NNotepad: fix load(), add zeros(), improve tensor output display (web…

…machinelearning#247)

- The load() helper was broken when the scripts were converted to modules, and also when array parsing was updated. Oops! Fix it.
- Add zeros() helper that creates a zero-initized tensor of the given shape. Useful for testing shape display.
- Ensure tensor output spaces n-dimensions tensors the same as numpy - more spaces between each layer.
- Ensure tensor output handles 0-sized dimensions.

nnotepad/README.md

@@ -49,6 +49,7 @@ The default [data type](https://webmachinelearning.github.io/webnn/#enumdef-mlop
 In addition to WebNN [`MLGraphBuilder`](https://webmachinelearning.github.io/webnn/#mlgraphbuilder) methods, you can use these helpers:
 
 * **load(_url_, _shape_, _dataType_)** - fetch a tensor resource. Must be served with appropriate [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) headers. Example: `load('https://www.random.org/cgi-bin/randbyte?nbytes=256', [16, 16], 'uint8')`
+* **zeros(_shape_, _dataType_)** - constant zero-filled tensor of the given shape. Example: `zeros([2,2,2,2], 'int8')`
 
 
 # Details & Gotchas

nnotepad/js/index.js

@@ -102,7 +102,7 @@ function explain(outputs) {
 
     const width = [...buffer]
         .map((n) => String(n).length)
-        .reduce((a, b) => Math.max(a, b));
+        .reduce((a, b) => Math.max(a, b), 0);
 
     const out = [];
     let bufferIndex = 0;
@@ -118,10 +118,7 @@ function explain(outputs) {
         if (i !== shape[dim] - 1) {
           out.push(', ');
           if (dim + 1 !== shape.length) {
-            if (dim + 2 !== shape.length) {
-              out.push('\n');
-            }
-            out.push('\n');
+            out.push('\n'.repeat(shape.length - dim - 1));
             out.push(' '.repeat(indent + dim + 1));
           }
         }

nnotepad/js/nnotepad.js

@@ -368,7 +368,7 @@ export class NNotepad {
         .join('');
 
     const AsyncFunction = async function() {}.constructor;
-    return [new AsyncFunction(['_'], src), src];
+    return [new AsyncFunction(['_', 'Util'], src), src];
 
     function serializeLine(line, last) {
       const expr = serializeExpr(line.expr);
@@ -476,18 +476,35 @@ export class NNotepad {
         if (url.type !== 'string') {
           throw new TypeError('load(): expected string');
         }
-        if (shape.type !== 'tensor') {
+        if (shape.type !== 'array') {
           throw new TypeError('load(): expected array');
         }
         if (dataType.type !== 'string') {
           throw new TypeError('load(): expected string');
         }
+        const dims = shape.value.map((expr) => expr.value);
         const ctor = WebNNUtil.dataTypeToBufferType(dataType.value);
         return `_.constant({dataType: "${dataType.value}", dimensions: ${
-          Util.stringify(shape.value)}}, new ${
+          Util.stringify(dims)}}, new ${
           ctor.name}(await Util.loadBuffer(${Util.stringify(url.value)})))`;
       }
 
+      if (name === 'zeros') {
+        const [shape, dataType] = args;
+        if (shape.type !== 'array') {
+          throw new TypeError('zeros(): expected array');
+        }
+        if (dataType.type !== 'string') {
+          throw new TypeError('zeros(): expected string');
+        }
+        const dims = shape.value.map((expr) => expr.value);
+        const ctor = WebNNUtil.dataTypeToBufferType(dataType.value);
+        const len = dims.reduce((a, b) => a * b, 1);
+        return `_.constant({dataType: "${dataType.value}", dimensions: ${
+          Util.stringify(dims)}}, new ${
+          ctor.name}(${len}))`;
+      }
+
       return `_.${name}(${
         args.map(
             (arg, index) => serializeExpr(
@@ -509,7 +526,7 @@ export class NNotepad {
     const builder = new self.MLGraphBuilder(context);
 
     const outputOperands = [];
-    let output = await builderFunc(builder);
+    let output = await builderFunc(builder, Util);
     if (output instanceof self.MLOperand) {
       // TODO: remove try/catch once all back-ends support `identity()`.
       try {

nnotepad/res/docs.html

@@ -77,6 +77,7 @@ <h1>Helpers</h1>
 <p>In addition to WebNN <a href="https://webmachinelearning.github.io/webnn/#mlgraphbuilder"><code>MLGraphBuilder</code></a> methods, you can use these helpers:</p>
 <ul>
 <li><strong>load(<em>url</em>, <em>shape</em>, <em>dataType</em>)</strong> - fetch a tensor resource. Must be served with appropriate <a href="https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS">CORS</a> headers. Example: <code>load('https://www.random.org/cgi-bin/randbyte?nbytes=256', [16, 16], 'uint8')</code></li>
+<li><strong>zeros(<em>shape</em>, <em>dataType</em>)</strong> - constant zero-filled tensor of the given shape. Example: <code>zeros([2,2,2,2], 'int8')</code></li>
 </ul>
 <h1>Details &amp; Gotchas</h1>
 <ul>