CUDA: add COL2IM_1D op

[Ssamdeman]

Overview

Implements GGML_OP_COL2IM_1D on the CUDA backend — the inverse of IM2COL and the missing counterpart to the CPU reference merged in #24206. Uses an output-centric gather kernel (no atomics), templated over f32/f16/bf16 with fp32 accumulation. The host wrapper reads op_params = [s0, OC, p0] and derives K = src->ne[0] / OC.

Testing:

• test-backend-ops -o COL2IM_1D → 33/33 OK across all dtype × kernel/stride/padding cases
• Full test-backend-ops regression clean (12868/12868)
• Regenerated docs/ops.md via scripts/create_ops_docs.py

Additional information

The kernel math was verified line-by-line against the CPU _impl reference. col2im_1d.cu/.cuh are self-contained; the sibling im2col files are untouched.

Requirements

• I have read and agree with the contributing guidelines

• AI usage disclosure:

• AI usage disclosure: YES. I authored the code in this PR myself — I wrote the CUDA kernel, host wrapper, and backend wiring based on my own understanding of the col2im_1d math and the ggml backend structure. I used AI assistively in two ways: (1) to explain ggml architecture and help me verify my indexing/memory-layout assumptions against the merged CPU reference, and (2) when my tests failed, I gave the AI my code and the errors, and it helped me understand why they were failing and what to correct. I reviewed and applied the fixes myself, I understand every line in the final code, and I take full responsibility for all submitted changes.

[ggml-gh-bot]

Hi @Ssamdeman, thanks for your contribution!

Per our contribution guidelines, the automated PR checker found the following issue(s) that need your attention:

• AI-generated content: This project does not accept PRs, descriptions or commit messages that are fully or predominantly AI-generated. If you have used AI to assist you in writing code, please make sure to disclose that explicitly.

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[Ssamdeman]

Thanks. I clarified it more!

[am17an]

remove the ops.csv changes