ajtulloch/BERT-Large Performance.ipynb Secret

## BERT-Large Performance.ipynb
{
  "cells": [
    {
      "metadata": {
        "toc": true
      },
      "cell_type": "markdown",
      "source": "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#BERT-Large-performance\" data-toc-modified-id=\"BERT-Large-performance-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>BERT-Large performance</a></span><ul class=\"toc-item\"><li><span><a href=\"#FasterTransformer-<->-HuggingFace-weights\" data-toc-modified-id=\"FasterTransformer-<->-HuggingFace-weights-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>FasterTransformer &lt;-&gt; HuggingFace weights</a></span></li><li><span><a href=\"#FasterTransformer.Encoder-implementation\" data-toc-modified-id=\"FasterTransformer.Encoder-implementation-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>FasterTransformer.Encoder implementation</a></span></li><li><span><a href=\"#HuggingFace.Encoder-implementation\" data-toc-modified-id=\"HuggingFace.Encoder-implementation-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>HuggingFace.Encoder implementation</a></span></li><li><span><a href=\"#Construct-inputs-and-encoders\" data-toc-modified-id=\"Construct-inputs-and-encoders-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Construct inputs and encoders</a></span></li><li><span><a href=\"#Check-correctness\" data-toc-modified-id=\"Check-correctness-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Check correctness</a></span></li><li><span><a href=\"#Benchmark\" data-toc-modified-id=\"Benchmark-1.6\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>Benchmark</a></span></li><li><span><a href=\"#Visualization\" data-toc-modified-id=\"Visualization-1.7\"><span class=\"toc-item-num\">1.7&nbsp;&nbsp;</span>Visualization</a></span></li></ul></li></ul></div>"
    },
    {
      "metadata": {},
      "id": "560325b4",
      "cell_type": "markdown",
      "source": "# BERT-Large performance \n\nThis is mostly derived from \n\n- https://github.com/NVIDIA/FasterTransformer/blob/dd4c071755b1b5206c6987add6145eac03f6814d/sample/pytorch/utils/encoder.py\n- https://github.com/NVIDIA/FasterTransformer/blob/dd4c071755b1b5206c6987add6145eac03f6814d/sample/pytorch/encoder_sample.py"
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "6eb154f5",
      "cell_type": "code",
      "source": "import os\n\nimport torch\nfrom transformers import BertConfig\nfrom transformers.modeling_bert import BertEncoder",
      "execution_count": 1,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "To use data.metrics please install scikit-learn. See https://scikit-learn.org/stable/index.html\n"
        }
      ]
    },
    {
      "metadata": {},
      "id": "db1aa986",
      "cell_type": "markdown",
      "source": "## FasterTransformer <-> HuggingFace weights"
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "7af020ce",
      "cell_type": "code",
      "source": "class EncoderWeights(object):\n    def __init__(self, layer_num, hidden_dim, weights=None):\n        \"\"\"weights need be a state_dict of bert model\"\"\"\n        self.layer_num = layer_num\n        self.int8 = False\n        self.hidden_dim = hidden_dim\n        self.weights = {}\n        if weights is None:\n            self._generated_weights = True\n            for i in range(layer_num):\n                pre = 'bert.encoder.layer.' + str(i) + '.'\n                self.weights[pre +\n                             'attention.self.query.weight'] = torch.zeros(\n                                 hidden_dim, hidden_dim)\n                self.weights[pre + 'attention.self.query.bias'] = torch.zeros(\n                    hidden_dim)\n                self.weights[pre + 'attention.self.key.weight'] = torch.zeros(\n                    hidden_dim, hidden_dim)\n                self.weights[pre + 'attention.self.key.bias'] = torch.zeros(\n                    hidden_dim)\n                self.weights[pre +\n                             'attention.self.value.weight'] = torch.zeros(\n                                 hidden_dim, hidden_dim)\n                self.weights[pre + 'attention.self.value.bias'] = torch.zeros(\n                    hidden_dim)\n                self.weights[pre +\n                             'attention.output.dense.weight'] = torch.zeros(\n                                 hidden_dim, hidden_dim)\n                self.weights[pre +\n                             'attention.output.dense.bias'] = torch.zeros(\n                                 hidden_dim)\n                self.weights[\n                    pre + 'attention.output.LayerNorm.weight'] = torch.zeros(\n                        hidden_dim)\n                self.weights[pre +\n                             'attention.output.LayerNorm.bias'] = torch.zeros(\n                                 