CUDA: dedup MoE gate/up activation quantization (#25441)

* CUDA: dedup MoE gate/up activation quantization (fp4)

For MoE gate/up projections the src1 activation is broadcast across the
routed experts (ne11 == 1), so ids_src1 maps every one of a token's
n_expert_used slots to the same physical row. The MMQ path therefore
re-quantized each token's activation n_expert_used times.

For fp4 (NVFP4/MXFP4) src0, quantize each unique token row once instead of
once per expert. For NVFP4 a single quantize+scatter kernel
(quantize_scatter_mmq_nvfp4) quantizes each token once and writes the
resulting block_fp4_mmq straight to all n_expert_used slots, using an
inverse token->compact-row map (build_tok2c). MXFP4, and
GGML_CUDA_MOE_QUANT_GATHER=1, use a two-kernel variant: quantize unique
rows then gather into the expert-sorted layout (gather_mmq_fp4_blocks).
Both are bit-identical to the previous gather-then-quantize path (identical
source data, deterministic per-block quantization), verified by
test-backend-ops MUL_MAT_ID (type_a=nvfp4, broadcast b=1; 790/790 for the
default, gather, and per-expert paths) and by coherent end-to-end
generation. Set GGML_CUDA_NO_MOE_QUANT_DEDUP=1 to force the original
per-expert path.

Same-binary A/B on RTX 5090 (sm_120), Qwen3.6-35B-A3B-NVFP4 prefill @8192
(nsys, graphs-off; the unchanged mul_mat_q GEMM confirms stable clocks):
activation-quant GPU-busy drops 61% (78.2 -> 30.4 ms) with the fused
quantize+scatter, vs 33% (78.2 -> 52.8 ms) for the two-kernel gather. The
fused path avoids materializing and re-reading the 8x compact buffer,
writing the expert copies directly from registers.

* CUDA: bounds-check token ids in build_tok2c_kernel

Guard against malformed ids_src1: skip out-of-range token ids (t < 0 or
t >= n_tokens) and drop entries beyond n_expert_used per token instead of
writing past the token's tok2c region. No behavior change for valid MoE
routing data; test-backend-ops MUL_MAT_ID 790/790.

* Refactor the code based on review comments

- Removed previously added kernels that were not necessary anymore\
- Added an inverse mapping from (token, slot) to compact row. Each token is quantized once and scattered to its compact rows.

* Adding q8_1 support for dedup and addressing review comments

* Add pragma unrolls

* Remove redundant cudaMemsetAsync call

* Removing follow up redundancies

---------

Co-authored-by: praneshgo <227579474+praneshgo@users.noreply.github.com>

ggml/src/ggml-cuda/mmf.cu

@@ -85,7 +85,7 @@ void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * sr
         GGML_ASSERT(sis1 > 0);
 
         ggml_cuda_launch_mm_ids_helper(ids_d, ids_src_compact_dev.get(), ids_dst_compact_dev.get(), expert_bounds_dev.get(),
-            static_cast<int>(n_experts), static_cast<int>(n_tokens), static_cast<int>(n_expert_used), static_cast<int>(ne11), si1, sis1, ctx.stream());
+            static_cast<int>(n_experts), static_cast<int>(n_tokens), static_cast<int>(n_expert_used), static_cast<int>(ne11), si1, sis1, /*write_inverse =*/ false, ctx.stream());
         CUDA_CHECK(cudaGetLastError());
 
         ids_info.ids_src_compact   = ids_src_compact_dev.get();