mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-25 19:48:11 +00:00
### Description This PR is a preview of cherry-picks for ort-web to `rel-1.17.3` based on `rel-1.17.2`. <details> <summary>Changes of ort-web to cherry-pick</summary> The following commits are from main branch. `o` stands for pick, and `x` stands for skip. ``` o2e0a388c36[js/webgpu] Add HardSigmoid support (#19215) od226e40856[js/webgpu] set query type in onRunStart (#19202) o61610ff986[js/webgpu] Add FusedConv clip test case (#18900) oa33b5bd1fa[JS/WebGPU] Added Uniforms to SkipLayerNorm. (#18788) o591f90c0b9[js/webgpu] Fix issue of timestamp query (#19258) o7252c6e747[WebNN EP] Support WebNN async API with Asyncify (#19145) o5b06505073[js/webgpu] Fix Tanh explosion (#19201) o656ca66186[js/webgpu] Support uniforms for conv, conv transpose, conv grouped (#18753) oa3f0e2422b[js/webgpu] Support f16 uniform (#19098) o9e69606360fix f16 for attention, enable slice and flatten for more types (#19262) o624b4e2063[js/webgpu] Remove enableShapesUniforms (#19279) o90883a366a[js/webgpu] Add hardSigmoid activation for fusedConv (#19233) o85cef0af8c[js/webgpu] Support capture and replay for jsep (#18989) od73131cf0f[js/webgpu] Use DataType as uniform cpu type (#19281) odd1f6ccc45[js/webgpu] resolve codescan alert (#19343) o3a2ab1963a[js/webgpu] Refactor createTensorShapeVariables (#18883) oefc17e79de[js/webgpu] Fix the undefined push error (#19366) x50806a7dd5[js/web] support external data in npm test (#19377) occbe264a39[js/webgpu] Add LeakyRelu activation for fusedConv (#19369) o5ff27ef02a[js/webgpu] support customop FastGelu (#19392) x03be65e064[js/web] fix types exports in package.json (#19458) o06269a3952[js/webgpu] allow uint8 tensors for webgpu (#19545) odfeda9019c[JS/WebGPU] Add MatMulNBits (#19446) o1b48054e1b[js/webgpu] Create Split indices helpers by rank, not by shape (#19554) o3fe2c137ee[js] small fix to workaround formatter (#19400) x70567a4b3a[js/web] use ApiTensor insteadof onnxjs Tensor in TensorResultValidator (#19358) o6e04e36e3f[js/common] upgrade tsc in common from 4.9.5 to 5.2.2 (#19317) o58f4921686[js] changes to allow Float16Array if any polyfill is available (#19305) o57d6819212[js/web] Fix fused-conv is not included in npm test (#19581) oebd220b073Misspelling in README.md (#19433) o38c3432393Bump ip from 1.1.8 to 1.1.9 in /js/react_native (#19582) ofe82fccf1a[js/webgpu] Fix Conv2DTransposeMatMul f16 compilation failure (#19596) o76a2a487a1Bump ip from 1.1.8 to 1.1.9 in /js/react_native/e2e (#19583) o29b1106033[node] Switch to setImmediate to avoid starving the Node.js event loop (#19610) oae3d73c981[JS/WebGPU] Fix Split and Where to handle corner cases. (#19613) oaec2389ad0[js/webgpu] allows a ProgramInfo's RunData to use zero sized output (#19614) obb43a0f133[js/webgpu] minor fixes to make tinyllama work (#19564) o0edb035808[js/web] fix suite test list for zero sized tensor (#19638) o3cb81cdde2[js/common] move 'env.wasm.trace' to 'env.trace' (#19617) oe30618d055[js/webgpu] use Headless for webgpu test by default (#19702) of06164ef8b[js/web] transfer input buffer back to caller thread (#19677) xa788514027[js/web] dump debug logs for karma for diagnose purpose (#19785) o24b72d2613[JS/WebGPU] Preserve zero size input tensor dims. (#19737) o4538d31a8b[js/webgpu] expose a few properties in WebGPU API (#19857) o53de2d8cb0[js/webgpu] Enable GroupedConvVectorize path (#19791) oed250b88c3[JS/WebGPU] Optimize MatMulNBits (#19852) xe771a763c3[js/test] align web test runner flags with ort.env (#19790) o79e50aeef3[js/web] rewrite backend resolve to allow multiple EPs (#19735) oacb0df2280Fix #19931 