hidden_dim)\n                self.weights[pre + 'intermediate.dense.weight'] = torch.zeros(\n                    4 * hidden_dim, hidden_dim)\n                self.weights[pre + 'intermediate.dense.bias'] = torch.zeros(\n                    4 * hidden_dim)\n                self.weights[pre + 'output.dense.weight'] = torch.zeros(\n                    hidden_dim, 4 * hidden_dim)\n                self.weights[pre +\n                             'output.dense.bias'] = torch.zeros(hidden_dim)\n                self.weights[pre + 'output.LayerNorm.weight'] = torch.zeros(\n                    hidden_dim)\n                self.weights[pre +\n                             'output.LayerNorm.bias'] = torch.zeros(hidden_dim)\n            for k, v in self.weights.items():\n                if not k.endswith('_amax'):\n                    self.weights[k] = torch.nn.init.uniform_(v, -1, 1)\n        else:\n            self._generated_weights = False\n            for k, v in weights.items():\n                ks = k.split('.')\n                if ks[-2] == 'LayerNorm':\n                    if ks[-1] == 'gamma':\n                        ks[-1] = 'weight'\n                    elif ks[-1] == 'beta':\n                        ks[-1] = 'bias'\n                self.weights['.'.join(ks)] = v\n\n    def listed_weights(self, layer_idx):\n        ret = []\n        pre = 'bert.encoder.layer.' + str(layer_idx) + '.'\n        ret.append(self.weights[pre + 'attention.self.query.weight'])  # 0\n        ret.append(self.weights[pre + 'attention.self.query.bias'])\n        ret.append(self.weights[pre + 'attention.self.key.weight'])  # 2\n        ret.append(self.weights[pre + 'attention.self.key.bias'])\n        ret.append(self.weights[pre + 'attention.self.value.weight'])  # 4\n        ret.append(self.weights[pre + 'attention.self.value.bias'])\n        ret.append(self.weights[pre + 'attention.output.dense.weight'])  # 6\n        ret.append(self.weights[pre + 'attention.output.dense.bias'])\n        ret.append(self.weights[pre + 'attention.output.LayerNorm.weight'])\n        ret.append(self.weights[pre + 'attention.output.LayerNorm.bias'])\n        ret.append(self.weights[pre + 'intermediate.dense.weight'])  # 10\n        ret.append(self.weights[pre + 'intermediate.dense.bias'])\n        ret.append(self.weights[pre + 'output.dense.weight'])  # 12\n        ret.append(self.weights[pre + 'output.dense.bias'])\n        ret.append(self.weights[pre + 'output.LayerNorm.weight'])\n        ret.append(self.weights[pre + 'output.LayerNorm.bias'])\n        if not self.int8:\n            ret[0] = ret[0].transpose(-1, -2).contiguous()\n            ret[2] = ret[2].transpose(-1, -2).contiguous()\n            ret[4] = ret[4].transpose(-1, -2).contiguous()\n            ret[6] = ret[6].transpose(-1, -2).contiguous()\n            ret[10] = ret[10].transpose(-1, -2).contiguous()\n            ret[12] = ret[12].transpose(-1, -2).contiguous()\n            ret.append(torch.tensor(0))\n        else:\n            ret.append(self.weights[pre + 'amaxList'])\n        return ret\n\n    def to_cuda(self):\n        for k, v in self.weights.items():\n            self.weights[k] = v.cuda()\n\n    def to_half(self):\n        if self.int8:\n            raise RuntimeError(\n                \"Cannot cast to half if the weights have been casted to int8.\")\n        for k, v in self.weights.items():\n            self.weights[k] = v.half()",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {},
      "id": "55d063b3",
      "cell_type": "markdown",
      "source": "## FasterTransformer.Encoder implementation "
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "6f9ab629",
      "cell_type": "code",
      "source": "class CustomEncoder(torch.nn.Module):\n    def __init__(self,\n                 layer_num,\n                 head_num,\n                 head_size,\n                 weights,\n                 int8_mode=0,\n                 allow_gemm_test=False,\n                 path='./lib/libpyt_fastertransformer.so'):\n        super().__init__()\n        self.layer_num = layer_num\n        self.remove_padding = True\n        self.int8_mode = int8_mode\n        self.encoders = []\n        use_trt_kernel = False\n        torch.classes.load_library(path)\n        for i in range(layer_num):\n            assert len(weights.listed_weights(i)) == 17\n            self.encoders.append(\n                torch.classes.FasterTransformer.Encoder(\n                    *weights.listed_weights(i), head_num, head_size,\n                    True, int8_mode, layer_num, i, allow_gemm_test,\n                    use_trt_kernel, 1.0))\n        self.build_mask_remove_padding = torch.ops.fastertransformer.build_mask_remove_padding\n        self.rebuild_padding = torch.ops.fastertransformer.rebuild_padding\n\n    def