mlocalhost:8265\u001b[39m\u001b[22m\n"
     ]
    }
   ],
   "source": [
    "serve.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import BertTokenizer, BertConfig\n",
    "import torch\n",
    "from transformers.modeling_bert import BertLayer, BertEmbeddings, BertPooler, BertPreTrainingHeads\n",
    "from torch.nn import LayerNorm as BertLayerNorm\n",
    "\n",
    "class Stage0(torch.nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super(Stage0, self).__init__()\n",
    "        self.embedding_layer = BertEmbeddings(config)\n",
    "        self.layers = []\n",
    "#         print(config.num_hidden_layers)\n",
    "        for i in range(config.num_hidden_layers // 8):\n",
    "            self.layers.append(BertLayer(config))\n",
    "        len(self.layers)\n",
    "        self.layers = torch.nn.ModuleList(self.layers)\n",
    "        self.config = config\n",
    "        self.apply(self.init_bert_weights)\n",
    "\n",
    "    def init_bert_weights(self, module):\n",
    "        if isinstance(module, (torch.nn.Linear, torch.nn.Embedding)):\n",
    "            module.weight.data.normal_(mean=0.0,\n",
    "                                       std=self.config.initializer_range)\n",
    "        elif isinstance(module, BertLayerNorm):\n",
    "            module.bias.data.zero_()\n",
    "            module.weight.data.fill_(1.0)\n",
    "        if isinstance(module, torch.nn.Linear) and module.bias is not None:\n",
    "            module.bias.data.zero_()\n",
    "\n",
    "    def forward(self, input0, input1):\n",
    "        out0 = input0\n",
    "        out1 = input1\n",
    "        out = self.embedding_layer(out0, out1)\n",
    "        for layer in self.layers:\n",
    "            out,  = layer(out)\n",
    "        return out\n",
    "\n",
    "class Stage1(torch.nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super(Stage1, self).__init__()\n",
    "        self.layers = []\n",
    "        for i in range(config.num_hidden_layers // 8):\n",
    "            self.layers.append(BertLayer(config))\n",
    "        len(self.layers)\n",
    "        self.layers = torch.nn.ModuleList(self.layers)\n",
    "#         self.pooling_layer = BertPooler(config)\n",
    "#         self.pre_training_heads_layer = BertPreTrainingHeads(config)\n",
    "        self.config = config\n",
    "        self.apply(self.init_bert_weights)\n",
    "\n",
    "    def init_bert_weights(self, module):\n",
    "        if isinstance(module, (torch.nn.Linear, torch.nn.Embedding)):\n",
    "            module.weight.data.normal_(mean=0.0,\n",
    "                                       std=self.config.initializer_range)\n",
    "        elif isinstance(module, BertLayerNorm):\n",
    "            module.bias.data.zero_()\n",
    "            module.weight.data.fill_(1.0)\n",
    "        if isinstance(module, torch.nn.Linear) and module.bias is not None:\n",
    "            module.bias.data.zero_()\n",
    "\n",
    "    def forward(self, input0):\n",
    "        out = input0\n",
    "        for layer in self.layers:\n",
    "            out,  = layer(out)\n",
    "#         out2 = self.pooling_layer(out)\n",
    "#         out3 = self.pre_training_heads_layer(out, out2)\n",
    "        return out\n",
    "\n",
    "class Stage2(torch.nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super(Stage2, self).__init__()\n",
    "        self.layers = []\n",
    "        for i in range(config.num_hidden_layers // 8):\n",
    "            self.layers.append(BertLayer(config))\n",
    "        len(self.layers)\n",
    "        self.layers = torch.nn.ModuleList(self.layers)\n",
    "#         self.pooling_layer = BertPooler(config)\n",
    "#         self.pre_training_heads_layer = BertPreTrainingHeads(config)\n",
    "        self.config = config\n",
    "        self.apply(self.init_bert_weights)\n",
    "\n",
    "    def init_bert_weights(self, module):\n",
    "        if isinstance(module, (torch.nn.Linear, torch.nn.Embedding)):\n",
    "            module.weight.data.normal_(mean=0.0,\n",
    "                                       std=self.config.initializer_range)\n",
    "        elif isinstance(module, BertLayerNorm):\n",
    "            module.bias.data.zero_()\n",
    "            module.weight.data.fill_(1.0)\n",
    "        if isinstance(module, torch.nn.Linear) and module.bias is not None:\n",
    "            module.bias.data.zero_()\n",
    "\n",
    "    def forward(self, input0):\n",
