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### Description This PR provided a vectorized matmul algorithm. In most situations, we still go to the workgroup memory optimized matmul. But for some situations, like N and K are very small, using workgroup optimized matmul can't fully utilize the underlying hardware due to the 32x32 tile size. So for very small N/K, we switch to the naive vectorized matmul algorithm to improve the hardware execution unit usage. With this PR, matmul with input0: [1, 36864, 3], input1: [1, 3, 3], input2: [3] becomes less than 1 ms from 4.34 ms on Intel Gen9 GPUs. |
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| .. | ||
| activation_util.ts | ||
| conv2d_mm_webgpu.ts | ||
| conv_backprop_mm_webgpu.ts | ||
| conv_backprop_webgpu.ts | ||
| conv_util.ts | ||
| matmul_packed_webgpu.ts | ||