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Latency_Prediction_by_nn-Meter

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Jiahang commited on Jul 19, 2022

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aefff9b

1 Parent(s): 0388244

update script

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Files changed (7) hide show

README.md +5 -5
app.py +416 -0
block_latency_demo.json +759 -0
requirements.txt +6 -0
samples/mobilenetv3small_0.json +0 -0
samples/mobilenetv3small_0.onnx +3 -0
samples/mobilenetv3small_0.pb +3 -0

README.md CHANGED Viewed

@@ -1,10 +1,10 @@
 ---
-title: Latency Prediction By Nn-Meter
-emoji: 🏃
-colorFrom: blue
-colorTo: indigo
 sdk: gradio
-sdk_version: 3.0.26
 app_file: app.py
 pinned: false
 license: mit

 ---
+title: Latency Prediction by nn-Meter
+emoji: 🌖
+colorFrom: purple
+colorTo: red
 sdk: gradio
+sdk_version: 3.0.20
 app_file: app.py
 pinned: false
 license: mit

app.py ADDED Viewed

	@@ -0,0 +1,416 @@

+import json
+import gradio as gr
+from nn_meter import load_latency_predictor
+cortexA76cpu_predictor = load_latency_predictor("cortexA76cpu_tflite21")
+adreno640gpu_predictor = load_latency_predictor("adreno640gpu_tflite21")
+adreno630gpu = load_latency_predictor("adreno630gpu_tflite21")
+myriadvpu_predictor = load_latency_predictor("myriadvpu_openvino2019r2")
+predictor_map = {
+    "cortexA76cpu_tflite21": cortexA76cpu_predictor,
+    "adreno640gpu_tflite21": adreno640gpu_predictor,
+    "adreno630gpu_tflite21": adreno630gpu,
+    "myriadvpu_openvino2019r2": myriadvpu_predictor
+}
+feature_for_kernel = {
+    # remove the last two float
+    "conv":                 ["HW", "CIN", "COUT", "KERNEL_SIZE", "STRIDES"],
+    "dwconv":               ["HW", "CIN", "COUT", "KERNEL_SIZE", "STRIDES"],
+    "fc":                   ["CIN", "COUT"],
+    # support up to 4 cin, if less than 4, the latter cin will be set to 0
+    "concat":               ["HW", "CIN1", "CIN2", "CIN3", "CIN4"],
+    #
+    "maxpool":              ["HW", "CIN", "COUT", "KERNEL_SIZE", "POOL_STRIDES"],
+    "avgpool":              ["HW", "CIN", "COUT", "KERNEL_SIZE", "POOL_STRIDES"],
+    "split":                ["HW", "CIN"],
+    "channelshuffle":       ["HW", "CIN"],
+    "se":                   ["HW", "CIN"],
+    "global-avgpool":       ["HW", "CIN"],
+    "bnrelu":               ["HW", "CIN"],
+    "bn":                   ["HW", "CIN"],
+    "hswish":               ["HW", "CIN"],
+    "relu":                 ["HW", "CIN"],
+    "addrelu":              ["HW", "CIN1", "CIN2"],
+    "add":                  ["HW", "CIN1", "CIN2"],
+}
+def get_type(str):
+    operate_type = str.split("-")[0]
+    if operate_type == 'global' or operate_type == 'gap':
+        operate_type = 'global-avgpool'
+    return operate_type
+def get_configuration(operate_type, value_arr):
+    feature_arr = feature_for_kernel[operate_type]
+    if operate_type == 'concat':
+        configuration_arr = []
+        for i in range(len(feature_arr)):
+            if value_arr[i] != 0:
+                configuration_arr.append(feature_arr[i]+"="+str(value_arr[i]))
+            else:
+                break
+    else:
+        configuration_arr = [feature_arr[i]+"="+str(value_arr[i]) for i in range(min(len(feature_arr),len(value_arr)))]
+    return ', '.join(configuration_arr)
+def data_process(data):
+    new_data = []
+    for item in data:
+        operate_type = get_type(item[1])
+        new_item = {
+            "order": item[0],
+            "type": operate_type,
+            "configuration": get_configuration(operate_type, item[2]),
+            "latency": item[3],
+            "name": item[4],
+        }
+        new_data.append(new_item)
+    return new_data
+def generate_html(hardware, latency, block_detail):
+    data = data_process(block_detail)
+    doc = """<html>
+       <head>
+    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+    <meta name="viewport" content="width=device-width,
+                    initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
+    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/css/bootstrap.min.css" rel="stylesheet">
+    <link href="https://unpkg.com/bootstrap-table@1.20.2/dist/bootstrap-table.min.css" rel="stylesheet">
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap-icons@1.8.3/font/bootstrap-icons.css">
+    <style>
+        html {
+            font-family: sans-serif;
+            padding: 5px;
+        }
+        body {
+            padding: 10px;
+            font-size: 0.875rem;
+        }
+        #dataviz {
+            width: 100%;
+            height: 300px;
+            position: relative;
+        }
+        #toolbar {
