mirror of
https://github.com/gpujs/gpu.js.git
synced 2026-01-18 16:04:10 +00:00
b2df0ab604
Change `HTMLImageArray` render strategy to `gl.NEAREST`, like all the others Let `WebGL2FunctionNode` extend `WebGLFunctionNode` and lighten Remove all the different html pages associated with tests, and just use one file to handle them and use qunit for filtering them Bump version number
307 lines
10 KiB
JavaScript
307 lines
10 KiB
JavaScript
(function() {
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function immutableKernel(gpu) {
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return gpu.createKernel(function(v) {
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return v[this.thread.x] + 1;
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}, {
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output: [1],
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outputImmutable: true,
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outputToTexture: true
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});
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}
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function immutableSubKernels(gpu) {
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return gpu.createKernelMap(
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{
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valueOutput1: function value1(value) {
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return value + 1;
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},
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valueOutput2: function value2(value) {
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return value + 1;
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}
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},
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function(a, b) {
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value1(a[this.thread.x]);
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return value2(b[this.thread.x]);
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},
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{
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output: [1],
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outputImmutable: true,
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outputToTexture: true
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}
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);
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}
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function immutableKernelsMixed(gpu) {
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return gpu.createKernelMap(
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{
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valueOutput1: function value1(value) {
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return value + 1;
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},
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valueOutput2: function value2(value) {
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return value + 1;
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}
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},
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function(a, b) {
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value1(a[this.thread.x]);
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return value2(b[this.thread.x]) + 1;
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},
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{
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output: [1],
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outputImmutable: true,
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outputToTexture: true
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}
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);
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}
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QUnit.test('Issue #267 immutable kernel output - (auto)', () => {
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const gpu = new GPU();
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const kernel = immutableKernel(gpu);
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// start with a value on CPU
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const output1 = kernel([1]);
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const result1 = output1.toArray(gpu)[0];
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// reuse that output, simulating that this value will be monitored, and updated via the same kernel
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// this is often used in neural networks
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const output2 = kernel(output1);
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const result2 = output2.toArray(gpu)[0];
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const output3 = kernel(output2);
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const result3 = output3.toArray(gpu)[0];
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QUnit.assert.equal(result1, 2);
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QUnit.assert.equal(result2, 3);
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QUnit.assert.equal(result3, 4);
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});
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QUnit.test('Issue #267 immutable kernel output - (gpu)', () => {
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const gpu = new GPU({ mode: 'gpu' });
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const kernel = immutableKernel(gpu);
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// start with a value on CPU
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const output1 = kernel([1]);
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const result1 = output1.toArray(gpu)[0];
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// reuse that output, simulating that this value will be monitored, and updated via the same kernel
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// this is often used in neural networks
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const output2 = kernel(output1);
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const result2 = output2.toArray(gpu)[0];
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const output3 = kernel(output2);
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const result3 = output3.toArray(gpu)[0];
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QUnit.assert.equal(result1, 2);
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QUnit.assert.equal(result2, 3);
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QUnit.assert.equal(result3, 4);
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});
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QUnit.test('Issue #267 immutable kernel output - (webgl)', () => {
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const gpu = new GPU({ mode: 'webgl' });
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const kernel = immutableKernel(gpu);
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// start with a value on CPU
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const output1 = kernel([1]);
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const result1 = output1.toArray(gpu)[0];
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// reuse that output, simulating that this value will be monitored, and updated via the same kernel
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// this is often used in neural networks
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const output2 = kernel(output1);
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const result2 = output2.toArray(gpu)[0];
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const output3 = kernel(output2);
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const result3 = output3.toArray(gpu)[0];
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QUnit.assert.equal(result1, 2);
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QUnit.assert.equal(result2, 3);
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QUnit.assert.equal(result3, 4);
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});
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QUnit.test('Issue #267 immutable kernel output - (webgl2)', () => {
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const gpu = new GPU({ mode: 'webgl2' });
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const kernel = immutableKernel(gpu);
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// start with a value on CPU
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const output1 = kernel([1]);
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const result1 = output1.toArray(gpu)[0];
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// reuse that output, simulating that this value will be monitored, and updated via the same kernel
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// this is often used in neural networks
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const output2 = kernel(output1);
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const result2 = output2.toArray(gpu)[0];
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const output3 = kernel(output2);
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const result3 = output3.toArray(gpu)[0];
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QUnit.assert.equal(result1, 2);
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QUnit.assert.equal(result2, 3);
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QUnit.assert.equal(result3, 4);
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});
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QUnit.test('Issue #267 immutable sub-kernel output - (auto)', () => {
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const gpu = new GPU();
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const kernel = immutableSubKernels(gpu);
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// start with a value on CPU
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const output1 = kernel([1], [2]);
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const result1 = output1.valueOutput1.toArray(gpu)[0];
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// reuse that output, simulating that this value will be monitored, and updated via the same kernel
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// this is often used in neural networks
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const output2 = kernel(output1.valueOutput1, output1.valueOutput2);
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const result2 = output2.valueOutput1.toArray(gpu)[0];
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const output3 = kernel(output2.valueOutput1, output2.valueOutput2);
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const result3 = output3.valueOutput1.toArray(gpu)[0];
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QUnit.assert.equal(result1, 2);
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QUnit.assert.equal(result2, 3);
