Demo reuse code for WebNN op tests

webnn/clamp.https.any.js

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+// META: title=test WebNN API clamp operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-clamp
+
+const computeClamp = (builder, resources) => {
+  // MLOperand clamp(MLOperand x, optional MLClampOptions options = {});
+  // use 'x' for input operand name
+  const [inputOperand] = createInputOperands(builder, resources, ['x']);
+  return builder.clamp(inputOperand, resources.options);
+};
+
+testWebNNOperation('clamp', '/webnn/resources/test_data/clamp.json', computeClamp);

webnn/concat.https.any.js

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+// META: title=test WebNN API concat operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-concat
+
+const computeConcat = (builder, resources) => {
+  // MLOperand concat(sequence<MLOperand> inputs, long axis);
+  const inputOperands = [];
+  for (let input of resources.inputs) {
+    inputOperands.push(builder.input(input.name, {type: resources.type, dimensions: input.shape}));
+  }
+  return builder.concat(inputOperands, resources.axis);
+};
+
+testWebNNOperation('concat', '/webnn/resources/test_data/concat.json', computeConcat);

webnn/gemm.https.any.js

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+// META: title=test WebNN API gemm operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-gemm
+
+const computeGemm = (builder, resources) => {
+  // MLOperand gemm(MLOperand a, MLOperand b, optional MLGemmOptions options = {});
+  // use 'a' and 'b' for input operands name
+  const [inputOperandA, inputOperandB] = createInputOperands(builder, resources, ['a', 'b']);
+  const operandType = resources.type;
+  const TestTypedArray = TypedArrayDict[operandType];
+  const options = resources.options;
+  const gemmOptions = {};
+  if (options !== undefined) {
+    if (options.c !== undefined) {
+      if (typeof options.c === 'number') {
+        // scalar
+        gemmOptions.c = new TestTypedArray([options.c])[0];
+      } else {
+        gemmOptions.c = builder.constant({type: operandType, dimensions: options.c.shape}, new TestTypedArray(options.c.data));
+      }
+    }
+    if (options.alpha !== undefined) {
+      // scalar
+      gemmOptions.alpha = new TestTypedArray([options.alpha])[0];
+    }
+    if (options.beta !== undefined) {
+      // scalar
+      gemmOptions.beta = new TestTypedArray([options.beta])[0];
+    }
+    if (options.aTranspose !== undefined) {
+      gemmOptions.aTranspose = options.aTranspose;
+    }
+    if (options.bTranspose !== undefined) {
+      gemmOptions.bTranspose = options.bTranspose;
+    }
+  }
+  return builder.gemm(inputOperandA, inputOperandB, gemmOptions);
+};
+
+testWebNNOperation('gemm', '/webnn/resources/test_data/gemm.json', computeGemm);

webnn/matmul.https.any.js

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+// META: title=test WebNN API matmul operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-matmul
+
+const computeMatmul = (builder, resources) => {
+  // MLOperand matmul(MLOperand a, MLOperand b);
+  // use 'a' and 'b' for input operands name
+  const [inputOperandA, inputOperandB] = createInputOperands(builder, resources, ['a', 'b']);
+  return builder.matmul(inputOperandA, inputOperandB);
+};
+
+testWebNNOperation('matmul', '/webnn/resources/test_data/matmul.json', computeMatmul);

webnn/relu.https.any.js

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+// META: title=test WebNN API relu operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-relu
+
+const computeRelu = (builder, resources) => {
+  // MLOperand relu(MLOperand x);
+  // use 'x' for input operand name
+  const [inputOperand] = createInputOperands(builder, resources, ['x']);
+  return builder.relu(inputOperand);
+};
+
+testWebNNOperation('relu', '/webnn/resources/test_data/relu.json', computeRelu);

webnn/reshape.https.any.js

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+// META: title=test WebNN API reshape operation
+// META: global=window,dedicatedworker
+// META: script=./resources/utils.js
+// META: timeout=long
+
+'use strict';
+
+// https://webmachinelearning.github.io/webnn/#api-mlgraphbuilder-reshape
+
+const computeReshape = (builder, resources) => {
+  // MLOperand reshape(MLOperand input, sequence<long> newShape);
+  // use 'input' for input operand name
+  const [inputOperand] = createInputOperands(builder, resources, ['input']);
+  return builder.reshape(inputOperand, resources.newShape);
+};
+
+testWebNNOperation('reshape', '/webnn/resources/test_data/reshape.json', computeReshape);
+