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en/model_weights.js

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+var oReq = new XMLHttpRequest();
+oReq.open("GET", "weights.bin", true);
+oReq.responseType = "arraybuffer";
+
+var weights_meta={'rnn/~/conv1_d__b': [[0, 73], [73]], 'rnn/~/conv1_d__w': [[73, 26718], [5, 73, 73]], 'rnn/~/embed__embeddings': [[26718, 64094], [512, 73]], 'rnn/~/embed_1__embeddings': [[64094, 69423], [73, 73]], 'rnn/~/linear__b': [[69423, 69454], [31]], 'rnn/~/linear__w': [[69454, 85326], [512, 31]], 'rnn/~/lstm_attention_core/~/gru__b': [[85326, 86862], [1536]], 'rnn/~/lstm_attention_core/~/gru__w_h': [[86862, 873294], [512, 1536]], 'rnn/~/lstm_attention_core/~/gru__w_i': [[873294, 990030], [76, 1536]], 'rnn/~/lstm_attention_core/~/gru_1__b': [[990030, 991566], [1536]], 'rnn/~/lstm_attention_core/~/gru_1__w_h': [[991566, 1777998], [512, 1536]], 'rnn/~/lstm_attention_core/~/gru_1__w_i': [[1777998, 2681166], [588, 1536]], 'rnn/~/lstm_attention_core/~/linear__b': [[2681166, 2681169], [3]], 'rnn/~/lstm_attention_core/~/linear__w': [[2681169, 2682933], [588, 3]]};
+
+var WEIGHTS = {};
+var weight_buffer = null;
+var W = null;
+var w32 = null;
+var w16 = null;
+
+oReq.onload = function (oEvent) {
+  var arrayBuffer = oReq.response; // Note: not oReq.responseText
+  if (arrayBuffer) {
+    // convert bfloat16 to float32
+    // w16 = new Uint16Array(arrayBuffer)
+    // weight_buffer = new SharedArrayBuffer(2*arrayBuffer.byteLength);
+    // w32 = new Uint16Array(weight_buffer);
+    // for(var i=0; i < w16.length; i++) {
+    //     w32[i * 2 + 1] = w16[i];
+    // }
+    W = new Float32Array(arrayBuffer);
+    document.getElementById("btn").innerText = "Buffer arrieved";
+
+    for(var k in weights_meta) {
+        info = weights_meta[k];
+        offset = info[0];
+        shape = info[1];
+        WEIGHTS[k] = tf.tensor(W.subarray(offset[0], offset[1]), shape);
+    }
+
+    document.getElementById("btn").disabled = false;
+    tf.engine().startScope();    
+    setTimeout(function() {
+        cur_run = cur_run + 1;
+        dojob(cur_run);
+    }, 0);
+
+    document.getElementById("btn").innerText = "Generate";
+  }
+};
+
+tf.setBackend('wasm');
+tf.ready().then( function() {
+        tf.enableProdMode();
+        oReq.send(null);
+});

en/script.js

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+var log = console.log;
+var ctx = null;
+var canvas = null;
+var RNN_SIZE = 512;
+var cur_run = 0;
+
+var randn = function() {
+    // Standard Normal random variable using Box-Muller transform.
+    var u = Math.random() * 0.999 + 1e-5;
+    var v = Math.random() * 0.999 + 1e-5;
+    return Math.sqrt(-2.0 * Math.log(u)) * Math.cos(2.0 * Math.PI * v);
+}
+
+var rand_truncated_normal = function(low, high) {
+    while (true) {
+        r = randn();
+        if (r >= low && r <= high)
+            break;
+        // rejection sampling.
