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onnxruntime-web
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onnxruntime-web - npm Package Compare versions
Comparing version 1.21.0-dev.20241106-742a0d30be to 1.21.0-dev.20241107-6a295eb75b
@@ -1,1 +0,1 @@ | ||
| 742a0d30beead962a7ce114bdc986eb27eea5a4d | ||
| 6a295eb75bb6e1044cfa6a63df3cb641b9d566a9 |
| /*! | ||
| * ONNX Runtime Web v1.21.0-dev.20241106-742a0d30be | ||
| * ONNX Runtime Web v1.21.0-dev.20241107-6a295eb75b | ||
| * Copyright (c) Microsoft Corporation. All rights reserved. | ||
| * Licensed under the MIT License. | ||
| */ | ||
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| //# sourceMappingURL=ort.node.min.js.map |
| /*! | ||
| * ONNX Runtime Web v1.21.0-dev.20241106-742a0d30be | ||
| * ONNX Runtime Web v1.21.0-dev.20241107-6a295eb75b | ||
| * Copyright (c) Microsoft Corporation. All rights reserved. | ||
| * Licensed under the MIT License. | ||
| */ | ||
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C({location:"ml-tensor",mlTensor:t.mlTensor,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}}});var C,Ee=b(()=>{"use strict";yt();St();vt();Pt();C=class{constructor(e,n,o){Ot();let r,i;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,r=e.type,i=e.dims,e.location){case"cpu-pinned":{let a=H.get(r);if(!a)throw new TypeError(`unsupported type "${r}" to create tensor from pinned buffer`);if(!(e.data instanceof a))throw new TypeError(`buffer should be of type ${a.name}`);this.cpuData=e.data;break}case"texture":{if(r!=="float32")throw new TypeError(`unsupported type "${r}" to create tensor from texture`);this.gpuTextureData=e.texture,this.downloader=e.download,this.disposer=e.dispose;break}case"gpu-buffer":{if(r!=="float32"&&r!=="float16"&&r!=="int32"&&r!=="int64"&&r!=="uint32"&&r!=="uint8"&&r!=="bool"&&r!=="uint4"&&r!=="int4")throw new TypeError(`unsupported type "${r}" to create tensor from gpu 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0?e.format:"RGBA",u=e.tensorFormat!==void 0&&e.tensorFormat!==void 0?e.tensorFormat:"RGB",f=n*o,d=u==="RGBA"?new Float32Array(f*4):new Float32Array(f*3),l=4,c=0,p=1,w=2,m=3,y=0,A=f,h=f*2,g=-1;a==="RGB"&&(l=3,c=0,p=1,w=2,m=-1),u==="RGBA"?g=f*3:u==="RBG"?(y=0,h=f,A=f*2):u==="BGR"&&(h=0,A=f,y=f*2);for(let M=0;M<f;M++,c+=l,w+=l,p+=l,m+=l)d[y++]=(t[c]+s[0])/i[0],d[A++]=(t[p]+s[1])/i[1],d[h++]=(t[w]+s[2])/i[2],g!==-1&&m!==-1&&(d[g++]=(t[m]+s[3])/i[3]);return u==="RGBA"?new C("float32",d,[1,4,n,o]):new C("float32",d,[1,3,n,o])},wt=async(t,e)=>{let n=typeof HTMLImageElement<"u"&&t instanceof HTMLImageElement,o=typeof ImageData<"u"&&t instanceof ImageData,r=typeof ImageBitmap<"u"&&t instanceof ImageBitmap,i=typeof t=="string",s,a=e??{},u=()=>{if(typeof document<"u")return document.createElement("canvas");if(typeof OffscreenCanvas<"u")return new OffscreenCanvas(1,1);throw new Error("Canvas is not supported")},f=d=>typeof HTMLCanvasElement<"u"&&d instanceof HTMLCanvasElement||d instanceof OffscreenCanvas?d.getContext("2d"):null;if(n){let d=u();d.width=t.width,d.height=t.height;let l=f(d);if(l!=null){let c=t.height,p=t.width;if(e!==void 0&&e.resizedHeight!==void 0&&e.resizedWidth!==void 0&&(c=e.resizedHeight,p=e.resizedWidth),e!==void 0){if(a=e,e.tensorFormat!