ggml-blas: refactor BLAS backend

[taronaeo]

I saw this comment and felt a bit sad since we use the BLAS backend a lot: #14909 (comment)

Currently, the BLAS backend dequantizes the weight tensors on the fly, which runs on every matrix multiplication operation. This is not ideal, and more performance gains can be attained by pre-dequantizing the weight tensors and running the matrix multiplication calculation on the dequantized buffers.

We currently see a 53.56% performance improvement for Prompt Processing, and 107.79% for Token Generation.

Next up in the roadmap will be to probably introduce the following:

1. GGML_OP_MUL_MAT_ID for MoE models
2. LibXSMM for smaller matrix multiplication

But this PR focuses more on improving the performance first.

Changes

1. Dequantization for weight tensors now runs only once, during tensor init.
2. test-backend-ops.cpp now sets weight tensors to GGML_BACKEND_BUFFER_USAGE_WEIGHTS instead of GGML_BACKEND_BUFFER_USAGE_ANY which is used to detect when to dequantize a tensor.

Performance Benchmark

$ build/bin/llama-bench -m ~/Library/Caches/llama.cpp/LiquidAI_LFM2-8B-A1B-GGUF_LFM2-8B-A1B-Q4_K_M.gguf -r 1 -t 8

This PR

model	size	params	backend	threads	test	t/s
lfm2moe 8B.A1B Q4_K - Medium	4.70 GiB	8.34 B	BLAS	8	pp512	221.20 ± 0.00
lfm2moe 8B.A1B Q4_K - Medium	4.70 GiB	8.34 B	BLAS	8	tg128	77.75 ± 0.00

build: 717531b (7364)

Upstream

model	size	params	backend	threads	test	t/s
lfm2moe 8B.A1B Q4_K - Medium	4.70 GiB	8.34 B	BLAS	8	pp512	127.74 ± 0.00
lfm2moe 8B.A1B Q4_K - Medium	4.70 GiB	8.34 B	BLAS	8	tg128	23.29 ± 0.00

build: 254098a (7399)

AI Declaration: AI has been used to modify the test-backend-ops.cpp code to set weight tensors usage to GGML_BACKEND_BUFFER_USAGE_WEIGHTS because I couldn't figure it out without it crashing. Modifications to the BLAS backend were written by a human.

[taronaeo]

Made the PR ready for review by accident - pressed the wrong button.