+            margin-top: 10px;
+            margin-bottom: 15px;
+            display: flex;
+            align-items: center;
+        }
+        input[type="number"]:focus-visible {
+            outline: none;
+        }
+        .bootstrap-table .fixed-table-container .fixed-table-body {
+            height: auto;
+        }
+    </style>
+</head>
+<body>
+    <h4 style="font-size: 1.5rem">Latency Analysis <i class="bi bi-question-circle" data-bs-container="body" data-bs-toggle="popover" data-bs-placement="right" style="font-size:1.2rem;"></i></h4>
+    <div id="popoverInfo" style="display: none">
+        The latency results are empowered by Microsoft nn-Meter. For more technical details, please refer to the paper: <a href="https://dl.acm.org/doi/abs/10.1145/3529706.3529712" target="_blank">nn-METER: Towards Accurate Latency Prediction of DNN Inference on Diverse Edge Devices</a>.
+    </div>
+    <div id="toolbar">
+        <div style="display: flex;align-items: center;">
+            <span>Group By: </span>
+            <select class="form-select" id="inputGroupBy" style="width: fit-content;margin-left: 5px;">
+                <option value="type">Operator Type</option>
+                <option value="name">None</option>
+            </select>
+        </div>
+        <div style="margin-left: 45px;margin-top:6px;display: flex;align-items: center;">
+            <div><label><input type="radio" name="quantity" value="all" class="quantity" checked> Show all</label></div>
+            <div style="margin-left: 10px;">
+                <label><input type="radio" name="quantity" value="top" class="quantity"> Show top</label>
+                <input type="number" value="10" min="1" style="width: 50px;    border: none;
+            border-bottom: 1px #aaa solid;" id="quantityNumber" disabled>
+            </div>
+        </div>
+    </div>
+    <div style="display: flex;">
+        <div id="dataviz"> </div>
+    </div>
+    <table id="table" data-search="true" data-search-align="left" data-pagination="true" data-page-size="30" data-page-list="[10, 20, 30, 50, 100, all]">
+        <thead>
+            <tr>
+                <th data-field="order" data-sortable="true">Excution Order</th>
+                <th data-field="type" data-sortable="true">Operator Type</th>
+                <th data-field="configuration">Configuration</th>
+                <th data-field="latency" data-sortable="true">Latency (ms)</th>
+                <th data-field="name" width="20%" data-sortable="true">Detail Operator</th>
+            </tr>
+        </thead>
+    </table>
+    <script src="https://cdn.jsdelivr.net/npm/echarts@5.3.3/dist/echarts.min.js" type="text/javascript"></script>
+    <script src="https://cdn.jsdelivr.net/npm/jquery/dist/jquery.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.0-beta1/dist/js/bootstrap.bundle.min.js"></script>
+    <script src="https://unpkg.com/bootstrap-table@1.20.2/dist/bootstrap-table.min.js"></script>
+</body>
+        <script>
+        """ + f"""let rawData = {str(data).replace("'", '"')};""" + """
+        rawData.forEach(item => {
+            item.name = item.name.split(";").join("; ");
+            item.latency = Number(item.latency) ? Number(item.latency) : item.latency;
+        })
+        // table
+        let $table = $("#table");
+        $(function () {
+            $table.bootstrapTable({ data: rawData })
+        })
+        // visualization
+        const chartDom = document.getElementById("dataviz");
+        let myChart = echarts.init(chartDom);
+        Array.prototype.groupBy = function (key) {
+            return this.reduce(function (rv, x) {
+                (rv[x[key]] = rv[x[key]] || []).push(x);
+                return rv;
+            }, {});
+        };
+        function processData(rawData, groupBy, quantity) {
+            // transform data
+            let seriesData = Object.entries(rawData.groupBy(groupBy)).map(([name, arr]) => {
+                const value = arr.reduce((sum, curr) => sum + curr.latency, 0);
+                const type = arr[0].type;
+                return { name, value, type }
+            })
+                .sort((a, b) => {
+                    return b.value - a.value
+                });
+            if (quantity) {
+                seriesData = seriesData.slice(0, quantity);
+            }
+            return {
+                seriesData,
+                legendData: seriesData.filter(d => Number(d.value)).map(d => d.name)
+            };
+        }
+        function formatNumber(num, fixed = 2){
+            if(Number(num.toFixed(fixed)) > 0){
+                return num.toFixed(fixed);
+            }else{
+                return num.toPrecision(1);