broken Get Started link of "ONNX Runtime JavaScript API" page (#19932) ob29849a287[js/common] fix typedoc warnings (#19933) oafdab62f53Bump follow-redirects from 1.15.4 to 1.15.6 in /js/web (#19949) o28ad6c3955Bump follow-redirects from 1.15.4 to 1.15.6 in /js/node (#19951) o7e0d424934accumulate in fp32 for Reduce* (#19868) o4c6a6a37f7[js/webgpu] Fix NAN caused by un-initialized buffer in instance-norm (#19387) o01c7aaf6aa[js/webgpu] allow setting env.webgpu.adapter (#19940) oc45cff60cf[js/webgpu] fix maxpool / fp16 (#19981) ``` </details> <details> <summary>Cherry-pick commandlines</summary> ```sh git cherry-pick2e0a388c36git cherry-pickd226e40856git cherry-pick61610ff986git cherry-picka33b5bd1fagit cherry-pick591f90c0b9git cherry-pick7252c6e747git cherry-pick5b06505073git cherry-pick656ca66186git cherry-picka3f0e2422bgit cherry-pick9e69606360git cherry-pick624b4e2063git cherry-pick90883a366agit cherry-pick85cef0af8c#<<<<< Note: conflicts git cherry-pickd73131cf0fgit cherry-pickdd1f6ccc45git cherry-pick3a2ab1963agit cherry-pickefc17e79degit cherry-pickccbe264a39git cherry-pick5ff27ef02agit cherry-pick06269a3952git cherry-pickdfeda9019cgit cherry-pick1b48054e1bgit cherry-pick3fe2c137eegit cherry-pick6e04e36e3fgit cherry-pick58f4921686git cherry-pick57d6819212git cherry-pickebd220b073git cherry-pick38c3432393git cherry-pickfe82fccf1agit cherry-pick76a2a487a1git cherry-pick29b1106033git cherry-pickae3d73c981git cherry-pickaec2389ad0git cherry-pickbb43a0f133git cherry-pick0edb035808git cherry-pick3cb81cdde2git cherry-picke30618d055git cherry-pickf06164ef8bgit cherry-pick24b72d2613git cherry-pick4538d31a8bgit cherry-pick53de2d8cb0git cherry-picked250b88c3git cherry-pick79e50aeef3git cherry-pickacb0df2280git cherry-pickb29849a287git cherry-pickafdab62f53git cherry-pick28ad6c3955git cherry-pick7e0d424934git cherry-pick4c6a6a37f7git cherry-pick01c7aaf6aagit cherry-pickc45cff60cf``` </details> <details> <summary>Cherry-pick conflicts</summary> -85cef0af8c#18989 this change is for enabling graph capture feature for JSEP, and it is done after ROCM EP enabled graph capture feature. However, the ROCM EP graph capture feature is not cherry-picked in rel-1.17.2. </details> --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: Jiajia Qin <jiajia.qin@intel.com> Co-authored-by: Xu Xing <xing.xu@intel.com> Co-authored-by: satyajandhyala <satya.k.jandhyala@gmail.com> Co-authored-by: Yang Gu <yang.gu@intel.com> Co-authored-by: Wanming Lin <wanming.lin@intel.com> Co-authored-by: Jiajie Hu <jiajie.hu@intel.com> Co-authored-by: Guenther Schmuelling <guschmue@microsoft.com> Co-authored-by: Matttttt <18152455+martholomew@users.noreply.github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Segev Finer <segev208@gmail.com> Co-authored-by: Belem Zhang <belem.zhang@intel.com>
404 lines
14 KiB
TypeScript
404 lines
14 KiB
TypeScript
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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import {WebGpuBackend} from '../backend-webgpu';
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import {LOG_DEBUG} from '../log';
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import {GpuData, GpuDataId, GpuDataType} from './types';
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/**
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* manages GpuDataId -> GpuBuffer
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*/
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export interface GpuDataManager {
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/**
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* copy data from CPU to GPU.