    "        out = input0\n",
    "        for layer in self.layers:\n",
    "            out,  = layer(out)\n",
    "#         out2 = self.pooling_layer(out)\n",
    "#         out3 = self.pre_training_heads_layer(out, out2)\n",
    "        return out\n",
    "\n",
    "class Stage3(torch.nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super(Stage3, self).__init__()\n",
    "        self.layers = []\n",
    "        for i in range(config.num_hidden_layers // 8):\n",
    "            self.layers.append(BertLayer(config))\n",
    "        len(self.layers)\n",
    "        self.layers = torch.nn.ModuleList(self.layers)\n",
    "        self.pooling_layer = BertPooler(config)\n",
    "        self.pre_training_heads_layer = BertPreTrainingHeads(config)\n",
    "        self.config = config\n",
    "        self.apply(self.init_bert_weights)\n",
    "\n",
    "    def init_bert_weights(self, module):\n",
    "        if isinstance(module, (torch.nn.Linear, torch.nn.Embedding)):\n",
    "            module.weight.data.normal_(mean=0.0,\n",
    "                                       std=self.config.initializer_range)\n",
    "        elif isinstance(module, BertLayerNorm):\n",
    "            module.bias.data.zero_()\n",
    "            module.weight.data.fill_(1.0)\n",
    "        if isinstance(module, torch.nn.Linear) and module.bias is not None:\n",
    "            module.bias.data.zero_()\n",
    "\n",
    "    def forward(self, input0):\n",
    "        out = input0\n",
    "        for layer in self.layers:\n",
    "            out,  = layer(out)\n",
    "#         out2 = self.pooling_layer(out)\n",
    "#         out3 = self.pre_training_heads_layer(out, out2)\n",
    "        return out\n",
    "\n",
    "##################################################################################################\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "return_type = 'torch'\n",
    "batch_size = 1\n",
    "def noop(*args, **kwargs):\n",
    "    bs = serve.context.batch_size\n",
    "    time.sleep(0.1)\n",
    "#     assert (\n",
    "#         bs == batch_size\n",
    "#     ), f\"worker received {batch_size} which is not what expected {batch_size}\"\n",
    "    result = \"\"\n",
    "    \n",
    "    if return_type == \"torch\":\n",
    "        result = torch.zeros((3, 224, 224))\n",
    "\n",
    "    if bs is None:  # No batching\n",
    "        return result\n",
    "    else:\n",
    "        return [result] * bs\n",
    "\n",
    "noop = serve.accept_batch(noop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "bert_config = BertConfig.from_pretrained('bert-large-uncased')\n",
    "def stage0(*args, data, **kwargs):\n",
    "\n",
    "    model = Stage0(bert_config)\n",
    "    model.cuda()\n",
    "    input0 = torch.stack([item['input_ids'][0] for item in data])\n",
    "    input1 = torch.stack([item['attention_mask'][0] for item in data])\n",
    "\n",
    "    input0 = input0.cuda()\n",
    "    input1 = input1.cuda()\n",
    "    outputs = model(input0, input1)\n",
    "    res = list(outputs.cpu().unbind())\n",
    "    return res\n",
    "\n",
    "def stage1(*args, data, **kwargs):\n",
    "    model = Stage1(bert_config)\n",
    "    model.cuda()\n",
    "#     print('stage1: ', data[0].shape)\n",
    "    input0 = torch.stack(data)\n",
    "    input0 = input0.cuda()\n",
    "    outputs = model(input0)\n",
    "    res = list(outputs.cpu().unbind())\n",
    "    return res\n",
    "\n",
    "def stage2(*args, data, **kwargs):\n",
    "    model = Stage2(bert_config)\n",
    "    model.cuda()\n",
    "#     print('stage2: ', data[0].shape)\n",
    "    input0 = torch.stack(data)\n",
    "    input0 = input0.cuda()\n",
    "    outputs = model(input0)\n",
    "    res = list(outputs.cpu().unbind())\n",
    "    return res\n",
    "\n",
    "def stage3(*args, data, **kwargs):\n",
    "    model = Stage3(bert_config)\n",
    "    model.cuda()\n",
    "#     print('stage3: ', data[0].shape)\n",
    "    input0 = torch.stack(data)\n",
    "    input0 = input0.cuda()\n",
    "    outputs = model(input0)\n",
    "    res = list(outputs.cpu().unbind())\n",
    "    return res\n",
    "\n",
    "stage3 = serve.accept_batch(stage3)\n",
    "stage2 = serve.accept_batch(stage2)\n",
    "stage1 = serve.accept_batch(stage1)\n",
    "stage0 = serve.accept_batch(stage0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "with serve.using_router(\"noop\"):\n",
    "    serve.create_endpoint(\"noop\", \"/noop\")\n",
