Jiajia Qin 25f427466e [js/webgpu] Optimize ConvTranspose (Continue) (#23429) ... BUG #23273 This PR does below optimizations: 1. When output channels is one, 1) calculate the offset before the inchannel loop to reduce indices to offsets calculation, 2) split the `inputChannelsPerGroup` into `inputChannelsPerGroupInt` and `inputChannelsRemainder` parts so that we can always access 4 data for `inputChannelsPerGroupInt`. 2. Use precise initial value to reduce useless loop iterations. Thanks @jiangzhaoming 's suggestion's on this. With this PR, ConvTranspose becomes 3.7s from 8.4s on Intel Meteor Lake. On NV RTX 2000 Ada, it becomes 1.6s from 2.7s.		2025-01-22 08:59:17 -08:00
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webgpu	[js/webgpu] Optimize ConvTranspose (Continue) (#23429)	2025-01-22 08:59:17 -08:00
webnn	[WebNN] Fixes MLTensor caching across different contexts (#23100)	2024-12-17 12:51:16 -08:00
backend-webgpu.ts	[JS/WebGPU] Creating devices with subgroup features enabled if possible (#21833)	2024-11-07 02:13:40 -08:00
backend-webnn.ts	[WebNN] Fixed WebNN Module undefined issue (#22795)	2024-11-11 21:31:24 -08:00
init.ts	[JS/WebGPU] Creating devices with subgroup features enabled if possible (#21833)	2024-11-07 02:13:40 -08:00
log.ts	[js] change default formatter for JavaScript/TypeScript from clang-format to Prettier (#21728)	2024-08-14 16:51:22 -07:00
tensor-view.ts	[js/webgpu] support float16 for Clip (#21584)	2024-08-28 13:19:20 -07:00
util.ts	[JS/WebGPU] Support WASM64 (#21836)	2024-10-24 20:21:51 -07:00