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this.compileComputePipelineStageDescriptor(\n this.context.shader!,\n );\n\n const buffers = this.context.uniforms.filter(\n (uniform) => uniform.storageClass === STORAGE_CLASS.StorageBuffer,\n );\n const uniforms = this.context.uniforms.filter(\n (uniform) => uniform.storageClass === STORAGE_CLASS.Uniform,\n );\n\n let bufferBindingIndex = uniforms.length ? 1 : 0;\n this.bindGroupEntries = [];\n if (bufferBindingIndex) {\n let offset = 0;\n // FIXME: 所有 uniform 合并成一个 buffer，固定使用 Float32Array 存储，确实会造成一些内存的浪费\n // we use std140 layout @see https://www.khronos.org/opengl/wiki/Interface_Block_(GLSL)\n const mergedUniformData: number[] = [];\n uniforms.forEach((uniform) => {\n if (isNumber(uniform.data)) {\n this.uniformGPUBufferLayout.push({\n name: uniform.name,\n offset,\n });\n offset += 4;\n mergedUniformData.push(uniform.data);\n } else {\n let originDataLength = uniform.data?.length || 1;\n if (originDataLength === 3) {\n // vec3 -> vec4\n // @see http://ptgmedia.pearsoncmg.com/images/9780321552624/downloads/0321552628_AppL.pdf\n originDataLength = 4;\n uniform.data.push(0);\n }\n // 4 elements per block/line\n const padding = (offset / 4) % 4;\n if (padding > 0) {\n const space = 4 - padding;\n if (originDataLength > 1 && originDataLength <= space) {\n if (originDataLength === 2) {\n if (space === 3) {\n offset += 4;\n mergedUniformData.push(0);\n }\n mergedUniformData.push(...uniform.data);\n this.uniformGPUBufferLayout.push({\n name: uniform.name,\n offset,\n });\n }\n } else {\n for (let i = 0; i < space; i++) {\n offset += 4;\n mergedUniformData.push(0);\n }\n mergedUniformData.push(...uniform.data);\n this.uniformGPUBufferLayout.push({\n name: uniform.name,\n offset,\n });\n }\n }\n\n offset += 4 * originDataLength;\n }\n });\n\n this.uniformBuffer = new WebGPUBuffer(this.engine, {\n // TODO: 处理 Struct 和 boolean\n // @ts-ignore\n data:\n mergedUniformData instanceof Array\n ? // @ts-ignore\n new Float32Array(mergedUniformData)\n : mergedUniformData,\n usage:\n WebGPUConstants.BufferUsage.Uniform |\n WebGPUConstants.BufferUsage.CopyDst,\n });\n\n this.bindGroupEntries.push({\n binding: 0,\n resource: {\n buffer: this.uniformBuffer.get(),\n },\n });\n }\n\n // create GPUBuffers for storeage buffers\n buffers.forEach((buffer) => {\n if (!isNil(buffer.data)) {\n if (\n buffer.type === AST_TOKEN_TYPES.Vector4FloatArray ||\n buffer.type === AST_TOKEN_TYPES.FloatArray\n ) {\n let gpuBuffer;\n if (buffer.name === this.context.output.name) {\n gpuBuffer = new WebGPUBuffer(this.engine, {\n // @ts-ignore\n data: isFinite(Number(buffer.data)) ? [buffer.data] : buffer.data,\n usage:\n WebGPUConstants.BufferUsage.Storage |\n WebGPUConstants.BufferUsage.CopyDst |\n WebGPUConstants.BufferUsage.CopySrc,\n });\n this.outputBuffer = gpuBuffer;\n this.context.output = {\n name: buffer.name,\n // @ts-ignore\n length: isFinite(Number(buffer.data)) ? 1 : buffer.data.length,\n typedArrayConstructor: Float32Array,\n gpuBuffer: gpuBuffer.get(),\n };\n } else {\n if (buffer.isReferer) {\n // @ts-ignore\n if (buffer.data.model && buffer.data.model.outputBuffer) {\n // @ts-ignore\n gpuBuffer = (buffer.data.model as WebGPUComputeModel)\n .outputBuffer;\n } else {\n // referred kernel haven't been executed\n }\n } else {\n gpuBuffer = new WebGPUBuffer(this.engine, {\n // @ts-ignore\n data: isFinite(Number(buffer.data))\n ? [buffer.data]\n : buffer.data,\n usage:\n WebGPUConstants.BufferUsage.Storage |\n WebGPUConstants.BufferUsage.CopyDst |\n WebGPUConstants.BufferUsage.CopySrc,\n });\n }\n }\n\n this.vertexBuffers[buffer.name] = gpuBuffer;\n this.bindGroupEntries.push({\n binding: bufferBindingIndex,\n resource: {\n name: buffer.name,\n refer: gpuBuffer ? undefined : buffer.data,\n buffer: gpuBuffer ? gpuBuffer.get() : undefined,\n },\n });\n bufferBindingIndex++;\n }\n }\n });\n\n // create compute pipeline layout\n this.computePipeline = this.engine.device.createComputePipeline({\n computeStage,\n });\n\n console.log(this.bindGroupEntries);\n\n this.bindGroup = this.engine.device.createBindGroup({\n layout: this.computePipeline.getBindGroupLayout(0),\n entries: this.bindGroupEntries,\n });\n }\n\n public destroy(): void {\n if (this.uniformBuffer) {\n this.uniformBuffer.destroy();\n }\n\n Object.keys(this.vertexBuffers).forEach((bufferName) =>\n this.vertexBuffers[bufferName].destroy(),\n );\n }\n\n public async readData() {\n const { output } = this.context;\n if (output) {\n const { length, typedArrayConstructor, gpuBuffer } = output;\n if (gpuBuffer) {\n // await gpuBuffer.mapAsync(WebGPUConstants.MapMode.Read);\n // const arraybuffer = gpuBuffer.getMappedRange();\n // let arraybuffer;\n\n // if (isSafari) {\n // arraybuffer = await gpuBuffer.mapReadAsync();\n // } else {\n const byteCount = length! * typedArrayConstructor!.BYTES_PER_ELEMENT;\n\n // @see https://developers.google.com/web/updates/2019/08/get-started-with-gpu-compute-on-the-web\n const gpuReadBuffer = this.engine.device.createBuffer({\n size: byteCount,\n usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ,\n });\n const encoder = this.engine.device.createCommandEncoder();\n encoder.copyBufferToBuffer(gpuBuffer, 0, gpuReadBuffer, 0, byteCount);\n const queue: GPUQueue = isSafari\n ? // @ts-ignore\n this.engine.device.getQueue()\n : this.engine.device.defaultQueue;\n queue.submit([encoder.finish()]);\n\n await gpuReadBuffer.mapAsync(WebGPUConstants.MapMode.Read);\n const arraybuffer = gpuReadBuffer.getMappedRange();\n const typedArray = new typedArrayConstructor!(arraybuffer.slice(0));\n gpuReadBuffer.unmap();\n\n return typedArray;\n }\n }\n return new Float32Array();\n }\n\n public run() {\n if (this.engine.currentComputePass) {\n this.engine.currentComputePass.setPipeline(this.computePipeline);\n\n // this.bindGroupEntries.forEach((entry) => {\n // if (!entry.resource.buffer) {\n // // get referred kernel's output\n // const gpuBuffer = (entry.resource.refer.model as WebGPUComputeModel)\n // .outputBuffer;\n // this.vertexBuffers[entry.resource.name] = gpuBuffer;\n // entry.resource.buffer = gpuBuffer.get();\n // }\n // });\n\n // const bindGroup = this.engine.device.createBindGroup({\n // layout: this.computePipeline.getBindGroupLayout(0),\n // entries: this.bindGroupEntries,\n // });\n this.engine.currentComputePass.setBindGroup(0, this.bindGroup);\n this.engine.currentComputePass.dispatch(...this.context.dispatch);\n }\n }\n\n public updateBuffer(\n bufferName: string,\n data:\n | number[]\n | Float32Array\n | Uint8Array\n | Uint16Array\n | Uint32Array\n | Int8Array\n | Int16Array\n | Int32Array,\n offset: number = 0,\n ) {\n const buffer = this.vertexBuffers[bufferName];\n if (buffer) {\n buffer.subData({ data, offset });\n }\n }\n\n public updateUniform(\n uniformName: string,\n data:\n | number\n | number[]\n | Float32Array\n | Uint8Array\n | Uint16Array\n | Uint32Array\n | Int8Array\n | Int16Array\n | Int32Array,\n ) {\n const layout = this.uniformGPUBufferLayout.find(\n (l) => l.name === uniformName,\n );\n\n if (layout) {\n this.uniformBuffer.subData({\n data: Number.isFinite(data)\n ? new Float32Array([data as number])\n : new Float32Array(\n data as\n | number[]\n | Float32Array\n | Uint8Array\n | Uint16Array\n | Uint32Array\n | Int8Array\n | Int16Array\n | Int32Array,\n ),\n offset: layout.offset,\n });\n }\n }\n\n public confirmInput(model: IComputeModel, inputName: string): void {\n // copy output GPUBuffer of kernel\n const inputBuffer = this.vertexBuffers[inputName];\n const outputBuffer = (model as WebGPUComputeModel).outputBuffer;\n\n if (inputBuffer && outputBuffer && inputBuffer !== outputBuffer) {\n const encoder = this.engine.device.createCommandEncoder();\n const {\n length,\n typedArrayConstructor,\n } = (model as WebGPUComputeModel).context.output;\n const byteCount = length! * typedArrayConstructor!.BYTES_PER_ELEMENT;\n encoder.copyBufferToBuffer(\n outputBuffer.get(),\n 0,\n inputBuffer.get(),\n 0,\n byteCount,\n );\n const queue: GPUQueue = isSafari\n ? // @ts-ignore\n this.engine.device.getQueue()\n : this.engine.device.defaultQueue;\n queue.submit([encoder.finish()]);\n }\n }\n\n private compileShaderToSpirV(\n source: string,\n type: string,\n shaderVersion: string,\n ): Promise<Uint32Array> {\n return this.compileRawShaderToSpirV(shaderVersion + source, type);\n }\n\n private compileRawShaderToSpirV(\n source: string,\n type: string,\n ): Promise<Uint32Array> {\n return this.engine.glslang.compileGLSL(source, type);\n }\n\n private async compileComputePipelineStageDescriptor(\n computeCode: string,\n ): Promise<Pick<GPUComputePipelineDescriptor, 'computeStage'>> {\n let computeShader: Uint32Array | string = computeCode;\n const shaderVersion = '#version 450\\n';\n if (!this.engine.options.useWGSL) {\n computeShader = await this.compileShaderToSpirV(\n computeCode,\n 'compute',\n shaderVersion,\n );\n }\n\n return {\n computeStage: {\n module: this.engine.device.createShaderModule({\n code: computeShader,\n // @ts-ignore\n isWHLSL: isSafari,\n }),\n entryPoint: 'main',\n },\n };\n }\n}\n"],"file":"WebGPUComputeModel.js"} |