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export const description = `
Returns the atomically loaded the value pointed to by atomic_ptr. It does not modify the object.
`;
import { makeTestGroup } from '../../../../../../../common/framework/test_group.js';
import { keysOf } from '../../../../../../../common/util/data_tables.js';
import { GPUTest } from '../../../../../../gpu_test.js';
import { dispatchSizes, workgroupSizes, typedArrayCtor, kMapId } from './harness.js';
export const g = makeTestGroup(GPUTest);
g.test('load_storage')
.specURL('https://www.w3.org/TR/WGSL/#atomic-load')
.desc(
`
AS is storage or workgroup
T is i32 or u32
fn atomicLoad(atomic_ptr: ptr<AS, atomic<T>, read_write>) -> T
`
)
.params(u =>
u
.combine('workgroupSize', workgroupSizes)
.combine('dispatchSize', dispatchSizes)
.combine('mapId', keysOf(kMapId))
.combine('scalarType', ['u32', 'i32'])
)
.fn(t => {
const numInvocations = t.params.workgroupSize * t.params.dispatchSize;
const bufferNumElements = numInvocations;
const scalarType = t.params.scalarType;
const mapId = kMapId[t.params.mapId];
const wgsl = `
@group(0) @binding(0)
var<storage, read_write> input : array<atomic<${scalarType}>>;
@group(0) @binding(1)
var<storage, read_write> output : array<${scalarType}>;
@compute @workgroup_size(${t.params.workgroupSize})
fn main(
@builtin(global_invocation_id) global_invocation_id : vec3<u32>,
) {
let id = ${scalarType}(global_invocation_id[0]);
output[id] = atomicLoad(&input[id]);
}
`;
const pipeline = t.device.createComputePipeline({
layout: 'auto',
compute: {
module: t.device.createShaderModule({ code: wgsl }),
entryPoint: 'main',
},
});
const arrayType = typedArrayCtor(scalarType);
// Create input buffer with values [map_id(0)..map_id(n)]
const inputBuffer = t.device.createBuffer({
size: bufferNumElements * arrayType.BYTES_PER_ELEMENT,
usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC,
mappedAtCreation: true,
});
t.trackForCleanup(inputBuffer);
const data = new arrayType(inputBuffer.getMappedRange());
data.forEach((_, i) => (data[i] = mapId.f(i, numInvocations)));
inputBuffer.unmap();
const outputBuffer = t.device.createBuffer({
size: bufferNumElements * arrayType.BYTES_PER_ELEMENT,
usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC,
});
t.trackForCleanup(outputBuffer);
const bindGroup = t.device.createBindGroup({
layout: pipeline.getBindGroupLayout(0),
entries: [
{ binding: 0, resource: { buffer: inputBuffer } },
{ binding: 1, resource: { buffer: outputBuffer } },
],
});
// Run the shader.
const encoder = t.device.createCommandEncoder();
const pass = encoder.beginComputePass();
pass.setPipeline(pipeline);
pass.setBindGroup(0, bindGroup);
pass.dispatchWorkgroups(t.params.dispatchSize);
pass.end();
t.queue.submit([encoder.finish()]);
// Both input and output buffer should be the same now
const expected = new (typedArrayCtor(t.params.scalarType))(bufferNumElements);
expected.forEach((_, i) => (expected[i] = mapId.f(i, numInvocations)));
t.expectGPUBufferValuesEqual(inputBuffer, expected);
t.expectGPUBufferValuesEqual(outputBuffer, expected);
});
g.test('load_workgroup')
.specURL('https://www.w3.org/TR/WGSL/#atomic-load')
.desc(
`
AS is storage or workgroup
T is i32 or u32
fn atomicLoad(atomic_ptr: ptr<AS, atomic<T>, read_write>) -> T
`
)
.params(u =>
u
.combine('workgroupSize', workgroupSizes)
.combine('dispatchSize', dispatchSizes)
.combine('mapId', keysOf(kMapId))
.combine('scalarType', ['u32', 'i32'])
)
.fn(t => {
const numInvocations = t.params.workgroupSize;
const wgNumElements = numInvocations;
const scalarType = t.params.scalarType;
const dispatchSize = t.params.dispatchSize;
const mapId = kMapId[t.params.mapId];
const extra = mapId.wgsl(numInvocations, t.params.scalarType); // Defines map_id()
const wgsl =
`
var<workgroup> wg: array<atomic<${scalarType}>, ${wgNumElements}>;
// Result of each workgroup is written to output[workgroup_id.x]
@group(0) @binding(0)
var<storage, read_write> output: array<${scalarType}, ${wgNumElements * dispatchSize}>;
@compute @workgroup_size(${t.params.workgroupSize})
fn main(
@builtin(local_invocation_index) local_invocation_index: u32,
@builtin(workgroup_id) workgroup_id : vec3<u32>
) {
let id = ${scalarType}(local_invocation_index);
let global_id = ${scalarType}(workgroup_id.x * ${wgNumElements} + local_invocation_index);
// Initialize wg[id] with this invocations global id (mapped)
atomicStore(&wg[id], map_id(global_id));
workgroupBarrier();
// Test atomic loading of value at wg[id] and store result in output[global_id]
output[global_id] = atomicLoad(&wg[id]);
}
` + extra;
const pipeline = t.device.createComputePipeline({
layout: 'auto',
compute: {
module: t.device.createShaderModule({ code: wgsl }),
entryPoint: 'main',
},
});
const arrayType = typedArrayCtor(scalarType);
const outputBuffer = t.device.createBuffer({
size: wgNumElements * dispatchSize * arrayType.BYTES_PER_ELEMENT,
usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC,
});
t.trackForCleanup(outputBuffer);
const bindGroup = t.device.createBindGroup({
layout: pipeline.getBindGroupLayout(0),
entries: [{ binding: 0, resource: { buffer: outputBuffer } }],
});
// Run the shader.
const encoder = t.device.createCommandEncoder();
const pass = encoder.beginComputePass();
pass.setPipeline(pipeline);
pass.setBindGroup(0, bindGroup);
pass.dispatchWorkgroups(dispatchSize);
pass.end();
t.queue.submit([encoder.finish()]);
// Expected values should be map_id(0..n)
const expected = new (typedArrayCtor(t.params.scalarType))(
wgNumElements * t.params.dispatchSize
);
expected.forEach((_, i) => (expected[i] = mapId.f(i, numInvocations)));
t.expectGPUBufferValuesEqual(outputBuffer, expected);
});
|