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*/
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upload(id: GpuDataId, data: Uint8Array): void;
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/**
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* copy data from GPU to GPU.
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*/
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memcpy(sourceId: GpuDataId, destinationId: GpuDataId): void;
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/**
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* create new data on GPU.
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*/
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create(size: number, usage?: number): GpuData;
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/**
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* get GPU data by ID.
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*/
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get(id: GpuDataId): GpuData|undefined;
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/**
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* release the data on GPU by ID.
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*
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* @return size of the data released
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*/
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release(id: GpuDataId): number;
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/**
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* copy data from GPU to CPU.
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*/
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download(id: GpuDataId, getTargetBuffer: () => Uint8Array): Promise<void>;
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/**
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* refresh the buffers that marked for release.
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*
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* when release() is called, the buffer is not released immediately. this is because we need to wait for the commands
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* to be submitted to the GPU. this function is called after the commands are submitted so that the buffers can be
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* actually released.
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*/
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refreshPendingBuffers(): void;
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/**
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* register an external buffer for IO Binding. If the buffer is already registered, return the existing GPU data ID.
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*
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* GPU data manager only manages a mapping between the buffer and the GPU data ID. It will not manage the lifecycle of
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* the external buffer.
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*/
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registerExternalBuffer(buffer: GPUBuffer, originalSize: number, previousBuffer?: GPUBuffer): number;
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/**
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* unregister an external buffer for IO Binding.
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*/
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unregisterExternalBuffer(buffer: GPUBuffer): void;
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/**
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* destroy all gpu buffers.
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*/
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dispose(): void;
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/**
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* release session related data.
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* @param sessionId - specify the session ID.
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*/
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onReleaseSession(sessionId: number): void;
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}
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interface StorageCacheValue {
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gpuData: GpuData;
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originalSize: number;
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}
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/**
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* normalize the buffer size so that it fits the 128-bits (16 bytes) alignment.
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*/
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const calcNormalizedBufferSize = (size: number) => Math.ceil(size / 16) * 16;
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let guid = 1;
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const createNewGpuDataId = () => guid++;
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/**
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* exported standard download function. This function is used by the session to download the data from GPU, and also by
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* factory to create GPU tensors with the capacity of downloading data from GPU.
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*
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* @param backend - the WebGPU backend
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* @param gpuBuffer - the GPU buffer to download
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* @param originalSize - the original size of the data
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* @param getTargetBuffer - optional. If provided, the data will be copied to the target buffer. Otherwise, a new buffer
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* will be created and returned.
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*/
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export const downloadGpuData =
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async(backend: WebGpuBackend, gpuBuffer: GPUBuffer, originalSize: number, getTargetBuffer?: () => Uint8Array):
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Promise<Uint8Array> => {
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const bufferSize = calcNormalizedBufferSize(originalSize);
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const gpuReadBuffer = backend.device.createBuffer(
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// eslint-disable-next-line no-bitwise
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{size: bufferSize, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ});
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try {
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const commandEncoder = backend.getCommandEncoder();
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backend.endComputePass();
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commandEncoder.copyBufferToBuffer(
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gpuBuffer /* source buffer */, 0 /* source offset */, gpuReadBuffer /* destination buffer */,
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0 /* destination offset */, bufferSize /* size */
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);
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backend.flush();
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await gpuReadBuffer.mapAsync(GPUMapMode.READ);
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const arrayBuffer = gpuReadBuffer.getMappedRange();
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if (getTargetBuffer) {
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// if we already have a CPU buffer to accept the data, no need to clone the ArrayBuffer.