==void 0)throw new Error("Image input config format must be RGBA for HTMLImageElement");a.tensorFormat="RGBA",a.height=c,a.width=p}else a.tensorFormat="RGBA",a.height=c,a.width=p;l.drawImage(t,0,0),s=l.getImageData(0,0,p,c).data}else throw new Error("Can not access image data")}else if(o){let d,l;if(e!==void 0&&e.resizedWidth!==void 0&&e.resizedHeight!==void 0?(d=e.resizedHeight,l=e.resizedWidth):(d=t.height,l=t.width),e!==void 0&&(a=e),a.format="RGBA",a.height=d,a.width=l,e!==void 0){let c=u();c.width=l,c.height=d;let p=f(c);if(p!=null)p.putImageData(t,0,0),s=p.getImageData(0,0,l,d).data;else throw new Error("Can not access image data")}else s=t.data}else if(r){if(e===void 0)throw new Error("Please provide image config with format for Imagebitmap");let d=u();d.width=t.width,d.height=t.height;let l=f(d);if(l!=null){let c=t.height,p=t.width;return l.drawImage(t,0,0,p,c),s=l.getImageData(0,0,p,c).data,a.height=c,a.width=p,qe(s,a)}else throw new Error("Can not access image data")}else{if(i)return new Promise((d,l)=>{let c=u(),p=f(c);if(!t||!p)return l();let w=new Image;w.crossOrigin="Anonymous",w.src=t,w.onload=()=>{c.width=w.width,c.height=w.height,p.drawImage(w,0,0,c.width,c.height);let m=p.getImageData(0,0,c.width,c.height);a.height=c.height,a.width=c.width,d(qe(m.data,a))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(s!==void 0)return qe(s,a);throw new Error("Input data provided is not supported - aborted tensor creation")},bt=(t,e)=>{let{width:n,height:o,download:r,dispose:i}=e,s=[1,o,n,4];return new C({location:"texture",type:"float32",texture:t,dims:s,download:r,dispose:i})},gt=(t,e)=>{let{dataType:n,dims:o,download:r,dispose:i}=e;return new C({location:"gpu-buffer",type:n??"float32",gpuBuffer:t,dims:o,download:r,dispose:i})},Et=(t,e)=>{let{dataType:n,dims:o,download:r,dispose:i}=e;return new C({location:"ml-tensor",type:n??"float32",mlTensor:t,dims:o,download:r,dispose:i})},Tt=(t,e,n)=>new C({location:"cpu-pinned",type:t,data:e,dims:n??[e.length]})});var H,ne,At,Ot,vt=b(()=>{"use strict";H=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array],["int4",Uint8Array],["uint4",Uint8Array]]),ne=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]),At=!1,Ot=()=>{if(!At){At=!0;let t=typeof BigInt64Array<"u"&&BigInt64Array.from,e=typeof BigUint64Array<"u"&&BigUint64Array.from,n=typeof 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C({location:"ml-tensor",mlTensor:t.mlTensor,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}}});var C,Ee=b(()=>{"use strict";yt();St();vt();Pt();C=class{constructor(e,n,o){Ot();let r,i;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,r=e.type,i=e.dims,e.location){case"cpu-pinned":{let a=H.get(r);if(!a)throw new TypeError(`unsupported type "${r}" to create tensor from pinned buffer`);if(!(e.data instanceof a))throw new TypeError(`buffer should be of type ${a.name}`);this.cpuData=e.data;break}case"texture":{if(r!=="float32")throw new TypeError(`unsupported type "${r}" to create tensor from texture`);this.gpuTextureData=e.texture,this.downloader=e.download,this.disposer=e.dispose;break}case"gpu-buffer":{if(r!=="float32"&&r!=="float16"&&r!=="int32"&&r!=="int64"&&r!=="uint32"&&r!=="uint8"&&r!=="bool"&&r!=="uint4"&&r!