jdelony/ik_llama.cpp
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eval-callback : sum over the full tensor, not just the printed slice (#2019)

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ggml_print_tensor() accumulated `sum` inside the truncated print loop, which
skips the middle of each row when ne[0] > 2*n (n = 3). The printed `sum =`
therefore only covered the first n and last n elements per row, not the whole
tensor. For example a {2688, 5} tensor reported the sum of 30 of its 13440
values. That makes the value useless for numerically comparing two runs, and it
disagrees with mainline llama.cpp, whose eval-callback sums every element in a
separate pass.

This factors the per-element read into a small helper and computes the sum in
its own loop over all elements (double accumulator). The truncated print is a
separate, unchanged pass, so only the printed `sum =` value changes. The change
is confined to examples/eval-callback.

Co-authored-by: mb8565 <244351746+mb8565@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
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mb8565 2026-06-24 01:57:19 -05:00 committed by GitHub
parent befbc0945b
commit a7d35d51dc
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1 changed files with 32 additions and 18 deletions
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50
examples/eval-callback/eval-callback.cpp
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@ -27,9 +27,38 @@ static std::string ggml_ne_string(const ggml_tensor * t) {
return str;
}
static float ggml_get_float_value(const uint8_t * data, ggml_type type, size_t i) {
if (type == GGML_TYPE_F16) {
return ggml_fp16_to_fp32(*(const ggml_fp16_t *) &data[i]);
} else if (type == GGML_TYPE_F32) {
return *(const float *) &data[i];
} else if (type == GGML_TYPE_I32) {
return (float) *(const int32_t *) &data[i];
} else if (type == GGML_TYPE_I16) {
return (float) *(const int16_t *) &data[i];
} else if (type == GGML_TYPE_I8) {
return (float) *(const int8_t *) &data[i];
} else {
GGML_ABORT("fatal error");
}
}
static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
GGML_ASSERT(n > 0);
float sum = 0;
// Compute the FULL tensor sum first (debug aid — was previously summing
// only the displayed first/last elements, which made it useless for
// comparing two binaries on the same tensor).
double sum = 0.0;
for (int64_t i3 = 0; i3 < ne[3]; i3++) {
for (int64_t i2 = 0; i2 < ne[2]; i2++) {
for (int64_t i1 = 0; i1 < ne[1]; i1++) {
for (int64_t i0 = 0; i0 < ne[0]; i0++) {
size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
sum += ggml_get_float_value(data, type, i);
}
}
}
}
for (int64_t i3 = 0; i3 < ne[3]; i3++) {
printf(" [\n");
for (int64_t i2 = 0; i2 < ne[2]; i2++) {
@ -50,22 +79,7 @@ static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne
i0 = ne[0] - n;
}
size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
float v;
if (type == GGML_TYPE_F16) {
v = ggml_fp16_to_fp32(*(ggml_fp16_t *) &data[i]);
} else if (type == GGML_TYPE_F32) {
v = *(float *) &data[i];
} else if (type == GGML_TYPE_I32) {
v = (float) *(int32_t *) &data[i];
} else if (type == GGML_TYPE_I16) {
v = (float) *(int16_t *) &data[i];
} else if (type == GGML_TYPE_I8) {
v = (float) *(int8_t *) &data[i];
} else {
GGML_ABORT("fatal error");
}
printf("%12.4f", v);
sum += v;
printf("%12.4f", ggml_get_float_value(data, type, i));
if (i0 < ne[0] - 1) printf(", ");
}
printf("],\n");
@ -73,7 +87,7 @@ static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne
printf(" ],\n");
}
printf(" ]\n");
printf(" sum = %f\n", sum);
printf(" sum = %f\n", (float) sum);
}
}