0a524f2404
* Refactor CUDA 2D transpose implementation to support multiple kernel types and improve parameter handling - Introduced a `conv2d_transpose_params` struct for better parameter management. - Updated `conv2d_transpose_kernel` to be templated for different kernel types (float and half). - Modified `ggml_cuda_conv_2d_transpose_p0` to handle both F16 and F32 kernel types. - Enhanced test cases to validate functionality for both kernel types. * Refactor test cases for 2D convolution transpose to support dynamic kernel types - Updated `test_conv_transpose_2d` structure to improve parameter handling by reordering constructor arguments. - Enhanced test case generation to iterate over kernel types, allowing for flexible testing of different configurations. - Removed hardcoded kernel type instances in favor of a loop for better maintainability and scalability. * Refactor ggml_compute_forward_conv_transpose_2d to support both F16 and F32 tensor types. * Refactor conv2d transpose kernel to use a template for kernel type, enhancing flexibility for different data types. Update test cases to include both F16 and F32 tensor types for comprehensive coverage. * Update ggml/src/ggml-cuda/conv2d-transpose.cu Co-authored-by: Aman Gupta <amangupta052@gmail.com> * Update ggml/src/ggml-cpu/ggml-cpu.c Co-authored-by: Aman Gupta <amangupta052@gmail.com> * Refactor conv2d transpose implementation by removing the conv2d_transpose_params struct and dispatching with direct kernel launch. * Enhance cpu conv2d transpose implementation by introducing a templated kernel type for improved flexibility with F16 and F32 data types. --------- Co-authored-by: Aman Gupta <amangupta052@gmail.com> |
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