=="int4")throw new TypeError(`unsupported type "${r}" to create tensor from gpu buffer`);this.gpuBufferData=e.gpuBuffer,this.downloader=e.download,this.disposer=e.dispose;break}case"ml-tensor":{if(r!=="float32"&&r!=="float16"&&r!=="int32"&&r!=="int64"&&r!=="uint32"&&r!=="uint64"&&r!=="int8"&&r!=="uint8"&&r!=="bool"&&r!=="uint4"&&r!=="int4")throw new TypeError(`unsupported type "${r}" to create tensor from MLTensor`);this.mlTensorData=e.mlTensor,this.downloader=e.download,this.disposer=e.dispose;break}default:throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`)}else{let a,u;if(typeof e=="string")if(r=e,u=o,e==="string"){if(!Array.isArray(n))throw new TypeError("A string tensor's data must be a string array.");a=n}else{let f=H.get(e);if(f===void 0)throw new TypeError(`Unsupported tensor type: ${e}.`);if(Array.isArray(n)){if(e==="float16"&&f===Uint16Array||e==="uint4"||e==="int4")throw new TypeError(`Creating a ${e} tensor from number array is not supported. 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Hr={};ye(Hr,{InferenceSession:()=>Bt,TRACE:()=>Je,TRACE_FUNC_BEGIN:()=>F,TRACE_FUNC_END:()=>W,Tensor:()=>I,TrainingSession:()=>Nt,default:()=>jr,env:()=>S,registerBackend:()=>K});V();V();V();var Wt="1.21.0-dev.20241107-6a295eb75b";var jr=Ze;{let t=(wr(),Ve(yr)).wasmBackend;K("cpu",t,10),K("wasm",t,10)}Object.defineProperty(S.versions,"web",{value:Wt,enumerable:!0});return Ve(Hr);})(); | ||
| typeof exports=="object"&&typeof module=="object"&&(module.exports=ort); | ||
| //# sourceMappingURL=ort.wasm.min.js.map |
@@ -7,2 +7,2 @@ // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| export const version = '1.21.0-dev.20241106-742a0d30be'; | ||
| export const version = '1.21.0-dev.20241107-6a295eb75b'; |
@@ -16,2 +16,3 @@ // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| ComputeContext, | ||
| DeviceInfo, | ||
| GpuArchitecture, | ||
@@ -138,2 +139,22 @@ GpuData, | ||
| class DeviceInfoImpl implements DeviceInfo { | ||
| readonly subgroupsSupported: boolean; | ||
| readonly subgroupsF16Supported: boolean; | ||
| readonly subgroupSizeRange?: readonly [number, number]; | ||
| constructor(device: GPUDevice) { | ||
| this.subgroupsSupported = device.features.has('subgroups' as GPUFeatureName); | ||
| this.subgroupsF16Supported = device.features.has('subgroups' as GPUFeatureName); | ||
| // Currently subgroups feature is still experimental and size attributes are not in the WebGPU IDL, so we have to | ||
| // workaround the IDL type checks. | ||
| // TODO: clean this after subgroups feature is settled in IDL. | ||
| const deviceSubgroupsLimits = device.limits as { minSubgroupSize?: number; maxSubgroupSize?: number }; | ||
| if (!this.subgroupsSupported || !deviceSubgroupsLimits.minSubgroupSize || !deviceSubgroupsLimits.maxSubgroupSize) { | ||
| this.subgroupSizeRange = undefined; | ||
| } else { | ||
| this.subgroupSizeRange = [deviceSubgroupsLimits.minSubgroupSize, deviceSubgroupsLimits.maxSubgroupSize]; | ||
| } | ||
| } | ||
| } | ||
| /** | ||
@@ -146,2 +167,3 @@ * this class is designed to store status and being used as a singleton for JSEP. It will be passed to jsepInit() as | ||
| device: GPUDevice; | ||
| deviceInfo: DeviceInfoImpl; | ||
| /** | ||
@@ -249,12 +271,18 @@ * an instance of GpuDataManager to manage a GpuDataId -> GpuBuffer mapping | ||
