Jaebaek Seo
Jaebaek Seo
We want to test HLSL shaders using SwiftShader. When I build Amber witht the following command, I met linking errors: ``` mkdir -p out/SWFT && cd out/SWFT cmake -GNinja -DAMBER_USE_DXC=true...
If you see [tests/cases/probe_without_clear_command.expect_fail.amber](tests/cases/probe_without_clear_command.expect_fail.amber), we expect it fails with "FrameBuffer::ChangeFrameImageLayout new layout cannot be kProbe from kInit" message. We should check the error messages for tests that we expect failures.
Spec says ``` maxUniformBufferRange is the maximum value that can be specified in the range member of any VkDescriptorBufferInfo structures passed to a call to vkUpdateDescriptorSets for descriptors of type...
Currently we put all tests into `tests/cases`. We cannot directly use them on Android because we will not put glslang and spirv-as to Android. @dj2 @dneto0 please doublecheck this. Write...
Following options are newly added by #144 : * AMBER_SKIP_TESTS * AMBER_SKIP_SPIRV_TOOLS * AMBER_SKIP_SHADERC @dj2 do you think we should add kokoro tests for them?
If some libraries in `third_party/` are already installed in the system, current Amber build adopts the system library instead of the one in `third_party/` and it can lead to build...
SPIR-V code gen has to support bitfield members for [HLSL 2021](https://devblogs.microsoft.com/directx/announcing-hlsl-2021/#bitfield-members-in-data-types). We must decide the type and memory layout for members with bitfields. Based on experiments with the DXIL code...
We support loading "bytes" data from a random device address without a memory layout support in #4226. In the future, we will add loading "structured" data from a random device...
After installing tensorflow and ngraph-tf, executing `python -c "import tensorflow as tf; print('TensorFlow version: ',tf.__version__);import ngraph_bridge; print(ngraph_bridge.__version__)"` showed the following error: ``` ➜ ~ source tf-test/bin/activate (tf-test) ➜ ~ pip...