{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a1e738e0",
   "metadata": {},
   "source": [
    "## 获取测试的 logit 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "29d3368b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hlens.npy\n",
      "logits.npy\n",
      "ys_lens.npy\n",
      "ys_pad.npy\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p ./test_data\n",
    "!test -f ./test_data/ctc_loss_compare_data.tgz || wget -P ./test_data https://paddlespeech.bj.bcebos.com/datasets/unit_test/asr/ctc_loss_compare_data.tgz\n",
    "!tar xzvf test_data/ctc_loss_compare_data.tgz -C ./test_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "240caf1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "data_dir=\"./test_data\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91bad949",
   "metadata": {},
   "outputs": [],
   "source": [
    "logits_np = np.load(os.path.join(data_dir, \"logits.npy\"))\n",
    "ys_pad_np = np.load(os.path.join(data_dir, \"ys_pad.npy\"))\n",
    "hlens_np = np.load(os.path.join(data_dir, \"hlens.npy\"))\n",
    "ys_lens_np = np.load(os.path.join(data_dir, \"ys_lens.npy\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cef2f15",
   "metadata": {},
   "source": [
    "## 使用 torch 的 ctc loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "90612004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.10.1+cu102'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "00799f97",
   "metadata": {},
   "outputs": [],
   "source": [
    "def torch_ctc_loss(use_cpu):\n",
    "    if use_cpu:\n",
    "        device = torch.device(\"cpu\")\n",
    "    else:\n",
    "        device = torch.device(\"cuda\")\n",
    "\n",
    "    reduction_type = \"sum\" \n",
    "\n",
    "    ctc_loss = torch.nn.CTCLoss(reduction=reduction_type)\n",
    "\n",
    "    ys_hat = torch.tensor(logits_np, device = device)\n",
    "    ys_pad = torch.tensor(ys_pad_np, device = device)\n",
    "    hlens = torch.tensor(hlens_np, device = device)\n",
    "    ys_lens = torch.tensor(ys_lens_np, device = device)\n",
    "\n",
    "    ys_hat = ys_hat.transpose(0, 1)\n",
    "    \n",
    "    # 开始计算时间\n",
    "    start_time = time.time()\n",
    "    ys_hat = ys_hat.log_softmax(2)\n",
    "    loss = ctc_loss(ys_hat, ys_pad, hlens, ys_lens)\n",
    "    end_time = time.time()\n",
    "    \n",
    "    loss = loss / ys_hat.size(1)\n",
    "    return end_time - start_time, loss.item()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba47b5a4",
   "metadata": {},
   "source": [
    "## 使用 paddle 的 ctc loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6882a06e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.2'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import paddle\n",
    "paddle.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3cfa3b7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def paddle_ctc_loss(use_cpu):    \n",
    "    import paddle.nn as pn\n",
    "    if use_cpu:\n",
    "        device = \"cpu\"\n",
    "    else:\n",
    "        device = \"gpu\"\n",
    "\n",
    "    paddle.set_device(device)\n",
    "\n",
    "    logits = paddle.to_tensor(logits_np)\n",
    "    ys_pad = paddle.to_tensor(ys_pad_np,dtype='int32')\n",
    "    hlens = paddle.to_tensor(hlens_np, dtype='int64')\n",
    "    ys_lens = paddle.to_tensor(ys_lens_np, dtype='int64')\n",
    "\n",
    "    logits = logits.transpose([1,0,2])\n",
    "\n",
    "    ctc_loss = pn.CTCLoss(reduction='sum')\n",
    "    # 开始计算时间\n",
    "    start_time = time.time()\n",
    "    pn_loss = ctc_loss(logits, ys_pad, hlens, ys_lens)\n",
    "    end_time = time.time()\n",
    "    \n",
    "    pn_loss = pn_loss / logits.shape[1]\n",
    "    return end_time - start_time, pn_loss.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "40413ef9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU, iteration 10\n",
      "torch_ctc_loss 159.17137145996094\n",
      "paddle_ctc_loss 159.16574096679688\n",
      "paddle average time 1.718252992630005\n",
      "torch average time 0.17536230087280275\n",
      "paddle time / torch time (cpu) 9.798303193320452\n",
      "\n",
      "GPU, iteration 10\n",
      "torch_ctc_loss 159.172119140625\n",
      "paddle_ctc_loss 159.17205810546875\n",
      "paddle average time 0.018606925010681154\n",
      "torch average time 0.0026710033416748047\n",
      "paddle time / torch time (gpu) 6.966267963938231\n"
     ]
    }
   ],