+    }
+    return r;
+}
+
+var softplus = function(x) {
+    const m = tf.maximum(x, 0.0);
+    return tf.add(m, tf.log(tf.add(tf.exp(tf.neg(m)), tf.exp(tf.sub(x, m)))));
+}
+
+
+var char2idx = {'\x00': 0, ' ': 1, '!': 2, '"': 3, '#': 4, "'": 5, '(': 6, ')': 7, ',': 8, '-': 9, '.': 10, '0': 11, '1': 12, '2': 13, '3': 14, '4': 15, '5': 16, '6': 17, '7': 18, '8': 19, '9': 20, ':': 21, ';': 22, '?': 23, 'A': 24, 'B': 25, 'C': 26, 'D': 27, 'E': 28, 'F': 29, 'G': 30, 'H': 31, 'I': 32, 'J': 33, 'K': 34, 'L': 35, 'M': 36, 'N': 37, 'O': 38, 'P': 39, 'R': 40, 'S': 41, 'T': 42, 'U': 43, 'V': 44, 'W': 45, 'Y': 46, 'a': 47, 'b': 48, 'c': 49, 'd': 50, 'e': 51, 'f': 52, 'g': 53, 'h': 54, 'i': 55, 'j': 56, 'k': 57, 'l': 58, 'm': 59, 'n': 60, 'o': 61, 'p': 62, 'q': 63, 'r': 64, 's': 65, 't': 66, 'u': 67, 'v': 68, 'w': 69, 'x': 70, 'y': 71, 'z': 72};
+
+var gru_core = function(input, weights, state, hidden_size) {
+    var [w_h,w_i,b] = weights;
+    var [w_h_z,w_h_a] = tf.split(w_h, [2 * hidden_size, hidden_size], 1);
+    var [b_z,b_a] = tf.split(b, [2 * hidden_size, hidden_size], 0);
+    gates_x = tf.matMul(input, w_i);
+    [zr_x,a_x] = tf.split(gates_x, [2 * hidden_size, hidden_size], 1);
+    zr_h = tf.matMul(state, w_h_z);
+    zr = tf.add(tf.add(zr_x, zr_h), b_z);
+    // fix this
+    [z,r] = tf.split(tf.sigmoid(zr), 2, 1);
+    a_h = tf.matMul(tf.mul(r, state), w_h_a);
+    a = tf.tanh(tf.add(tf.add(a_x, a_h), b_a));
+    next_state = tf.add(tf.mul(tf.sub(1., z), state), tf.mul(z, a));
+    return [next_state, next_state];
+};
+
+
+var generate = function() {
+    cur_run = cur_run + 1;
+    setTimeout(function() {   
+        var counter = 2000;
+        tf.disposeVariables();
+        
+        tf.engine().startScope();
+        ctx.clearRect(0, 0, canvas.width, canvas.height);
+        ctx.beginPath();
+        dojob(cur_run);
+    }, 200);
+
+    return false;
+}
+
+var dojob = function(run_id) {
+    var text = document.getElementById("user-input").value;
+    if (text.length == 0) {
+        text = "The quick brown fox jumps over the lazy dog";
+    }
+
+    var cur_x = 50.;
+    var cur_y = 300.;
+
+
+    log(text);
+    original_text = text;
+    text = '' + text + '          ' + text;
+
+    text = Array.from(text).map(function(e) {
+        return char2idx[e]
+    })
+    var text_embed = WEIGHTS['rnn/~/embed_1__embeddings'];
+    indices = tf.tensor1d(text, 'int32');
+    text = text_embed.gather(indices);
+
+    filter = WEIGHTS['rnn/~/conv1_d__w'];
+    embed = tf.conv1d(text, filter, 1, 'same');
+    bias = tf.expandDims(WEIGHTS['rnn/~/conv1_d__b'], 0);
+    embed = tf.add(embed, bias);
+
+    var writer_embed = WEIGHTS['rnn/~/embed__embeddings'];
+    var e = document.getElementById("writers");
+    var wid = parseInt(e.value);
+    // log(wid);
+
+    wid = tf.tensor1d([wid], 'int32');
+    wid = writer_embed.gather(wid);
+    embed = tf.add(wid, embed);
+
+    // initial state
+    var gru0_hx = tf.zeros([1, RNN_SIZE]);
+    var gru1_hx = tf.zeros([1, RNN_SIZE]);
+    // var gru2_hx = tf.zeros([1, RNN_SIZE]);
+
+    var att_location = tf.zeros([1, 1]);
+    var att_context = tf.zeros([1, 73]);
+
+    var input = tf.tensor([[0., 0., 1.]]);
+
+    gru0_w_h = WEIGHTS['rnn/~/lstm_attention_core/~/gru__w_h'];
+    gru0_w_i = WEIGHTS['rnn/~/lstm_attention_core/~/gru__w_i'];
+    gru0_bias = WEIGHTS['rnn/~/lstm_attention_core/~/gru__b'];
+
+    gru1_w_h = WEIGHTS['rnn/~/lstm_attention_core/~/gru_1__w_h'];
+    gru1_w_i = WEIGHTS['rnn/~/lstm_attention_core/~/gru_1__w_i'];
+    gru1_bias = WEIGHTS['rnn/~/lstm_attention_core/~/gru_1__b'];
+    att_w = WEIGHTS['rnn/~/lstm_attention_core/~/linear__w'];
+    att_b = WEIGHTS['rnn/~/lstm_attention_core/~/linear__b'];
+    gmm_w = WEIGHTS['rnn/~/linear__w'];
+    gmm_b = WEIGHTS['rnn/~/linear__b'];
+
+    ruler = tf.tensor([...Array(text.shape[0]).keys()]);
+    var bias = parseInt(document.getElementById("bias").value) / 100 * 3;
+
+    cur_x = 50.;
+    cur_y = 400.;
+    var path = [];
+    var dx = 0.;
+    var dy = 0;
+    var eos = 1.;
+    var counter = 0;
