onnxruntime/js/web/lib/wasm/jsep/webgpu/ops/3rd-party
Jiajia Qin ccbe264a39
[js/webgpu] Add LeakyRelu activation for fusedConv (#19369)
### Description
This PR 1) adds LeakyRelu activation for fusedConv; 2) makes `vec4<f16>`
value work with `float32` uniforms attributes.

For example:
`clamp(value, vec4<f16>(uniforms.clip_min),
vec4<f16>(uniforms.clip_max)` will throw compilation errors since
`uniforms.clip_min` and `uniforms.clip_min` are `f32` not `f16`. So we
need to change it to `clamp(value, vec4<f16>(f16(uniforms.clip_min)),
vec4<f16>(f16(uniforms.clip_max))`

And above problem was introduced when we make activation attributes as
uniforms instead of constant.

BTW, after adding LeakyRelu, `realesrgan-t256` model can pass.
2024-02-02 09:06:38 -08:00
..
activation_util.ts
conv2d_mm_webgpu.ts [js/webgpu] Add LeakyRelu activation for fusedConv (#19369) 2024-02-02 09:06:38 -08:00
conv_backprop_mm_webgpu.ts [js/webgpu] Refactor createTensorShapeVariables (#18883) 2024-02-01 17:59:00 -08:00
conv_backprop_webgpu.ts [js/webgpu] Refactor createTensorShapeVariables (#18883) 2024-02-01 17:59:00 -08:00
conv_util.ts [js/webgpu] Refactor matmul conv to support uniforms for matmul (#18452) 2023-11-22 14:42:55 -08:00
matmul_packed_webgpu.ts [js/webgpu] Add LeakyRelu activation for fusedConv (#19369) 2024-02-02 09:06:38 -08:00