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code/samples/matmul.js

@@ -1,19 +1,17 @@
-const context = await navigator.ml.createContext();
-
-// The following code multiplies matrix a [3, 4] with matrix b [4, 3]
-// into matrix c [3, 3].
+// Step 0: Create a context and graph builder for 'gpu', 'cpu' or 'npu'.
+const context = await navigator.ml.createContext({deviceType: 'gpu'});
 const builder = new MLGraphBuilder(context);
-const descA = {dataType: 'float32', dimensions: [3, 4]};
-const a = builder.input('a', descA);
-const descB = {dataType: 'float32', dimensions: [4, 3]};
-const bufferB = new Float32Array(sizeOfShape(descB.dimensions)).fill(0.5);
-const b = builder.constant(descB, bufferB);
-const c = builder.gemm(a, b);
-
+// Step 1: Create a computational graph calculating `c = a * b`.
+const a = builder.input('a', {dataType: 'float32', dimensions: [3, 4]});
+const b = builder.input('b', {dataType: 'float32', dimensions: [4, 3]});
+const c = builder.matmul(a, b);
+// Step 2: Compile it into an executable graph.
 const graph = await builder.build({c});
-const bufferA = new Float32Array(sizeOfShape(descA.dimensions)).fill(0.5);
-const bufferC = new Float32Array(sizeOfShape([3, 3]));
-const inputs = {'a': bufferA};
-const outputs = {'c': bufferC};
-const results = await context.compute(graph, inputs, outputs);
+// Step 3: Bind input and output buffers to the graph and execute.
+const bufferA = new Float32Array(3*4).fill(1.0);
+const bufferB = new Float32Array(4*3).fill(0.8);
+const bufferC = new Float32Array(3*3);
+const results = await context.compute(
+    graph, {'a': bufferA, 'b': bufferB}, {'c': bufferC});
+// Step 4: Retrieve the results.
 console.log(`values: ${results.outputs.c}`);

code/samples_repo.js

@@ -1,8 +1,8 @@
 // The samples under ./samples folder
 const samples = [
+  'matmul.js',
   'mul_add.js',
   'simple_graph.js',
-  'matmul.js',
 ];
 
 class SamplesRepository {