+
+
+    function loop(my_run_id) {
+        if (my_run_id < cur_run) {
+            tf.disposeVariables();
+            tf.engine().endScope();
+            return;
+        }
+
+        counter++;
+        if (counter < 2000) {
+            [att_location,att_context,gru0_hx,gru1_hx,input] = tf.tidy(function() {
+                // Attention
+                const inp_0 = tf.concat([att_context, input], 1);
+                gru0_hx_ = gru0_hx;
+                [out_0,gru0_hx] = gru_core(inp_0, [gru0_w_h, gru0_w_i, gru0_bias], gru0_hx, RNN_SIZE);
+                tf.dispose(gru0_hx_);
+                const att_inp = tf.concat([att_context, input, out_0], 1);
+                const att_params = tf.add(tf.matMul(att_inp, att_w), att_b);
+                [alpha,beta,kappa] = tf.split(softplus(att_params), 3, 1);
+                att_location_ = att_location;
+                att_location = tf.add(att_location, tf.div(kappa, 25.));
+                tf.dispose(att_location_)
+
+                const phi = tf.mul(alpha, tf.exp(tf.div(tf.neg(tf.square(tf.sub(att_location, ruler))), beta)));
+                att_context_ = att_context;
+                att_context = tf.sum(tf.mul(tf.expandDims(phi, 2), tf.expandDims(embed, 0)), 1)
+                tf.dispose(att_context_);
+
+                const inp_1 = tf.concat([input, out_0, att_context], 1);
+                tf.dispose(input);
+                gru1_hx_ = gru1_hx;
+                [out_1,gru1_hx] = gru_core(inp_1, [gru1_w_h, gru1_w_i, gru1_bias], gru1_hx, RNN_SIZE);
+                tf.dispose(gru1_hx_);
+
+                // GMM
+                const gmm_params = tf.add(tf.matMul(out_1, gmm_w), gmm_b);
+                [x,y,logstdx,logstdy,angle,log_weight,eos_logit] = tf.split(gmm_params, [5, 5, 5, 5, 5, 5, 1], 1);
+                // log_weight = tf.softmax(log_weight, 1);
+                // log_weight = tf.log(log_weight);
+                // log_weight = tf.mul(log_weight, 1. + bias);
+                // const idx = tf.multinomial(log_weight, 1).dataSync()[0];
+                // log_weight = tf.softmax(log_weight, 1);
+                // log_weight = tf.log(log_weight);
+                // log_weight = tf.mul(log_weight, 1. + bias);
+                const idx = tf.argMax(log_weight, 1).dataSync()[0];
+
+                x = x.dataSync()[idx];
+                y = y.dataSync()[idx];
+                const stdx = tf.exp(tf.sub(logstdx, bias)).dataSync()[idx];
+                const stdy = tf.exp(tf.sub(logstdy, bias)).dataSync()[idx];
+                angle = angle.dataSync()[idx];
+                e = tf.sigmoid(tf.mul(eos_logit, (1. + 0.*bias))).dataSync()[0];
+                const rx = rand_truncated_normal(-5, 5) * stdx;
+                const ry = rand_truncated_normal(-5, 5) * stdy;
+                x = x + Math.cos(-angle) * rx - Math.sin(-angle) * ry;
+                y = y + Math.sin(-angle) * rx + Math.cos(-angle) * ry;
+                if (Math.random() < e) {
+                    e = 1.;
+                } else {
+                    e = 0.;
+                }
+                input = tf.tensor([[x, y, e]]);
+                return [att_location, att_context, gru0_hx, gru1_hx, input];
+            });
+
+            [dx,dy,eos_] = input.dataSync();
+            dy = -dy * 3;
+            dx = dx * 3;
+            if (eos == 0.) {
+                ctx.beginPath();
+                ctx.moveTo(cur_x, cur_y, 0, 0);
+                ctx.lineTo(cur_x + dx, cur_y + dy);
+                ctx.stroke();
+            }
+            eos = eos_;
+            cur_x = cur_x + dx;
+            cur_y = cur_y + dy;
+
+            if (att_location.dataSync()[0] < original_text.length + 2) {
+                setTimeout(function() {loop(my_run_id);}, 0);
+            }
+        }
+    }
+
+    loop(run_id);
+}
+
+window.onload = function(e) {
+    //Setting up canvas
+    canvas = document.getElementById("hw-canvas");
+    ctx = canvas.getContext("2d");
+    ctx.canvas.width = window.innerWidth- 50;
+    ctx.canvas.height = window.innerHeight - 50;
+
+}