forward(self, hidden_states, attention_mask, sequence_lengths):\n        hidden_states, sequence_id_offset = self.build_mask_remove_padding(\n            hidden_states, sequence_lengths)\n        trt_seq_len = torch.cumsum(torch.cat([\n            torch.tensor([0], device='cuda').to(sequence_lengths),\n            sequence_lengths\n        ],\n                                             dim=0),\n                                   dim=0).to(torch.int32)\n        for i in range(self.layer_num):\n            hidden_states = self.encoders[i].forward(hidden_states,\n                                                     attention_mask,\n                                                     trt_seq_len,\n                                                     sequence_id_offset)\n        hidden_states = self.rebuild_padding(hidden_states, sequence_id_offset,\n                                             attention_mask, 0)\n        return (hidden_states, )",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {},
      "id": "d9de47b7",
      "cell_type": "markdown",
      "source": "## HuggingFace.Encoder implementation"
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "15da02b0",
      "cell_type": "code",
      "source": "class HuggingFaceEncoder(torch.nn.Module):\n    def __init__(self, layer_num, head_num, head_size, weights=None):\n        super().__init__()\n        hidden_dim = head_num * head_size\n        conf = BertConfig(hidden_size=hidden_dim,\n                          intermediate_size=4 * hidden_dim,\n                          num_attention_heads=head_num,\n                          num_hidden_layers=layer_num)\n        self.encoder = BertEncoder(conf)\n        w = {}\n        for k, v in weights.weights.items():\n            if k.startswith('bert.encoder') and not k.endswith('_amax'):\n                w[k[13:]] = weights.weights[k]\n        self.encoder.load_state_dict(w)\n        self.head_mask = [None] * layer_num\n\n    def forward(self, hidden_states, attention_mask):\n        extended_attention_mask = (1.0 - attention_mask) * -10000.0\n        output = self.encoder(hidden_states, extended_attention_mask,\n                              self.head_mask)\n        return output",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {},
      "id": "8aba6a80",
      "cell_type": "markdown",
      "source": "## Construct inputs and encoders"
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "be3a9a97",
      "cell_type": "code",
      "source": "def sequence_mask(lengths, max_len=None, is_2d=True):\n    batch_size = lengths.numel()\n    max_len = max_len or lengths.max()\n    mask = (torch.arange(0, max_len,\n                         device=lengths.device).type_as(lengths).repeat(\n                             batch_size, 1).lt(lengths.unsqueeze(1)))\n    if is_2d:\n        return mask\n    else:\n        mask = mask.view(-1, 1, 1, max_len)\n        m2 = mask.transpose(2, 3)\n        return mask * m2\n\n\nhead_size = 64\nbatch_size = 256\nlayer_num = 24\nseq_len = 64\nhead_num = 16\nhidden_dim = head_num * head_size\n\ninp = torch.randn(batch_size, seq_len, hidden_dim).cuda().half()\nmem_seq_lens = torch.tensor([seq_len for _ in range(batch_size)]).int().cuda()\nmask = sequence_mask(mem_seq_lens, seq_len, False).cuda().half()\npretrained_weights = None\nweights = EncoderWeights(layer_num, hidden_dim, pretrained_weights)\nhf_encoder = HuggingFaceEncoder(layer_num, head_num, head_size, weights)\nhf_encoder.cuda()\nhf_encoder.half()\nhf_encoder.eval()\nweights.to_half()\nweights.to_cuda()",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "9373abc3",
      "cell_type": "code",
      "source": "custom_encoder = CustomEncoder(\n    layer_num,\n    head_num,\n    head_size,\n    weights,\n    int8_mode=False,\n    allow_gemm_test=True,\n    path=os.path.expanduser(\n        \"~/FasterTransformer/build/lib/libpyt_fastertransformer.so\"))",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {},
      "id": "485a7e3c",
      "cell_type": "markdown",
      "source": "## Check correctness"
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": false
      },
      "id": "45c2ccbb",
      "cell_type": "code",
      "source": "output_mask = sequence_mask(mem_seq_lens, seq_len).to(mask.dtype).unsqueeze(-1)\nhf_output = hf_encoder(inp, mask)[0] * output_mask\n\nft_output = custom_encoder(inp, mask, mem_seq_lens)[0] * output_mask\n\ndiff = torch.abs(hf_output - ft_output)\nprint('FT Mean diff: {}'.format(torch.mean(diff)))\nprint('FT Max diff:  {}'.format(torch.max(diff)))\nprint('FT Min diff:  {}'.format(torch.min(diff)))",
      "execution_count": 7,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "FT Mean diff: 0.0016689300537109375\nFT Max diff:  0.033203125\nFT Min diff:  0.0\n"
        }
      ]
    },
    {
      "metadata": {},
