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fix: Compute the full sum in llama-eval-callback, not just the sum of printed values (#15637)

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This makes it much easier to compare between llama.cpp and transformers!

https://github.com/ggml-org/llama.cpp/issues/nemotron-nano-15409
Branch: gabe-l-hart/nvidia-nemotron-nano-15409

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
This commit is contained in:
Gabe Goodhart
2025-08-28 15:27:36 -05:00
committed by GitHub
parent c97dc09391
commit a8bca68f72
1 changed files with 32 additions and 18 deletions
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50
examples/eval-callback/eval-callback.cpp
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@@ -28,9 +28,40 @@ static std::string ggml_ne_string(const ggml_tensor * t) {
return str; return str;
} }
static float ggml_get_float_value(uint8_t * data, ggml_type type, const size_t * nb, size_t i0, size_t i1, size_t i2, size_t i3) {
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_I64) {
v = (float) *(int64_t *) &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");
}
return v;
}
static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) { 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); GGML_ASSERT(n > 0);
float sum = 0; float sum = 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++) {
const float v = ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
sum += v;
}
}
}
}
for (int64_t i3 = 0; i3 < ne[3]; i3++) { for (int64_t i3 = 0; i3 < ne[3]; i3++) {
LOG(" [\n"); LOG(" [\n");
for (int64_t i2 = 0; i2 < ne[2]; i2++) { for (int64_t i2 = 0; i2 < ne[2]; i2++) {
@@ -50,25 +81,8 @@ static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne
LOG("..., "); LOG("..., ");
i0 = ne[0] - n; i0 = ne[0] - n;
} }
size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0]; const float v = ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
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_I64) {
v = (float) *(int64_t *) &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");
}
LOG("%12.4f", v); LOG("%12.4f", v);
sum += v;
if (i0 < ne[0] - 1) LOG(", "); if (i0 < ne[0] - 1) LOG(", ");
} }
LOG("],\n"); LOG("],\n");