| if (adapter.features.has('chromium-experimental-timestamp-query-inside-passes')) { | ||
| requiredFeatures.push('chromium-experimental-timestamp-query-inside-passes' as GPUFeatureName); | ||
| } else if (adapter.features.has('timestamp-query')) { | ||
| requiredFeatures.push('timestamp-query'); | ||
| // Try requiring WebGPU features | ||
| const requireFeatureIfAvailable = (feature: GPUFeatureName) => | ||
| adapter.features.has(feature) && requiredFeatures.push(feature) && true; | ||
| // Try chromium-experimental-timestamp-query-inside-passes and fallback to timestamp-query | ||
| if (!requireFeatureIfAvailable('chromium-experimental-timestamp-query-inside-passes' as GPUFeatureName)) { | ||
| requireFeatureIfAvailable('timestamp-query'); | ||
| } | ||
| if (adapter.features.has('shader-f16')) { | ||
| requiredFeatures.push('shader-f16'); | ||
| requireFeatureIfAvailable('shader-f16'); | ||
| // Try subgroups | ||
| if (requireFeatureIfAvailable('subgroups' as GPUFeatureName)) { | ||
| // If subgroups feature is available, also try subgroups-f16 | ||
| requireFeatureIfAvailable('subgroups-f16' as GPUFeatureName); | ||
| } | ||
| this.device = await adapter.requestDevice(deviceDescriptor); | ||
| this.deviceInfo = new DeviceInfoImpl(this.device); | ||
| this.adapterInfo = new AdapterInfoImpl(adapter.info || (await adapter.requestAdapterInfo())); | ||
@@ -261,0 +289,0 @@ this.gpuDataManager = createGpuDataManager(this); |
@@ -14,3 +14,9 @@ // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| import { ShapeUtil } from './util'; | ||
| import { AdapterInfo, ComputeContext, ComputeContextInputsOutputsMapping, ProgramInfo } from './webgpu/types'; | ||
| import { | ||
| AdapterInfo, | ||
| ComputeContext, | ||
| ComputeContextInputsOutputsMapping, | ||
| DeviceInfo, | ||
| ProgramInfo, | ||
| } from './webgpu/types'; | ||
| import { WebNNBackend } from './backend-webnn'; | ||
@@ -74,2 +80,3 @@ | ||
| readonly adapterInfo: AdapterInfo; | ||
| readonly deviceInfo: DeviceInfo; | ||
| readonly opKernelContext: number; | ||
@@ -92,2 +99,3 @@ readonly inputs: readonly TensorView[]; | ||
| this.adapterInfo = backend.adapterInfo; | ||
| this.deviceInfo = backend.deviceInfo; | ||
@@ -118,14 +126,2 @@ // extract context data | ||
| getMaxComputeWorkgroupSizes(): [number, number, number] { | ||
| return [ | ||
| this.backend.device.limits.maxComputeWorkgroupSizeX, | ||
| this.backend.device.limits.maxComputeWorkgroupSizeY, | ||
| this.backend.device.limits.maxComputeWorkgroupSizeZ, | ||
| ]; | ||
| } | ||
| getMaxComputeWorkgroupStoragesize(): number { | ||
| return this.backend.device.limits.maxComputeWorkgroupStorageSize; | ||
| } | ||
| compute(program: ProgramInfo, inputsOutputsMapping?: ComputeContextInputsOutputsMapping): TensorView[] { | ||
@@ -132,0 +128,0 @@ // prepare inputs. inputs should always be valid data. |
@@ -194,4 +194,2 @@ // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| // pending buffers for uploading ( data is unmapped ) | ||
| private buffersForUploadingPending: GPUBuffer[]; | ||
| // pending buffers for computing | ||
@@ -216,3 +214,2 @@ private buffersPending: GPUBuffer[]; | ||
| this.freeUniformBuffers = new Map(); | ||
| this.buffersForUploadingPending = []; | ||
| this.buffersPending = []; | ||