+            }
+        }
+        function render(data, groupBy) {
+            const sum = data.seriesData.reduce(function (prev, current) {
+                return prev + (Number(current.value) ? Number(current.value) : 0)
+            }, 0);
+            let option = {
+                title: {
+                    text: """ + f"""`Total latency is {format(latency, '.4f')}(ms)`,
+                    subtext: `on Hardware {hardware}`,"""+"""
+                    left: "left",
+                    textStyle:{
+                        fontSize: 14
+                    }
+                },
+                tooltip: {
+                    trigger: "item",
+                    formatter: (params) => groupBy==="name"? `<i>type:</i> ${params.data.type}<br><i>detail:</i> ${params.data.name}<br><b>${formatNumber(params.data.value)}</b><br><b>(${formatNumber(params.data.value / sum * 100)}%)</b>` : `${params.data.name}<br><b>${formatNumber(params.data.value)}</b><br><b>(${formatNumber(params.data.value / sum * 100)}%)</b>`,
+                    extraCssText: "max-width: 400px; white-space: break-spaces;"
+                },
+                legend: {
+                    type: "scroll",
+                    orient: "vertical",
+                    right: "10%",
+                    top: "12%",
+                    bottom: "12%",
+                    data: data.legendData,
+                    formatter: (name) => {
+                        let arr = name.split(";");
+                        return arr.length === 1 ? name : (arr[0]+"...");
+                    },
+                    tooltip: {
+                        show: true,
+                        formatter: (params) => {
+                            let datum = data.seriesData.find(d => d.name === params.name);
+                            return groupBy==="name"? `<i>type:</i> ${datum.type}<br><i>detail:</i> ${datum.name}<br><b>${formatNumber(datum.value)}</b><br><b>(${formatNumber(datum.value / sum * 100)}%)</b>` :`${datum.name}<br><b>${formatNumber(datum.value)}</b><br><b>(${formatNumber(datum.value / sum * 100)}%)</b>`
+                        },
+                        position: (point, params, dom, rect, { contentSize, viewSize }) => [viewSize[0] * 0.4 - contentSize[0] * 0.5, viewSize[1] * 0.5 - contentSize[1] * 0.5]
+                    }
+                },
+                series: [
+                    {
+                        type: "pie",
+                        radius: ["40%", "75%"],
+                        center: ["40%", "50%"],
+                        data: data.seriesData,
+                        emphasis: {
+                            itemStyle: {
+                                shadowBlur: 10,
+                                shadowOffsetX: 0,
+                                shadowColor: "rgba(0, 0, 0, 0.5)"
+                            }
+                        }, label: {
+                            formatter: "{d}%",
+                            position: "inside",
+                            color: "#fff",
+                        },
+                    }
+                ],
+                color: ["#4e79a7", "#f28e2c", "#e15759", "#76b7b2", "#59a14f", "#edc949", "#af7aa1", "#ff9da7", "#9c755f", "#bab0ab"]
+            };
+            myChart.dispose();
+            myChart = echarts.init(chartDom);
+            myChart.setOption(option);
+            myChart.on("selectchanged", function(params){
+                const index = params.fromActionPayload.dataIndexInside;
+                const text = data.seriesData[index].name;
+                $table.bootstrapTable("resetSearch", text);
+            });
+            myChart.on("legendselectchanged", function(params) {
+                suppressSelection(myChart, params);
+            });
+            function suppressSelection(chart, params) {
+                chart.setOption({ animation: false });
+                // Re-select what the user unselected
+                chart.dispatchAction({
+                    type: "legendSelect",
+                    name: params.name
+                });
+                chart.setOption({ animation: true });
+            }
+        }
+        // config
+        let groupBy = "type";
+        let quantityNumber = 10;
+        let showAll = true;
+        render(processData(rawData, groupBy), groupBy);
+        function redraw() {
+            render(processData(rawData, groupBy, showAll ? null : quantityNumber), groupBy);
+        }
+        // change groupby
+        document.getElementById("inputGroupBy")
+            .addEventListener("change", function () {
+                groupBy = this.value;