      "id": "49acad96",
      "cell_type": "markdown",
      "source": "## Benchmark"
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "a67fa8ff",
      "cell_type": "code",
      "source": "def benchmark_torch_function(iters, f, *args):\n    f(*args)\n    torch.cuda.synchronize()\n    start_event = torch.cuda.Event(enable_timing=True)\n    end_event = torch.cuda.Event(enable_timing=True)\n    start_event.record()\n    for _ in range(iters):\n        f(*args)\n    end_event.record()\n    torch.cuda.synchronize()\n    return (start_event.elapsed_time(end_event) * 1.0e-3) / iters\n\n\ndef bert_flops(B, T, D, L):\n    mlp = 2 * (B * T * D * 4 * D) + 2 * (B * T * D * 4 * D)\n    qkv = 3 * 2 * B * T * D * D\n    attn = 2 * B * D * T * T + 2 * B * D * T * T + 2 * B * T * D * D\n    return L * (mlp + qkv + attn)\n\n\nflops = bert_flops(1, seq_len, hidden_dim, layer_num)",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": false
      },
      "id": "72626e3c",
      "cell_type": "code",
      "source": "bs = [1, 2, 4, 8, 16, 32, 64, 128]\n\n\ndef ft_benchmark(encoder, b):\n    inp = torch.empty(b, seq_len, hidden_dim).half().cuda()\n    mem_seq_lens = torch.tensor([seq_len for _ in range(b)]).int().cuda()\n    mask = sequence_mask(mem_seq_lens, seq_len, False).half().cuda()\n    t = benchmark_torch_function(10, lambda: encoder(inp, mask, mem_seq_lens))\n    return t / b\n\n\ndef hf_benchmark(encoder, b):\n    inp = torch.empty(b, seq_len, hidden_dim).half().cuda()\n    mem_seq_lens = torch.tensor([seq_len for _ in range(b)]).int().cuda()\n    mask = sequence_mask(mem_seq_lens, seq_len, False).half().cuda()\n    t = benchmark_torch_function(10, lambda: encoder(inp, mask))\n    return t / b\n\n\nft = [ft_benchmark(custom_encoder, b) for b in bs]\nhf = [hf_benchmark(hf_encoder, b) for b in bs]",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {},
      "id": "906955f3",
      "cell_type": "markdown",
      "source": "## Visualization"
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "b87c2417",
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\n%matplotlib inline\n%config InlineBackend.figure_format='retina'\nimport seaborn as sns\nsns.set(rc={\"figure.dpi\":300, 'savefig.dpi':300})\nsns.set_context('notebook')\nsns.set_style(\"ticks\")\nplt.style.use('ggplot')\n\nimport pandas as pd\n\nrecs = [\n    dict(backend=\"HuggingFace\", qps=1/t, t=t, tflops=flops / t / 1.0e12, b=b) for (b, t) in zip(bs, hf)\n] + [\n    dict(backend=\"FasterTransformer\", qps=1/t, t=t, tflops=flops / t / 1.0e12, b=b) for (b, t) in zip(bs, ft)    \n]\n\ndf = pd.DataFrame.from_records(recs)",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "6f73ada7",
      "cell_type": "code",
      "source": "g = sns.lineplot(data=df, x=\"b\", y=\"tflops\", hue=\"backend\")\ng.set_title(\"BERT-Large performance\")\ng.set_xlabel(\"Batch Size\")\n_ = g.set_ylabel(\"TFLOP/s\")",
      "execution_count": 12,
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": "<Figure size 1800x1200 with 1 Axes>"
          },
          "metadata": {
            "image/png": {
              "height": 1185,
              "width": 1647
            }
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false
      },
      "id": "1f3b6904",
      "cell_type": "code",
      "source": "g = sns.lineplot(data=df, x=\"b\", y=\"qps\", hue=\"backend\")\ng.set_title(\"BERT-Large performance\")\ng.set_xlabel(\"Batch Size\")\n_ = g.set_ylabel(\"Sentences/s\")",
      "execution_count": 13,
      "outputs": [
        {
          "data": {
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            "text/plain": "<Figure size 1800x1200 with 1 Axes>"
          },
          "metadata": {
            "image/png": {
              "height": 1185,
              "width": 1676
            }
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "id": "f942ccb6",
      "cell_type": "markdown",
      "source": "Again, getting >160TFLOP/s (>50% of the peak CUBLAS performance on these machines) is pretty good!"
    }
  ],
  "metadata": {
    "gist": {
      "id": "",
      "data": {
        "description": "BERT-Large Performance.ipynb",
        "public": false
      }
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3 (ipykernel)",
      "language": "python"
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    "language_info": {
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      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "toc": {
      "nav_menu": {},
      "number_sections": true,
      "sideBar": true,
      "skip_h1_title": false,
      "base_numbering": 1,
      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": true,
      "toc_position": {},
      "toc_section_display": true,
      "toc_window_display": false
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}