    "    config = serve.BackendConfig(max_batch_size=batch_size)\n",
    "#     config = serve.BackendConfig()\n",
    "    serve.create_backend(noop, \"noop\", backend_config=config)\n",
    "    serve.link(\"noop\", \"noop\")\n",
    "    handle = serve.get_handle(\"noop\")\n",
    "\n",
    "num_backends = 4\n",
    "backend = [stage0, stage1, stage2, stage3]\n",
    "handle = [None]*num_backends\n",
    "\n",
    "for i in range(num_backends):\n",
    "    router_name = 'stage' + str(i)\n",
    "    with serve.using_router(router_name):\n",
    "        serve.create_endpoint(router_name, '/'+router_name)\n",
    "        config = serve.BackendConfig(max_batch_size=batch_size, num_gpus=1)\n",
    "    #     config = serve.BackendConfig()\n",
    "        serve.create_backend(backend[i], router_name, backend_config=config)\n",
    "        serve.link(router_name, router_name)\n",
    "        handle[i] = serve.get_handle(router_name)\n",
    "\n",
    "# with serve.using_router(\"noop2\"):\n",
    "#     serve.create_endpoint(\"noop2\", \"/noop2\")\n",
    "#     config = serve.BackendConfig(max_batch_size=batch_size, num_gpus=1)\n",
    "# #     config = serve.BackendConfig()\n",
    "#     serve.create_backend(stage1, \"noop2\", backend_config=config)\n",
    "#     serve.link(\"noop2\", \"noop2\")\n",
    "#     handle2 = serve.get_handle(\"noop2\")\n",
    "\n",
    "num_queries = 30\n",
    "num_warmups = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12/12 [00:43<00:00,  3.65s/it]\n"
     ]
    }
   ],
   "source": [
    "latency = []\n",
    "txt = 'Debugging'\n",
    "tokenizer = BertTokenizer.from_pretrained('bert-large-uncased')\n",
    "encoded = tokenizer(text=txt, add_special_tokens=True,  # Add [CLS] and [SEP]\n",
    "                         max_length = 64,  # maximum length of a sentence\n",
    "                         padding='max_length',  # Add [PAD]s\n",
    "                         return_attention_mask = True,  # Generate the attention mask\n",
    "                         return_tensors = 'pt')\n",
    "\n",
    "prepoc_dummy_kwarg = {\"data\": [txt]}\n",
    "\n",
    "for i in tqdm(range(num_warmups + num_queries)):\n",
    "    if i == num_warmups:\n",
    "        serve.clear_trace()\n",
    "    oids = []\n",
    "    start = time.perf_counter()\n",
    "    x = None\n",
    "    if not batch_size:\n",
    "        ray.get(\n",
    "            # This is how to pass a higher level metadata to the tracing\n",
    "            # context\n",
    "            handle.options(\n",
    "                tracing_metadata={\"pipeline-id\": i}\n",
    "            ).remote()\n",
    "        )\n",
    "    else:\n",
    "#         r = handle1.options(tracing_metadata={\"pipeline-id\": i}).remote(\n",
    "#                 data=encoded\n",
    "#             )\n",
    "#         r = handle2.options(tracing_metadata={\"pipeline-id\": i}).remote(\n",
    "#                  data=r  # torch tensor\n",
    "#             )\n",
    "#         oids.append(r)\n",
    "\n",
    "#         x = ray.get(handle1.enqueue_batch(val=ray.get(handle.enqueue_batch(val=[1] * batch_size))))\n",
    "        #x = ray.get(handle2.options(tracing_metadata={\"pipeline-id\": i}).remote(data=ray.get(handle1.options(tracing_metadata={\"pipeline-id\": i}).remote(data = encoded))))\n",
    "#         ray.get([handle.remote() for _ in range(batch_size)])\n",
    "#         print(x)\n",
    "#         x1 = ray.get(handle[0].options(tracing_metadata={'pipeline-id':i}).remote(data=encoded))\n",
    "#         x2 = ray.get(handle[1].options(tracing_metadata={'pipeline-id':i}).remote(data=ray.get(handle[0].options(tracing_metadata={'pipeline-id':i}).remote(data=encoded))))\n",
    "#         x3 = ray.get(handle[2].options(tracing_metadata={'pipeline-id':i}).remote(data=ray.get(handle[1].options(tracing_metadata={'pipeline-id':i}).remote(data=ray.get(handle[0].options(tracing_metadata={'pipeline-id':i}).remote(data=encoded))))))\n",
    "#         x4 = ray.get(handle[1].options(tracing_metadata={\"pipeline-id\": i}).remote(data=ray.get(handle[0].options(tracing_metadata={\"pipeline-id\": i}).remote(data = encoded))))\n",
    "        x4 = ray.get(handle[3].options(tracing_metadata={'pipeline-id':i}).remote(data=ray.get(handle[2].options(tracing_metadata={'pipeline-id':i}).remote(data=ray.get(handle[1].options(tracing_metadata={'pipeline-id':i}).remote(data=ray.get(handle[0].options(tracing_metadata={'pipeline-id':i}).remote(data=encoded))))))))\n",
