vbatts/llama.cpp
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Bugfix printf tensor

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Added a function to access the tensor data by index (like an array) and print the value as float/int.
Updated the tensor print loops to use the new function.
This fixes two bugs:
1) the previous printf didn't work after a quickfix
2) the previous printf did not show rows/columns in the right place
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John 2024-02-08 03:00:00 +01:00 • committed by GitHub
parent 2c1e8d3205
commit b639e2a73f
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GPG key ID: B5690EEEBB952194
1 changed files with 72 additions and 13 deletions
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85
ggml-backend.c
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@ -1836,22 +1836,80 @@ bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t
return true;
}
void ggml_printTensorSample(const char *prefix, const struct ggml_tensor * tensor) {
// printf one number from a tensor (cont only) like an array index[0][1][2][3]
void ggml_print_f32_index(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) {
void * data;
if (!ggml_is_contiguous(tensor)) {
int64_t id[4] = {i0, i1, i2, i3};
int index_cont = i0*tensor->ne[3]*tensor->ne[2]*tensor->ne[1]*tensor->nb[0] + i1*tensor->ne[3]*tensor->ne[2]*tensor->nb[1] + i2*tensor->ne[3]*tensor->nb[2] + i3*tensor->nb[3];
ggml_unravel_index(tensor, index_cont, &id[0], &id[1], &id[2], &id[3]); // untested
printf("NONCONT");
return;
i0 = id[0];
i1 = id[1];
i2 = id[2];
i3 = id[3];
data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; // untested
} else
{
data = (char *) tensor->data + i0*tensor->ne[3]*tensor->ne[2]*tensor->ne[1]*tensor->nb[0] + i1*tensor->ne[3]*tensor->ne[2]*tensor->nb[1] + i2*tensor->ne[3]*tensor->nb[2] + i3*tensor->nb[3];
// void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3];
}
char *tensor_data;
if (tensor->backend != GGML_BACKEND_CPU) {
switch (tensor->type) {
case GGML_TYPE_I8:
printf("%8d", *((int8_t *) data));
break;
case GGML_TYPE_I16:
printf("%8d", *((int16_t *) data));
break;
case GGML_TYPE_I32:
printf("%8d", *((int32_t *) data));
break;
case GGML_TYPE_F16:
printf("%8.4f", GGML_FP16_TO_FP32(*((ggml_fp16_t *) data)));
break;
case GGML_TYPE_F32:
printf("%8.4f", *((float *) data));
break;
default:
printf("UNKTYPE");
break;
}
}
void ggml_printTensorSample(const char *prefix, const struct ggml_tensor * tensor_in) {
struct ggml_tensor tensor_dummy; // avoid any ctx
struct ggml_tensor * tensor = &tensor_dummy;
if (tensor_in->backend != GGML_BACKEND_CPU) {
// for any mmap solution we can actually set the CPU data of a tensor during load even if it's GPU offloaded
// this shouldn't have a negative effect, worked well in ggllm, saves the need of tensor_get operations for weights
if (tensor->buffer == NULL) {
if (tensor_in->buffer == NULL) {
printf("ggml_printTensorSample: tensor buffer is NULL\n");
return;
}
tensor_data = (char *) malloc(ggml_nbytes(tensor));
ggml_backend_tensor_get(tensor, tensor_data, 0, ggml_nbytes(tensor));