@@ -257,9 +254,8 @@ this.capturedPendingBuffers = new Map(); | ||
| // GPU copy | ||
| const commandEncoder = this.backend.getCommandEncoder(); | ||
| this.backend.endComputePass(); | ||
| const commandEncoder = this.backend.device.createCommandEncoder(); | ||
| commandEncoder.copyBufferToBuffer(gpuBufferForUploading, 0, gpuDataCache.gpuData.buffer, 0, size); | ||
| this.backend.device.queue.submit([commandEncoder.finish()]); | ||
| gpuBufferForUploading.destroy(); | ||
| LOG_DEBUG('verbose', () => `[WebGPU] GpuDataManager.upload(id=${id})`); | ||
| this.buffersForUploadingPending.push(gpuBufferForUploading); | ||
| } | ||
@@ -401,8 +397,2 @@ | ||
| refreshPendingBuffers(): void { | ||
| for (const buffer of this.buffersForUploadingPending) { | ||
| // upload buffer is only useful in the session creation time. So we don't need to reuse them in session running. | ||
| buffer.destroy(); | ||
| } | ||
| this.buffersForUploadingPending = []; | ||
| if (this.buffersPending.length === 0) { | ||
@@ -409,0 +399,0 @@ return; |
@@ -96,9 +96,19 @@ // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| const device = this.backend.device; | ||
| const extensions: string[] = []; | ||
| if (device.features.has('shader-f16')) { | ||
| extensions.push('enable f16;'); | ||
| } | ||
| const enableDirectives: string[] = []; | ||
| // Enable WGSL extensions based on available WebGPU features | ||
| const extensionsInfo: Array<{ feature: GPUFeatureName; extension: string }> = [ | ||
| { feature: 'shader-f16', extension: 'f16' }, | ||
| { feature: 'subgroups' as GPUFeatureName, extension: 'subgroups' }, | ||
| { feature: 'subgroups-f16' as GPUFeatureName, extension: 'subgroups_f16' }, | ||
| ]; | ||
| extensionsInfo.forEach((info) => { | ||
| if (device.features.has(info.feature)) { | ||
| enableDirectives.push(`enable ${info.extension};`); | ||
| } | ||
| }); | ||
| const shaderHelper = createShaderHelper(normalizedDispatchGroupSize, this.backend.device.limits); | ||
| const userCode = programInfo.getShaderSource(shaderHelper); | ||
| const code = `${extensions.join('\n')}\n${shaderHelper.additionalImplementations}\n${userCode}`; | ||
| const code = `${enableDirectives.join('\n')}\n${shaderHelper.additionalImplementations}\n${userCode}`; | ||
| const shaderModule = device.createShaderModule({ code, label: programInfo.name }); | ||
@@ -105,0 +115,0 @@ LOG_DEBUG('verbose', () => `[WebGPU] ${programInfo.name} shader code: ${code}`); |
@@ -24,2 +24,7 @@ // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| } | ||
| export interface DeviceInfo { | ||
| readonly subgroupsSupported: boolean; | ||
| readonly subgroupsF16Supported: boolean; | ||
| readonly subgroupSizeRange?: readonly [number, number]; | ||
| } | ||
@@ -165,2 +170,7 @@ export interface GpuData { | ||
| /** | ||
| * gpu device info | ||
| */ | ||
| readonly deviceInfo: DeviceInfo; | ||
| /** | ||
| * stores the pointer to OpKernelContext | ||
@@ -192,6 +202,4 @@ */ | ||
| output(index: number, dims: readonly number[]): number; | ||
| getMaxComputeWorkgroupSizes(): [number, number, number]; | ||
| getMaxComputeWorkgroupStoragesize(): number; | ||
| } | ||
| export type TimestampQuery = 'none' | 'inside-passes' | 'at-passes'; |