    "#     x = ray.wait(oids, len(oids))\n",
    "    end = time.perf_counter()\n",
    "    latency.append(end - start)\n",
    "    \n",
    "# Remove initial samples\n",
    "latency = latency[num_warmups:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latency for single noop backend (ms)\n",
      "count       2.000000\n",
      "mean     2631.009342\n",
      "std         1.026083\n",
      "min      2630.283792\n",
      "50%      2631.009342\n",
      "90%      2631.589783\n",
      "95%      2631.662338\n",
      "99%      2631.720382\n",
      "max      2631.734893\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "series = pd.Series(latency) * 1000\n",
    "print(\"Latency for single noop backend (ms)\")\n",
    "print(series.describe(percentiles=[0.5, 0.9, 0.95, 0.99]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trace file written to trace.json\n"
     ]
    }
   ],
   "source": [
    "trace_file = 'trace.json'\n",
    "with open(trace_file, \"w\") as f:\n",
    "    json.dump(serve.get_trace(), f)\n",
    "print(f\"trace file written to {trace_file}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   endpoint pipeline-id backend batch_id\n",
      "11   stage3          10  stage3       10\n",
      "12   stage3          11  stage3       11\n"
     ]
    }
   ],
   "source": [
    "data = serve.get_trace()\n",
    "df = pd.DataFrame(data['metadata'])\n",
    "df = df.transpose()\n",
    "print(df)\n",
    "df = pd.DataFrame(data['traces'])\n",
    "df = df.sort_values(['query_id', 'event', 'time_us']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_us</th>\n",
       "      <th>query_id</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1607139055992979</td>\n",
       "      <td>11</td>\n",
       "      <td>router_dequeue_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1607139056854332</td>\n",
       "      <td>11</td>\n",
       "      <td>router_dequeue_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1607139057293788</td>\n",
       "      <td>11</td>\n",
       "      <td>router_dequeue_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1607139057733020</td>\n",
       "      <td>11</td>\n",
       "      <td>router_dequeue_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1607139055991522</td>\n",
       "      <td>11</td>\n",
       "      <td>router_enqueue_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1607139056852903</td>\n",
       "      <td>11</td>\n",
       "      <td>router_enqueue_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1607139057292253</td>\n",
       "      <td>11</td>\n",
       "      <td>router_enqueue_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1607139057731575</td>\n",
       "      <td>11</td>\n",
       "      <td>router_enqueue_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1607139056846856</td>\n",
       "      <td>11</td>\n",
       "      <td>router_recv_result_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1607139057285660</td>\n",
       "      <td>11</td>\n",
       "      <td>router_recv_result_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1607139057725093</td>\n",
       "      <td>11</td>\n",
       "      <td>router_recv_result_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1607139058617599</td>\n",
       "      <td>11</td>\n",
       "      <td>router_recv_result_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1607139056844012</td>\n",
       "      <td>11</td>\n",
       "      <td>worker_done_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1607139057282891</td>\n",
       "      <td>11</td>\n",
       "      <td>worker_done_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1607139057722398</td>\n",
       "      <td>11</td>\n",
       "      <td>worker_done_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1607139058614825</td>\n",
       "      <td>11</td>\n",
       "      <td>worker_done_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1607139055994133</td>\n",
       "      <td>11</td>\n",
       "      <td>worker_start_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1607139056856036</td>\n",
       "      <td>11</td>\n",
       "      <td>worker_start_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1607139057295839</td>\n",
       "      <td>11</td>\n",
       "      <td>worker_start_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1607139057735425</td>\n",
       "      <td>11</td>\n",