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QUnit.assert.equal(result3, 4);
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});
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QUnit.test('Issue #267 immutable sub-kernel output - (gpu)', () => {
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const gpu = new GPU({ mode: 'gpu' });
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const kernel = immutableSubKernels(gpu);
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// start with a value on CPU
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const output1 = kernel([1], [2]);
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const result1 = output1.valueOutput1.toArray(gpu)[0];
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// reuse that output, simulating that this value will be monitored, and updated via the same kernel
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// this is often used in neural networks
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const output2 = kernel(output1.valueOutput1, output1.valueOutput2);
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const result2 = output2.valueOutput1.toArray(gpu)[0];
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const output3 = kernel(output2.valueOutput1, output2.valueOutput2);
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const result3 = output3.valueOutput1.toArray(gpu)[0];
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QUnit.assert.equal(result1, 2);
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QUnit.assert.equal(result2, 3);
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QUnit.assert.equal(result3, 4);
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});
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QUnit.test('Issue #267 immutable sub-kernel output - (webgl)', () => {
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const gpu = new GPU({ mode: 'webgl' });
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const kernel = immutableSubKernels(gpu);
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// start with a value on CPU
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const output1 = kernel([1], [2]);
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const result1 = output1.valueOutput1.toArray(gpu)[0];
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// reuse that output, simulating that this value will be monitored, and updated via the same kernel
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// this is often used in neural networks
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const output2 = kernel(output1.valueOutput1, output1.valueOutput2);
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const result2 = output2.valueOutput1.toArray(gpu)[0];
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const output3 = kernel(output2.valueOutput1, output2.valueOutput2);
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const result3 = output3.valueOutput1.toArray(gpu)[0];
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QUnit.assert.equal(result1, 2);
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QUnit.assert.equal(result2, 3);
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QUnit.assert.equal(result3, 4);
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});
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QUnit.test('Issue #267 immutable sub-kernel output - (webgl2)', () => {
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const gpu = new GPU({ mode: 'webgl2' });
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const kernel = immutableSubKernels(gpu);
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// start with a value on CPU
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const output1 = kernel([1], [2]);
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const result1 = output1.valueOutput1.toArray(gpu)[0];
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// reuse that output, simulating that this value will be monitored, and updated via the same kernel
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// this is often used in neural networks
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const output2 = kernel(output1.valueOutput1, output1.valueOutput2);
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const result2 = output2.valueOutput1.toArray(gpu)[0];
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const output3 = kernel(output2.valueOutput1, output2.valueOutput2);
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const result3 = output3.valueOutput1.toArray(gpu)[0];
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QUnit.assert.equal(result1, 2);
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QUnit.assert.equal(result2, 3);
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QUnit.assert.equal(result3, 4);
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});
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QUnit.test('Issue #267 immutable kernel & sub-kernel output - (auto)', () => {
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const gpu = new GPU();
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const kernel = immutableKernelsMixed(gpu);
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// start with a value on CPU
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const output1 = kernel([1], [2]);
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const result1 = output1.result.toArray(gpu)[0];
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// reuse that output, simulating that this value will be monitored, and updated via the same kernel
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// this is often used in neural networks
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const output2 = kernel(output1.result, output1.valueOutput2);
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const result2 = output2.result.toArray(gpu)[0];
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const output3 = kernel(output2.result, output2.valueOutput2);
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const result3 = output3.result.toArray(gpu)[0];
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QUnit.assert.equal(result1, 4);
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QUnit.assert.equal(result2, 5);
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QUnit.assert.equal(result3, 6);
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});
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QUnit.test('Issue #267 immutable kernel & sub-kernel output - (gpu)', () => {
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const gpu = new GPU({ mode: 'gpu' });
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const kernel = immutableKernelsMixed(gpu);
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// start with a value on CPU
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const output1 = kernel([1], [2]);
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const result1 = output1.result.toArray(gpu)[0];
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// reuse that output, simulating that this value will be monitored, and updated via the same kernel
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// this is often used in neural networks
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const output2 = kernel(output1.result, output1.valueOutput2);
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const result2 = output2.result.toArray(gpu)[0];
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const output3 = kernel(output2.result, output2.valueOutput2);
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const result3 = output3.result.toArray(gpu)[0];
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QUnit.assert.equal(result1, 4);
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QUnit.assert.equal(result2, 5);
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QUnit.assert.equal(result3, 6);
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});
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QUnit.test('Issue #267 immutable kernel & sub-kernel output - (webgl)', () => {
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const gpu = new GPU({ mode: 'webgl' });
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const kernel = immutableKernelsMixed(gpu);
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// start with a value on CPU
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const output1 = kernel([1], [2]);
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const result1 = output1.result.toArray(gpu)[0];
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// reuse that output, simulating that this value will be monitored, and updated via the same kernel
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// this is often used in neural networks
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const output2 = kernel(output1.result, output1.valueOutput2);
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const result2 = output2.result.toArray(gpu)[0];
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const output3 = kernel(output2.result, output2.valueOutput2);
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const result3 = output3.result.toArray(gpu)[0];
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QUnit.assert.equal(result1, 4);
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QUnit.assert.equal(result2, 5);
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QUnit.assert.equal(result3, 6);
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});
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QUnit.test('Issue #267 immutable kernel & sub-kernel output - (webgl2)', () => {
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const gpu = new GPU({ mode: 'webgl2' });
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const kernel = immutableKernelsMixed(gpu);
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// start with a value on CPU
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const output1 = kernel([1], [2]);
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const result1 = output1.result.toArray(gpu)[0];
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// reuse that output, simulating that this value will be monitored, and updated via the same kernel
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// this is often used in neural networks
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const output2 = kernel(output1.result, output1.valueOutput2);
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const result2 = output2.result.toArray(gpu)[0];
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const output3 = kernel(output2.result, output2.valueOutput2);
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const result3 = output3.result.toArray(gpu)[0];
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QUnit.assert.equal(result1, 4);
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QUnit.assert.equal(result2, 5);
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QUnit.assert.equal(result3, 6);
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});
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})(); |