+                redraw();
+            });
+        // change the model of show
+        function changeShowModel() {
+            if (this.value === "top") {
+                document.getElementById("quantityNumber").disabled = false;
+                showAll = false;
+            } else {
+                document.getElementById("quantityNumber").disabled = true;
+                showAll = true;
+            }
+            redraw();
+        }
+        let items = Object.values(document.getElementsByClassName("quantity"))
+            .forEach(item => item.addEventListener("change", changeShowModel));
+        // change the number of show
+        document.getElementById("quantityNumber")
+            .addEventListener("change", function () {
+                quantityNumber = this.value;
+                redraw();
+            })
+        // enable popover
+        const popoverTriggerList = document.querySelectorAll(`[data-bs-toggle="popover"]`)
+        const popoverList = [...popoverTriggerList].map(popoverTriggerEl => new bootstrap.Popover(popoverTriggerEl, {
+            html : true,
+            content: function() {
+                return $("#popoverInfo").html();
+            }
+        }));
+    </script>
+</html>
+"""
+    return f"""<iframe style="width: 100%; height: 480px" name="result" allow="midi; geolocation; microphone; camera; display-capture; encrypted-media;" sandbox="allow-modals allow-forms allow-scripts allow-same-origin allow-popups allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" allowpaymentrequest="" frameborder="0" srcdoc='{doc}'></iframe>"""
+def generate_error_html(massage):
+    return f"""<div style="color:#842029;background: #f8d7da;padding: 10px;border-radius: 10px; margin-top: 15px;"><b>nn-meter meets an error in latency prediction</b>: {massage}</div>
+        <div style="padding: 10px;">If you have any questions about the result, you can open new issues in <a href="https://github.com/microsoft/nn-Meter" target="_blank" style="color:#2563eb">nn-meter Git repository</a>.</div>
+        """
+def get_latency(model, hardware_name):
+    if model == None:
+        return generate_error_html("Please upload a model file or select one example below.")
+    model = model.name
+    if hardware_name == '':
+        return generate_error_html("Please select a device.")
+    predictor = predictor_map[hardware_name]
+    if model.endswith("onnx"):
+        model_type = "onnx"
+    elif model.endswith("pb"):
+        model_type = "pb"
+    else:
+        model_type = "nnmeter-ir"
+    try:
+        model_latency, block_detail = predictor.detailed_predict(model, model_type)
+        return generate_html(hardware_name, model_latency, block_detail)
+    except Exception as e:
+        return generate_error_html(repr(e))
+title = "Interactive demo: nn-Meter (Draft Version)"
+description = "Demo for Microsoft's nn-Meter, a novel and efficient system to accurately predict the inference latency of DNN models on diverse edge devices. To use it, simply upload a model file, or use one of the example below and click ‘submit’. Results will show up in a few seconds."
+article = "<p style='text-align: center'><a href='https://dl.acm.org/doi/10.1145/3458864.3467882'>nn-Meter: towards accurate latency prediction of deep-learning model inference on diverse edge devices</a> | <a href='https://github.com/microsoft/nn-Meter'>Github Repo</a></p>"
+examples =[
+    ["samples/mobilenetv3small_0.pb", "cortexA76cpu_tflite21"],
+    ["samples/mobilenetv3small_0.onnx", "adreno640gpu_tflite21"],
+    ["samples/mobilenetv3small_0.json", "adreno630gpu_tflite21"]
+]
+inputs = [
+    gr.inputs.File(label="Model File"),
+    gr.inputs.Radio(choices=["cortexA76cpu_tflite21", "adreno640gpu_tflite21", "adreno630gpu_tflite21", "myriadvpu_openvino2019r2"], label="Device"),
+]
+outputs = gr.outputs.HTML()
+iface = gr.Interface(fn=get_latency,
+                     inputs=inputs,
+                     outputs=outputs,
+                     title=title,
+                     description=description,
+                     article=article,
+                     examples=examples,
+                     allow_flagging="auto",
+                     css="""
+                        div[id="6"] {
+                            flex-direction: column;
+                        }
+                        div[id="12"] {
+                            margin-left: 0px !important;
+                            margin-top: 0.75em !important;
+                        }
+                        div[id="12"] iframe{
+                            height: 80vh !important;
+                        }
+                    """)
+iface.launch()