   "source": [
    "# 使用 CPU\n",
    "\n",
    "iteration = 10\n",
    "use_cpu = True\n",
    "torch_total_time = 0\n",
    "paddle_total_time = 0\n",
    "for _ in range(iteration):\n",
    "    cost_time, torch_loss = torch_ctc_loss(use_cpu)\n",
    "    torch_total_time += cost_time\n",
    "for _ in range(iteration):\n",
    "    cost_time, paddle_loss = paddle_ctc_loss(use_cpu)\n",
    "    paddle_total_time += cost_time\n",
    "print (\"CPU, iteration\", iteration)\n",
    "print (\"torch_ctc_loss\", torch_loss)\n",
    "print (\"paddle_ctc_loss\", paddle_loss)\n",
    "print (\"paddle average time\", paddle_total_time / iteration)\n",
    "print (\"torch average time\", torch_total_time / iteration)\n",
    "print (\"paddle time / torch time (cpu)\" , paddle_total_time/ torch_total_time)\n",
    "\n",
    "print (\"\")\n",
    "\n",
    "# 使用 GPU\n",
    "\n",
    "use_cpu = False\n",
    "torch_total_time = 0\n",
    "paddle_total_time = 0\n",
    "for _ in range(iteration):\n",
    "    cost_time, torch_loss  = torch_ctc_loss(use_cpu)\n",
    "    torch_total_time += cost_time\n",
    "for _ in range(iteration):\n",
    "    cost_time, paddle_loss = paddle_ctc_loss(use_cpu)\n",
    "    paddle_total_time += cost_time\n",
    "print (\"GPU, iteration\", iteration)\n",
    "print (\"torch_ctc_loss\", torch_loss)\n",
    "print (\"paddle_ctc_loss\", paddle_loss)\n",
    "print (\"paddle average time\", paddle_total_time / iteration)\n",
    "print (\"torch average time\", torch_total_time / iteration)\n",
    "print (\"paddle time / torch time (gpu)\" , paddle_total_time/ torch_total_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cdf8697",
   "metadata": {},
   "source": [
    "## 其他: 使用 PaddleSpeech 中的 ctcloss 查一下loss值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73fad81d",
   "metadata": {},
   "outputs": [],
   "source": [
    "logits_np = np.load(os.path.join(data_dir, \"logits.npy\"))\n",
    "ys_pad_np = np.load(os.path.join(data_dir, \"ys_pad.npy\"))\n",
    "hlens_np = np.load(os.path.join(data_dir, \"hlens.npy\"))\n",
    "ys_lens_np = np.load(os.path.join(data_dir, \"ys_lens.npy\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b41e45d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-02-25 11:34:34.143 | INFO     | paddlespeech.s2t.modules.loss:__init__:41 - CTCLoss Loss reduction: sum, div-bs: True\n",
      "2022-02-25 11:34:34.143 | INFO     | paddlespeech.s2t.modules.loss:__init__:42 - CTCLoss Grad Norm Type: instance\n",
      "2022-02-25 11:34:34.144 | INFO     | paddlespeech.s2t.modules.loss:__init__:73 - CTCLoss() kwargs:{'norm_by_times': True}, not support: {'norm_by_batchsize': False, 'norm_by_total_logits_len': False}\n",
      "loss 159.17205810546875\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/miniconda3/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py:253: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.float32, but right dtype is paddle.int32, the right dtype will convert to paddle.float32\n",
      "  format(lhs_dtype, rhs_dtype, lhs_dtype))\n"
     ]
    }
   ],
   "source": [
    "use_cpu = False\n",
    "\n",
    "from paddlespeech.s2t.modules.loss import CTCLoss\n",
    "\n",
    "if use_cpu:\n",
    "    device = \"cpu\"\n",
    "else:\n",
    "    device = \"gpu\"\n",
    "\n",
    "paddle.set_device(device)\n",
    "\n",
    "blank_id=0\n",
    "reduction_type='sum'\n",
    "batch_average= True\n",
    "grad_norm_type='instance'\n",
    "\n",
    "criterion = CTCLoss(\n",
    "        blank=blank_id,\n",
    "        reduction=reduction_type,\n",
    "        batch_average=batch_average,\n",
    "        grad_norm_type=grad_norm_type)\n",
    "\n",
    "logits = paddle.to_tensor(logits_np)\n",
    "ys_pad = paddle.to_tensor(ys_pad_np,dtype='int32')\n",
    "hlens = paddle.to_tensor(hlens_np, dtype='int64')\n",
    "ys_lens = paddle.to_tensor(ys_lens_np, dtype='int64')\n",
    "\n",
    "pn_ctc_loss = criterion(logits, ys_pad, hlens, ys_lens)\n",
    "print(\"loss\", pn_ctc_loss.item())\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de525d38",
   "metadata": {},
   "source": [
    "## 结论\n",
    "在 CPU 环境下： torch 的 CTC loss 的计算速度是 paddle 的 9.8 倍  \n",
    "在 GPU 环境下： torch 的 CTC loss 的计算速度是 paddle 的 6.87 倍\n",
    "\n",
    "## 其他结论\n",
    "torch 的 ctc loss 在 CPU 和 GPU 下 都没有完全对齐。其中CPU的前向对齐精度大约为 1e-2。 GPU 的前向对齐精度大约为 1e-4 。"
   ]
  }
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