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const targetBuffer = getTargetBuffer();
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targetBuffer.set(new Uint8Array(arrayBuffer, 0, originalSize));
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return targetBuffer;
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} else {
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// the mapped ArrayBuffer will be released when the GPU buffer is destroyed. Need to clone the
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// ArrayBuffer.
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return new Uint8Array(arrayBuffer.slice(0, originalSize));
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}
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} finally {
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gpuReadBuffer.destroy();
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}
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};
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class GpuDataManagerImpl implements GpuDataManager {
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// GPU Data ID => GPU Data ( storage buffer )
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private storageCache: Map<GpuDataId, StorageCacheValue>;
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// pending buffers for uploading ( data is unmapped )
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private buffersForUploadingPending: GPUBuffer[];
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// pending buffers for computing
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private buffersPending: GPUBuffer[];
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// The reusable storage buffers for computing.
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private freeBuffers: Map<number, GPUBuffer[]>;
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// The reusable uniform buffers
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private freeUniformBuffers: Map<number, GPUBuffer[]>;
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// The external buffers registered users for IO Binding.
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private externalBuffers: Map<GPUBuffer, GpuDataId>;
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// The pendingBuffers for capture graph.
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// a SessionID -> GPUBuffer[] mapping.
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private capturedPendingBuffers: Map<number, GPUBuffer[]>;
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constructor(private backend: WebGpuBackend) {
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this.storageCache = new Map();
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this.freeBuffers = new Map();
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this.freeUniformBuffers = new Map();
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this.buffersForUploadingPending = [];
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this.buffersPending = [];
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this.externalBuffers = new Map();
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this.capturedPendingBuffers = new Map();
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}
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upload(id: GpuDataId, data: Uint8Array): void {
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const srcArrayBuffer = data.buffer;
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const srcOffset = data.byteOffset;
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const srcLength = data.byteLength;
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const size = calcNormalizedBufferSize(srcLength);
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// get destination gpu buffer
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const gpuDataCache = this.storageCache.get(id);
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if (!gpuDataCache) {
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throw new Error('gpu data for uploading does not exist');
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}
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if (gpuDataCache.originalSize !== srcLength) {
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throw new Error(`inconsistent data size. gpu data size=${gpuDataCache.originalSize}, data size=${srcLength}`);
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}
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// create gpu buffer
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const gpuBufferForUploading = this.backend.device.createBuffer(
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// eslint-disable-next-line no-bitwise
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{mappedAtCreation: true, size, usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC});
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// copy (upload) data
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const arrayBuffer = gpuBufferForUploading.getMappedRange();
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new Uint8Array(arrayBuffer).set(new Uint8Array(srcArrayBuffer, srcOffset, srcLength));
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gpuBufferForUploading.unmap();
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// GPU copy
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const commandEncoder = this.backend.getCommandEncoder();
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this.backend.endComputePass();
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commandEncoder.copyBufferToBuffer(gpuBufferForUploading, 0, gpuDataCache.gpuData.buffer, 0, size);
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LOG_DEBUG('verbose', () => `[WebGPU] GpuDataManager.upload(id=${id})`);
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this.buffersForUploadingPending.push(gpuBufferForUploading);
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}
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memcpy(sourceId: GpuDataId, destinationId: GpuDataId): void {
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// get source gpu buffer
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const sourceGpuDataCache = this.storageCache.get(sourceId);
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if (!sourceGpuDataCache) {
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throw new Error('source gpu data for memcpy does not exist');
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}
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// get destination gpu buffer
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const destinationGpuDataCache = this.storageCache.get(destinationId);
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if (!destinationGpuDataCache) {
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throw new Error('destination gpu data for memcpy does not exist');
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}
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if (sourceGpuDataCache.originalSize !== destinationGpuDataCache.originalSize) {
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throw new Error('inconsistent source and destination gpu data size');
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}
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const size = calcNormalizedBufferSize(sourceGpuDataCache.originalSize);
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// GPU copy
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const commandEncoder = this.backend.getCommandEncoder();
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this.backend.endComputePass();
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commandEncoder.copyBufferToBuffer(
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sourceGpuDataCache.gpuData.buffer, 0, destinationGpuDataCache.gpuData.buffer, 0, size);
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}
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registerExternalBuffer(buffer: GPUBuffer, originalSize: number, previousBuffer?: GPUBuffer): number {
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let id: number|undefined;
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if (previousBuffer) {
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id = this.externalBuffers.get(previousBuffer);
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if (id === undefined) {
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throw new Error('previous buffer is not registered');
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}
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if (buffer === previousBuffer) {
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LOG_DEBUG(
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'verbose',
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() => `[WebGPU] GpuDataManager.registerExternalBuffer(size=${originalSize}) => id=${
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id}, buffer is the same, skip.`);
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return id;
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} else if (this.backend.capturedCommandList.has(this.backend.currentSessionId!)) {
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throw new Error(`Registering a different external buffer under graph capture mode is not supported yet.