       "      <td>worker_start_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1607139058626347</td>\n",
       "      <td>12</td>\n",
       "      <td>router_dequeue_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1607139059476839</td>\n",
       "      <td>12</td>\n",
       "      <td>router_dequeue_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1607139059926938</td>\n",
       "      <td>12</td>\n",
       "      <td>router_dequeue_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1607139060365992</td>\n",
       "      <td>12</td>\n",
       "      <td>router_dequeue_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1607139058624922</td>\n",
       "      <td>12</td>\n",
       "      <td>router_enqueue_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1607139059475383</td>\n",
       "      <td>12</td>\n",
       "      <td>router_enqueue_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1607139059925395</td>\n",
       "      <td>12</td>\n",
       "      <td>router_enqueue_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1607139060364578</td>\n",
       "      <td>12</td>\n",
       "      <td>router_enqueue_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1607139059468988</td>\n",
       "      <td>12</td>\n",
       "      <td>router_recv_result_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1607139059918990</td>\n",
       "      <td>12</td>\n",
       "      <td>router_recv_result_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1607139060358534</td>\n",
       "      <td>12</td>\n",
       "      <td>router_recv_result_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1607139061248905</td>\n",
       "      <td>12</td>\n",
       "      <td>router_recv_result_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1607139059466261</td>\n",
       "      <td>12</td>\n",
       "      <td>worker_done_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1607139059911348</td>\n",
       "      <td>12</td>\n",
       "      <td>worker_done_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1607139060356130</td>\n",
       "      <td>12</td>\n",
       "      <td>worker_done_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1607139061246396</td>\n",
       "      <td>12</td>\n",
       "      <td>worker_done_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1607139058628160</td>\n",
       "      <td>12</td>\n",
       "      <td>worker_start_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1607139059478775</td>\n",
       "      <td>12</td>\n",
       "      <td>worker_start_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1607139059928787</td>\n",
       "      <td>12</td>\n",
       "      <td>worker_start_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1607139060368080</td>\n",
       "      <td>12</td>\n",
       "      <td>worker_start_3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             time_us query_id                 event\n",
       "0   1607139055992979       11      router_dequeue_0\n",
       "1   1607139056854332       11      router_dequeue_1\n",
       "2   1607139057293788       11      router_dequeue_2\n",
       "3   1607139057733020       11      router_dequeue_3\n",
       "4   1607139055991522       11      router_enqueue_0\n",
       "5   1607139056852903       11      router_enqueue_1\n",
       "6   1607139057292253       11      router_enqueue_2\n",
       "7   1607139057731575       11      router_enqueue_3\n",
       "8   1607139056846856       11  router_recv_result_0\n",
       "9   1607139057285660       11  router_recv_result_1\n",
       "10  1607139057725093       11  router_recv_result_2\n",
       "11  1607139058617599       11  router_recv_result_3\n",
       "12  1607139056844012       11         worker_done_0\n",
       "13  1607139057282891       11         worker_done_1\n",
       "14  1607139057722398       11         worker_done_2\n",
       "15  1607139058614825       11         worker_done_3\n",
       "16  1607139055994133       11        worker_start_0\n",
       "17  1607139056856036       11        worker_start_1\n",
       "18  1607139057295839       11        worker_start_2\n",
       "19  1607139057735425       11        worker_start_3\n",
       "20  1607139058626347       12      router_dequeue_0\n",
       "21  1607139059476839       12      router_dequeue_1\n",
       "22  1607139059926938       12      router_dequeue_2\n",
       "23  1607139060365992       12      router_dequeue_3\n",