@@ -58,2 +58,29 @@ // Copyright (c) Microsoft Corporation. All rights reserved. | ||
| /** | ||
| * Map from MLOperandDataType to size in bits. Using bits instead of bytes to avoid possible precision loss on int4 and uint4. | ||
| */ | ||
| const webnnDataTypeToSize = new Map<MLOperandDataType, number>([ | ||
| ['float32', 32], | ||
| ['float16', 16], | ||
| ['int32', 32], | ||
| ['uint32', 32], | ||
| ['int64', 64], | ||
| ['uint64', 64], | ||
| ['int8', 8], | ||
| ['uint8', 8], | ||
| ['int4', 4], | ||
| ['uint4', 4], | ||
| ]); | ||
| /** | ||
| * Calculate the byte length of a tensor with the given data type and shape. | ||
| */ | ||
| const calculateByteLength = (dataType: MLOperandDataType, shape: readonly number[]): number => { | ||
| const size = webnnDataTypeToSize.get(dataType); | ||
| if (!size) { | ||
| throw new Error('Unsupported data type.'); | ||
| } | ||
| return Math.ceil((shape.reduce((a, b) => a * b) * size) / 8); | ||
| }; | ||
| /** | ||
| * TensorWrapper wraps an MLTensor and provides a way to track the last session that used it. | ||
@@ -96,2 +123,6 @@ */ | ||
| public get byteLength(): number { | ||
| return calculateByteLength(this.dataType, this.tensorShape); | ||
| } | ||
| public destroy(): void { | ||
@@ -116,3 +147,7 @@ LOG_DEBUG('verbose', () => '[WebNN] TensorWrapper.destroy'); | ||
| public sameTypeAndShape(dataType: MLOperandDataType, shape: readonly number[]): boolean { | ||
| return this.dataType === dataType && this.tensorShape.every((v, i) => v === shape[i]); | ||
| return ( | ||
| this.dataType === dataType && | ||
| this.tensorShape.length === shape.length && | ||
| this.tensorShape.every((v, i) => v === shape[i]) | ||
| ); | ||
| } | ||
@@ -142,2 +177,3 @@ } | ||
| this.tensorManager.releaseTensor(this.tensorWrapper); | ||
| this.wrapper = undefined; | ||
| } | ||
@@ -156,2 +192,5 @@ } | ||
| if (copyOld) { | ||
| if (this.wrapper.byteLength !== calculateByteLength(dataType, shape)) { | ||
| throw new Error('Unable to copy data to tensor with different size.'); | ||
| } | ||
| this.activeUpload = new Uint8Array(await this.wrapper.read()); | ||
@@ -177,4 +216,9 @@ } | ||
| if (this.wrapper) { | ||
| this.wrapper.write(data); | ||
| return; | ||
| if (data.byteLength === this.wrapper.byteLength) { | ||
| this.wrapper.write(data); | ||
| return; | ||
| } else { | ||
| LOG_DEBUG('verbose', () => 'Data size does not match tensor size. Releasing tensor.'); | ||
| this.releaseTensor(); | ||
| } | ||
| } | ||
@@ -321,2 +365,3 @@ | ||
| if (tensor.sameTypeAndShape(dataType, shape)) { | ||
| LOG_DEBUG('verbose', () => `[WebNN] Reusing tensor {dataType: ${dataType}, shape: ${shape}}`); | ||
| const wrapper = this.freeTensors.splice(index, 1)[0]; | ||
@@ -323,0 +368,0 @@ wrapper.sessionId = sessionId; |
@@ -10,3 +10,3 @@ { | ||
| "author": "fs-eire", | ||
| "version": "1.21.0-dev.20241106-742a0d30be", | ||
| "version": "1.21.0-dev.20241107-6a295eb75b", | ||
| "jsdelivr": "dist/ort.min.js", | ||
@@ -13,0 +13,0 @@ "dependencies": { |
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Found 1 instance in 1 package
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Improved metrics
- Total package byte prevSize
- increased by0.06%
96690410
- Lines of code
- increased by0.07%
307578
No dependency changes