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Please use the previous external buffer!`);
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}
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this.externalBuffers.delete(previousBuffer);
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} else {
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id = createNewGpuDataId();
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}
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this.storageCache.set(id, {gpuData: {id, type: GpuDataType.default, buffer}, originalSize});
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this.externalBuffers.set(buffer, id);
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LOG_DEBUG(
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'verbose',
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() => `[WebGPU] GpuDataManager.registerExternalBuffer(size=${originalSize}) => id=${id}, registered.`);
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return id;
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}
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unregisterExternalBuffer(buffer: GPUBuffer): void {
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const id = this.externalBuffers.get(buffer);
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if (id !== undefined) {
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this.storageCache.delete(id);
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this.externalBuffers.delete(buffer);
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LOG_DEBUG('verbose', () => `[WebGPU] GpuDataManager.unregisterExternalBuffer() => id=${id}`);
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}
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}
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// eslint-disable-next-line no-bitwise
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create(size: number, usage = GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST): GpuData {
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const bufferSize = calcNormalizedBufferSize(size);
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let gpuBuffer;
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// Currently, only storage buffers are reused.
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// eslint-disable-next-line no-bitwise
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const isStorage = (usage & GPUBufferUsage.STORAGE) === GPUBufferUsage.STORAGE;
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// eslint-disable-next-line no-bitwise
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const isUniform = (usage & GPUBufferUsage.UNIFORM) === GPUBufferUsage.UNIFORM;
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if (isStorage || isUniform) {
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const freeBuffers = isStorage ? this.freeBuffers : this.freeUniformBuffers;
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let buffers = freeBuffers.get(bufferSize);
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if (!buffers) {
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buffers = [];
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freeBuffers.set(bufferSize, buffers);
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}
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if (buffers.length > 0) {
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gpuBuffer = buffers.pop() as GPUBuffer;
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} else {
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// create gpu buffer
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gpuBuffer = this.backend.device.createBuffer({size: bufferSize, usage});
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}
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} else {
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// create gpu buffer
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gpuBuffer = this.backend.device.createBuffer({size: bufferSize, usage});
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}
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const gpuData = {id: createNewGpuDataId(), type: GpuDataType.default, buffer: gpuBuffer};
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this.storageCache.set(gpuData.id, {gpuData, originalSize: size});
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LOG_DEBUG('verbose', () => `[WebGPU] GpuDataManager.create(size=${size}) => id=${gpuData.id}`);
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return gpuData;
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}
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get(id: GpuDataId): GpuData|undefined {
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return this.storageCache.get(id)?.gpuData;
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}
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release(id: GpuDataId): number {
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const cachedData = this.storageCache.get(id);
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if (!cachedData) {
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throw new Error('releasing data does not exist');
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}
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LOG_DEBUG('verbose', () => `[WebGPU] GpuDataManager.release(id=${id}), gpuDataId=${cachedData.gpuData.id}`);
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this.storageCache.delete(id);
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this.buffersPending.push(cachedData.gpuData.buffer);
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// cachedData.gpuData.buffer.destroy();
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return cachedData.originalSize;
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}
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async download(id: GpuDataId, getTargetBuffer: () => Uint8Array): Promise<void> {
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const cachedData = this.storageCache.get(id);
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if (!cachedData) {
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throw new Error('data does not exist');
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}
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await downloadGpuData(this.backend, cachedData.gpuData.buffer, cachedData.originalSize, getTargetBuffer);
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}
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refreshPendingBuffers(): void {
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for (const buffer of this.buffersForUploadingPending) {
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// upload buffer is only useful in the session creation time. So we don't need to reuse them in session running.