       "24  1607139058624922       12      router_enqueue_0\n",
       "25  1607139059475383       12      router_enqueue_1\n",
       "26  1607139059925395       12      router_enqueue_2\n",
       "27  1607139060364578       12      router_enqueue_3\n",
       "28  1607139059468988       12  router_recv_result_0\n",
       "29  1607139059918990       12  router_recv_result_1\n",
       "30  1607139060358534       12  router_recv_result_2\n",
       "31  1607139061248905       12  router_recv_result_3\n",
       "32  1607139059466261       12         worker_done_0\n",
       "33  1607139059911348       12         worker_done_1\n",
       "34  1607139060356130       12         worker_done_2\n",
       "35  1607139061246396       12         worker_done_3\n",
       "36  1607139058628160       12        worker_start_0\n",
       "37  1607139059478775       12        worker_start_1\n",
       "38  1607139059928787       12        worker_start_2\n",
       "39  1607139060368080       12        worker_start_3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data['traces'])\n",
    "df = df.sort_values(['query_id', 'event', 'time_us']).reset_index(drop=True)\n",
    "df1 = pd.DataFrame()\n",
    "c = 0\n",
    "prev = None\n",
    "for a,q,i in df.itertuples(index=False):\n",
    "#     print(q)\n",
    "    if i == prev:\n",
    "        c+=1\n",
    "        df1 = df1.append({'query_id': str(q), 'event': i+'_'+str(c)}, ignore_index = True)\n",
    "    else:\n",
    "        c = 0\n",
    "        df1 = df1.append({'query_id': str(q), 'event': i+'_'+str(c)}, ignore_index = True)\n",
    "        prev = i\n",
    "# df.loc[df['query_id'] == 514]\n",
    "# df.head(20)\n",
    "df1.head(20)\n",
    "df[['event', 'query_id']] = df1\n",
    "df.head(50)\n",
    "# df = df.sort_values('time_us')\n",
    "# df.head(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.pivot(index='query_id', columns=\"event\").droplevel(0,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "router_enqueue_0->router_dequeue_0          1.4410\n",
      "router_dequeue_0->worker_start_0            1.4835\n",
      "worker_start_0->worker_done_0             843.9900\n",
      "worker_done_0->router_recv_result_0         2.7855\n",
      "router_recv_result_0->router_enqueue_1      6.2210\n",
      "router_enqueue_1->router_dequeue_1          1.4425\n",
      "router_dequeue_1->worker_start_1            1.8200\n",
      "worker_start_1->worker_done_1             429.7140\n",
      "worker_done_1->router_recv_result_1         5.2055\n",
      "router_recv_result_1->router_enqueue_2      6.4990\n",
      "router_enqueue_2->router_dequeue_2          1.5390\n",
      "router_dequeue_2->worker_start_2            1.9500\n",
      "worker_start_2->worker_done_2             426.9510\n",
      "worker_done_2->router_recv_result_2         2.5495\n",
      "router_recv_result_2->router_enqueue_3      6.2630\n",
      "router_enqueue_3->router_dequeue_3          1.4295\n",
      "router_dequeue_3->worker_start_3            2.2465\n",
      "worker_start_3->worker_done_3             878.8580\n",
      "worker_done_3->router_recv_result_3         2.6415\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "event_ordering = [\n",
    "    'router_enqueue',\n",
    "    \"router_dequeue\",\n",
    "    \"worker_start\",\n",
    "    \"worker_done\",\n",
    "    \"router_recv_result\"\n",
    "]\n",
    "new_event_ordering = []\n",
    "for i in range(num_backends):\n",
    "    for event in event_ordering:\n",
    "          new_event_ordering.append(event+ '_' + str(i)) \n",
    "event_ordering = new_event_ordering\n",
    "series = {}\n",
    "for e_start, e_end in zip(event_ordering, event_ordering[1:]):\n",
    "    series[f\"{e_start}->{e_end}\"] = (df[e_end] - df[e_start])/1000\n",
    "diff_df = pd.DataFrame(series)\n",
    "\n",
    "diff_df.to_csv('data.csv')\n",
    "print(diff_df.mean(axis=0))\n",
    "# diff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/nethome/gkakkar7/miniconda3/envs/srtml-exp/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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       "Text(0.5, 0, 'us')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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    "for name, s in diff_df.T.iterrows():\n",
    "    sns.distplot(s, label=name, kde_kws={\"cumulative\": True})\n",
    "plt.legend()\n",
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