tensor->data = (char *) malloc(ggml_nbytes(tensor_in));
ggml_backend_tensor_get(tensor_in, tensor->data, 0, ggml_nbytes(tensor_in));
memcpy(tensor->name, tensor_in->name, sizeof(tensor->name));
tensor->type = tensor_in->type;
tensor->ne[0] = tensor_in->ne[0];
tensor->ne[1] = tensor_in->ne[1];
tensor->ne[2] = tensor_in->ne[2];
tensor->ne[3] = tensor_in->ne[3];
tensor->nb[0] = tensor_in->nb[0];
tensor->nb[1] = tensor_in->nb[1];
tensor->nb[2] = tensor_in->nb[2];
tensor->nb[3] = tensor_in->nb[3];
tensor->backend = GGML_BACKEND_CPU;
tensor->op = tensor_in->op;
tensor->view_offs = tensor_in->view_offs;
tensor->view_src = tensor_in->view_src;
} else
{
tensor_data = tensor->data;
if (tensor_data == NULL) {
tensor = tensor_in;
if (tensor->data == NULL) {
printf("ggml_printTensorSample: tensor data is NULL\n");
return;
}
@ -1876,7 +1934,8 @@ void ggml_printTensorSample(const char *prefix, const struct ggml_tensor * tenso
if (n_dims == 1) {
printf("| 1: ");
for(int i = 0; i < tensor->ne[0] && i < MAX_ELEMENTS_ROW; i++){
printf("%11.3g, ", *(double *)((char *) tensor_data + i*tensor->nb[0]));
ggml_print_f32_index(tensor, i, 0, 0, 0);
if(i == MAX_ELEMENTS_ROW - 1 && tensor->ne[0] > MAX_ELEMENTS_ROW) printf(", ..");
}
if(MAX_ELEMENTS_ROW < tensor->ne[0]) printf(", ..");
printf("\n%s", sep);
@ -1885,7 +1944,7 @@ void ggml_printTensorSample(const char *prefix, const struct ggml_tensor * tenso
for(int i = 0; i < tensor->ne[0] && i < MAX_ELEMENTS_ROW; i++){
printf("| %d: ", i+1);
for(int j = 0; j < tensor->ne[1] && j < MAX_ELEMENTS_COL; j++){
printf("%11.4g ", *(double *)((char *) tensor_data + i*tensor->nb[0] + j*tensor->nb[1]));
ggml_print_f32_index(tensor, i, j, 0, 0);
if(j == MAX_ELEMENTS_COL - 1 && tensor->ne[1] > MAX_ELEMENTS_COL) printf(", ..");
}
printf("\n");
@ -1898,7 +1957,7 @@ void ggml_printTensorSample(const char *prefix, const struct ggml_tensor * tenso
for(int j = 0; j < tensor->ne[1] && j < MAX_ELEMENTS_COL; j++){
printf("[");
for(int k = 0; k < tensor->ne[2] && k < MAX_ELEMENTS_LAYER; k++){
printf("%11.4g", *(double *)((char *) tensor_data + i*tensor->nb[0] + j*tensor->nb[1] + k*tensor->nb[2]));
ggml_print_f32_index(tensor, i, j, k, 0);
if(k < tensor->ne[2] - 1 && k < MAX_ELEMENTS_LAYER - 1)
printf(", ");
}
@ -1917,7 +1976,7 @@ void ggml_printTensorSample(const char *prefix, const struct ggml_tensor * tenso
for(int j = 0; j < tensor->ne[2] && j < MAX_ELEMENTS_COL; j++){
printf("[");
for(int k = 0; k < tensor->ne[3] && k < MAX_ELEMENTS_LAYER; k++){
printf("%11.4f", *(double *)((char *) tensor_data + batch*tensor->nb[0] + i*tensor->nb[1] + j*tensor->nb[2] + k*tensor->nb[3]));
ggml_print_f32_index(tensor, batch, i, j, k);
if(k < tensor->ne[3] - 1 && k < MAX_ELEMENTS_LAYER - 1)
printf(", ");
}
@ -1931,7 +1990,7 @@ void ggml_printTensorSample(const char *prefix, const struct ggml_tensor * tenso
}
}
if (tensor->backend != GGML_BACKEND_CPU)
free(tensor_data);
free(tensor->data);
}
void ggml_tensor_printf(const struct ggml_tensor *tensor, char *prefix, int line, bool extended, bool print_sample) {