examples(eval-callback): Eval callback verbosity

[gabe-l-hart]

Description

This PR is extracted from #16982 since it's an isolated change that's not strictly related to implementing the SSD algorithm.

The changes in this PR add the ability to print more verbose tensors with llama-eval-callback. There are two higher-verbosity states available:

• -lv 1: Print all tensors with 8 digits of presion instead of 5
• -lv >1: Print all tensors with 8 digits of presion instead of 5 AND print all tensor values rather than the slimmed begin/end view (this is VERY verbose!)

Sample Output

Current (no -lv)

./bin/llama-eval-callback -m ~/models/ibm-granite/granite-4.0-300m/ggml-model-Q8_0.gguf --temp 0 -p "Hello world"

...
ggml_debug:              result_norm = (f32)        MUL(norm{1024, 1, 1, 1}, output_norm.weight{1024, 1, 1, 1}}) = {1024, 1, 1, 1}
                                     [
                                      [
                                       [      6.0798,      -0.2350,      -9.4697, ...,      -6.9825,      -0.5371,      -4.7651],
                                      ],
                                     ]
                                     sum = 145.378464
ggml_debug:                 node_931 = (f32)    MUL_MAT(token_embd.weight{1024, 100352, 1, 1}, result_norm{1024, 1, 1, 1}}) = {100352, 1, 1, 1}
                                     [
                                      [
                                       [     60.6613,      56.7787,      32.9983, ...,     -30.6528,     -30.6563,     -30.6577],
                                      ],
                                     ]
                                     sum = -72923.351562
ggml_debug:            result_output = (f32)      SCALE(node_931{100352, 1, 1, 1}, }) = {100352, 1, 1, 1}
                                     [
                                      [
                                       [     15.1653,      14.1947,       8.2496, ...,      -7.6632,      -7.6641,      -7.6644],
                                      ],
                                     ]
                                     sum = -18230.837891
...

-lv 1 / --verbose

./bin/llama-eval-callback -m ~/models/ibm-granite/granite-4.0-300m/ggml-model-Q8_0.gguf --temp 0 -p "Hello world" -lv 1

...
ggml_debug:              result_norm = (f32)        MUL(norm{1024, 1, 1, 1}, output_norm.weight{1024, 1, 1, 1}}) = {1024, 1, 1, 1}
                                     [
                                      [
                                       [  6.07975531,  -0.23501977,  -9.46970558, ...,  -6.98248482,  -0.53711814,  -4.76507378],
                                      ],
                                     ]
                                     sum = 145.378464
ggml_debug:                 node_931 = (f32)    MUL_MAT(token_embd.weight{1024, 100352, 1, 1}, result_norm{1024, 1, 1, 1}}) = {100352, 1, 1, 1}
                                     [
                                      [
                                       [ 60.66127014,  56.77870941,  32.99830627, ..., -30.65284157, -30.65631485, -30.65769768],
                                      ],
                                     ]
                                     sum = -72923.351562
ggml_debug:            result_output = (f32)      SCALE(node_931{100352, 1, 1, 1}, }) = {100352, 1, 1, 1}
                                     [
                                      [
                                       [ 15.16531754,  14.19467735,   8.24957657, ...,  -7.66321039,  -7.66407871,  -7.66442442],
                                      ],
                                     ]
                                     sum = -18230.837891

...

-lv 2

WARNING: It's best to redirect this to a file!

./bin/llama-eval-callback -m ~/models/ibm-granite/granite-4.0-300m/ggml-model-Q8_0.gguf --temp 0 -p "Hello world" -lv 2 > tmp.log

output

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                                       [  0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000,   0.00000000, 

[gabe-l-hart]

As I was making the PR, I think the -lv 2 is too verbose for almost all circumstances, so --verbose should somehow map to -lv 1 since that's probably what most people will try first. I'll see if it's possible to redirect this since those flags are coming from common.