block_latency_demo.json ADDED Viewed

	@@ -0,0 +1,759 @@

+[
+    [
+        "layer1.1.conv-bn#57",
+        [
+            224,
+            3,
+            16,
+            3,
+            2
+        ],
+        0.08969208666666643
+    ],
+    [
+        "layer1.1.hswish#58",
+        [
+            112,
+            16
+        ],
+        0.06512388473684201
+    ],
+    [
+        "layer2.1.conv-bn-relu#59",
+        [
+            112,
+            16,
+            16,
+            1,
+            1
+        ],
+        0.046539635777777595
+    ],
+    [
+        "layer2.2.dwconv-bn-relu#60",
+        [
+            112,
+            16,
+            16,
+            3,
+            1
+        ],
+        0.08458797897222242
+    ],
+    [
+        "layer2.3.conv-bn-add#61",
+        [
+            112,
+            16,
+            16,
+            1,
+            1
+        ],
+        0.046539635777777595
+    ],
+    [
+        "layer3.1.conv-bn-relu#62",
+        [
+            112,
+            16,
+            64,
+            1,
+            1
+        ],
+        0.2812146724444452
+    ],
+    [
+        "layer3.2.dwconv-bn-relu#63",
+        [
+            112,
+            64,
+            64,
+            3,
+            2
+        ],
+        0.1847138329761906
+    ],
+    [
+        "layer3.3.conv-bn#64",
+        [
+            56,
+            64,
+            24,
+            1,
+            1
+        ],
+        0.12564988711111114
+    ],
+    [
+        "layer4.1.conv-bn-relu#65",
+        [
+            56,
+            24,
+            72,
+            1,
+            1
+        ],
+        0.1313259719999998
+    ],
+    [
+        "layer4.2.dwconv-bn-relu#66",
+        [
+            56,
+            72,
+            72,
+            3,
+            1
+        ],
+        0.10148598884424595
+    ],
+    [
+        "layer4.3.conv-bn-add#67",
+        [
+            56,
+            72,
+            24,
+            1,
+            1
+        ],
+        0.12938113733333315
+    ],
+    [
+        "layer5.1.conv-bn-relu#68",
+        [
+            56,
+            24,
+            72,
+            1,
+            1
+        ],
+        0.1313259719999998
+    ],
+    [
+        "layer5.2.dwconv-bn#69",
+        [
+            56,
+            72,
+            72,
+            5,
+            2
+        ],
+        0.13221349583333308
+    ],
+    [
+        "layer5.2.se-relu#70",
+        [
+            28,
+            72
+        ],
+        0.2514369789473686
+    ],
+    [
+        "layer5.3.conv-bn#71",
+        [
+            28,
+            72,
+            40,
+            1,
+            1
+        ],
+        0.06686152977777766
+    ],
+    [
+        "layer6.1.conv-bn-relu#72",
+        [
+            28,
+            40,
+            120,
+            1,
+            1
+        ],
+        0.09544240288888854
+    ],
+    [
+        "layer6.2.dwconv-bn#73",
+        [
+            28,
+            120,
+            120,
+            5,
+            1
+        ],
+        0.1791692834623016
+    ],
+    [
+        "layer6.2.se-relu#74",
+        [
+            28,
+            120
+        ],
+        0.29275722631578976
+    ],
+    [
+        "layer6.3.conv-bn-add#75",
+        [
+            28,
+            120,
+            40,
+            1,
+            1
+        ],
+        0.09891386688888877
+    ],
+    [
+        "layer7.1.conv-bn-relu#76",
+        [
+            28,
+            40,
+            120,
+            1,
+            1
+        ],
+        0.09544240288888854
+    ],
+    [
+        "layer7.2.dwconv-bn#77",
+        [
+            28,
+            120,
+            120,
+            5,
+            1
+        ],
+        0.1791692834623016
+    ],
+    [
+        "layer7.2.se-relu#78",
+        [
+            28,
+            120
+        ],
+        0.29275722631578976
+    ],
+    [
+        "layer7.3.conv-bn-add#79",
+        [
+            28,
+            120,
+            40,
+            1,
+            1
+        ],
+        0.09891386688888877
+    ],
+    [
+        "layer8.1.conv-bn#80",
+        [
+            28,
+            40,
+            240,
+            1,
+            1
+        ],
+        0.17468612444444476
+    ],
+    [
+        "layer8.1.hswish#81",
+        [
+            28,
+            240
+        ],
+        0.034863998421052596
+    ],
+    [
+        "layer8.2.dwconv-bn#82",
+        [
+            28,
+            240,
+            240,
+            3,
+            2
+        ],
+        0.05733039375000008
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requirements.txt ADDED Viewed

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+-e git+https://github.com/microsoft/nn-Meter.git@dev/block-latency#egg=nn-Meter
+tensorflow==2.6.0
+torch==1.9.0
+torchvision==0.10.0
+onnx==1.10.0
+onnx-simplifier==0.3.6

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