CUDA & CPU: support F32 kernel type for CONV_TRANSPOSE_2D (#17094)

* 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>

ggml/src/ggml-cpu/ggml-cpu.c

@@ -2871,8 +2871,12 @@ struct ggml_cplan ggml_graph_plan(
                         const int64_t ne11 = node->src[1]->ne[1]; // H
                         const int64_t ne12 = node->src[1]->ne[2]; // Channels In
 
-                        cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03;
-                        cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12;
+                        GGML_ASSERT(node->src[0]->type == GGML_TYPE_F16 || node->src[0]->type == GGML_TYPE_F32);
+                        GGML_ASSERT(node->src[1]->type == GGML_TYPE_F32);
+
+                        cur += ggml_type_size(node->src[0]->type) * ne00 * ne01 * ne02 * ne03;
+                        cur += ggml_type_size(node->src[0]->type) * ne10 * ne11 * ne12;
+
                     } break;
                 case GGML_OP_TOP_K:
                     {