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buffer.destroy();
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}
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this.buffersForUploadingPending = [];
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if (this.buffersPending.length === 0) {
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return;
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}
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if (this.backend.sessionStatus === 'default') {
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for (const buffer of this.buffersPending) {
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// eslint-disable-next-line no-bitwise
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if ((buffer.usage & GPUBufferUsage.STORAGE) === GPUBufferUsage.STORAGE) {
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// Put the pending buffer to freeBuffers list instead of really destroying it for buffer reusing.
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this.freeBuffers.get(buffer.size)!.push(buffer);
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// eslint-disable-next-line no-bitwise
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} else if ((buffer.usage & GPUBufferUsage.UNIFORM) === GPUBufferUsage.UNIFORM) {
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// Put the pending buffer to freeUniformBuffers list instead of really destroying it for buffer reusing.
|
|
this.freeUniformBuffers.get(buffer.size)!.push(buffer);
|
|
} else {
|
|
buffer.destroy();
|
|
}
|
|
}
|
|
this.buffersPending = [];
|
|
} else {
|
|
// Don't release intermediate tensors in non-default mode.
|
|
// TODO: reuse the storage buffers in non-default mode.
|
|
let capturedBuffers = this.capturedPendingBuffers.get(this.backend.currentSessionId!);
|
|
if (!capturedBuffers) {
|
|
capturedBuffers = [];
|
|
this.capturedPendingBuffers.set(this.backend.currentSessionId!, capturedBuffers);
|
|
}
|
|
for (const buffer of this.buffersPending) {
|
|
capturedBuffers.push(buffer);
|
|
}
|
|
this.buffersPending = [];
|
|
}
|
|
}
|
|
|
|
dispose() {
|
|
this.freeBuffers.forEach((buffers) => {
|
|
buffers.forEach(buffer => {
|
|
buffer.destroy();
|
|
});
|
|
});
|
|
this.freeUniformBuffers.forEach((buffers) => {
|
|
buffers.forEach(buffer => {
|
|
buffer.destroy();
|
|
});
|
|
});
|
|
|
|
this.storageCache.forEach((storage) => {
|
|
storage.gpuData.buffer.destroy();
|
|
});
|
|
|
|
this.capturedPendingBuffers.forEach((buffers) => {
|
|
buffers.forEach(buffer => {
|
|
buffer.destroy();
|
|
});
|
|
});
|
|
this.storageCache = new Map();
|
|
this.freeBuffers = new Map();
|
|
this.freeUniformBuffers = new Map();
|
|
this.capturedPendingBuffers = new Map();
|
|
}
|
|
|
|
onReleaseSession(sessionId: number) {
|
|
// release the captured pending buffers.
|
|
const pendingBuffers = this.capturedPendingBuffers.get(sessionId);
|
|
if (pendingBuffers) {
|
|
pendingBuffers.forEach(buffer => {
|
|
buffer.destroy();
|
|
});
|
|
this.capturedPendingBuffers.delete(sessionId);
|
|
}
|
|
}
|
|
}
|
|
|
|
export const createGpuDataManager = (...args: ConstructorParameters<typeof GpuDataManagerImpl>): GpuDataManager =>
|
|
new GpuDataManagerImpl(...args);
|