Honor mmap setting when using tensor overrides

[ikawrakow]

The reason why mmap was disabled when using tensor overrides is this:

• When the command line argument is parsed (and the override buffer is set to CPU), we get the buffer type returned by ggml_backend_cpu_buffer_type()
• The tensor loading logic uses llama_default_buffer_type_cpu(true) instead to see if a buffer is a CPU buffer and hence can be memory mapped.
• When CUDA (or some other backend) is enabled, llama_default_buffer_type_cpu(true) returns a different buffer type (CUDA_Host in the case of the CUDA backend).
• As a result, the tensors set to be stored in the CPU memory buffer are not memory mapped

This PR fixes that by asking the buffer type to be either llama_default_buffer_type_cpu(true) or ggml_backend_cpu_buffer_type() to be eligible for using mmap.

Note, however, that -rtr still disables mmap because otherwise the model would be overwritten with the repacked tensors.

[ubergarm]

Wow sweet! I just got back home and saw this, pull'd and rebuilt and got my custom quant running locally on the 9950X + 96GB DDR5-6400 RAM + 3090TI 24GB! Got about 3 tok/sec generation on a quick initial test.

This quant is heavy (q8_0 on the GPU offload tensors) but still fits 32k context with enough left-over for x windows! Better perplexity than the unsloth UD-Q2_K_XL too.

Amazing that mmap() and Linux page cache can serve ~238GiB model weights off of a PCIe Gen 5 Crucial T700 2TB NVMe and 2x48GB tuned DIMMs.

This setup might benefit from -ser 6,1 too! Plenty to try out, thanks!

./build/bin/llama-server \
    --alias ubergarm/DeepSeek-R1-Q2_K_R4 \
    --model /mnt/ai/models/ubergarm/DeepSeek-R1-GGUF/DeepSeek-R1-GGUF-Q2_K_R4.gguf \
    --ctx-size 32768 \
    -ctk q8_0 \
    -mla 2 -fa \
    -amb 512 \
    -fmoe \
    --n-gpu-layers 63 \
    --override-tensor exps=CPU \
    --parallel 1 \
    --threads 16 \
    --host 127.0.0.1 \
    --port 8080

...
llama_model_loader: - type  f32:  361 tensors
llama_model_loader: - type q8_0:  612 tensors
llama_model_loader: - type q2_k_r4:  116 tensors
llama_model_loader: - type q3_k_r4:   58 tensors
...