Merge b639e2a73f into abd894ad96
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261
ggml-backend.c
261
ggml-backend.c
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@ -8,6 +8,7 @@
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <inttypes.h>
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#define MAX(a, b) ((a) > (b) ? (a) : (b))
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@ -2223,3 +2224,263 @@ bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t
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return true;
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}
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// printf one number from a tensor (cont only) like an array index[0][1][2][3]
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void ggml_print_f32_index(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) {
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void * data;
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if (!ggml_is_contiguous(tensor)) {
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int64_t id[4] = {i0, i1, i2, i3};
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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];
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ggml_unravel_index(tensor, index_cont, &id[0], &id[1], &id[2], &id[3]); // untested
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printf("NONCONT");
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return;
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i0 = id[0];
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i1 = id[1];
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i2 = id[2];
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i3 = id[3];
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data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; // untested
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} else
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{
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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];
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// void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3];
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}
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switch (tensor->type) {
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case GGML_TYPE_I8:
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printf("%8d", *((int8_t *) data));
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break;
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case GGML_TYPE_I16:
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printf("%8d", *((int16_t *) data));
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break;
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case GGML_TYPE_I32:
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printf("%8d", *((int32_t *) data));
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break;
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case GGML_TYPE_F16:
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printf("%8.4f", GGML_FP16_TO_FP32(*((ggml_fp16_t *) data)));
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break;
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case GGML_TYPE_F32:
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printf("%8.4f", *((float *) data));
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break;
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default:
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printf("UNKTYPE");
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break;
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}
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}
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void ggml_printTensorSample(const char *prefix, const struct ggml_tensor * tensor_in) {
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struct ggml_tensor tensor_dummy; // avoid any ctx
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struct ggml_tensor * tensor = &tensor_dummy;
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if (tensor_in->backend != GGML_BACKEND_CPU) {
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// for any mmap solution we can actually set the CPU data of a tensor during load even if it's GPU offloaded
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// this shouldn't have a negative effect, worked well in ggllm, saves the need of tensor_get operations for weights
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if (tensor_in->buffer == NULL) {
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printf("ggml_printTensorSample: tensor buffer is NULL\n");
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return;
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}
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tensor->data = (char *) malloc(ggml_nbytes(tensor_in));
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ggml_backend_tensor_get(tensor_in, tensor->data, 0, ggml_nbytes(tensor_in));
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memcpy(tensor->name, tensor_in->name, sizeof(tensor->name));
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tensor->type = tensor_in->type;
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tensor->ne[0] = tensor_in->ne[0];
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tensor->ne[1] = tensor_in->ne[1];
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tensor->ne[2] = tensor_in->ne[2];
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tensor->ne[3] = tensor_in->ne[3];
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tensor->nb[0] = tensor_in->nb[0];
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tensor->nb[1] = tensor_in->nb[1];
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tensor->nb[2] = tensor_in->nb[2];
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tensor->nb[3] = tensor_in->nb[3];
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tensor->backend = GGML_BACKEND_CPU;
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tensor->op = tensor_in->op;
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tensor->view_offs = tensor_in->view_offs;
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tensor->view_src = tensor_in->view_src;
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} else
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{
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tensor = tensor_in;
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if (tensor->data == NULL) {
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printf("ggml_printTensorSample: tensor data is NULL\n");
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return;
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}
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}
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const char *sep = "+-------------------------------------------------------------------------------------------+\n";
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const int MAX_ELEMENTS_ROW = 10;
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const int MAX_ELEMENTS_COL = 6;
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const int MAX_ELEMENTS_LAYER = 3; // layered
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const int MAX_ELEMENTS_BATCH = 2; // repeated display
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const char *dimensionLabels[] = {"Row", "Col", "Layer", "Batch"};
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printf("\n%s| Content of %s \"%s\" (%d dim)\n", sep, prefix, tensor->name, ggml_n_dims(tensor));
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printf("| Total Elements : [ ");
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for (int i = 0; i < ggml_n_dims(tensor); i++)
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printf("%s:%-3" PRId64 " ", dimensionLabels[i], tensor->ne[i]);
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printf("]\n%s", sep);
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int n_dims = ggml_n_dims(tensor);
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if (n_dims == 1) {
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printf("| 1: ");
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for(int i = 0; i < tensor->ne[0] && i < MAX_ELEMENTS_ROW; i++){
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ggml_print_f32_index(tensor, i, 0, 0, 0);
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if(i == MAX_ELEMENTS_ROW - 1 && tensor->ne[0] > MAX_ELEMENTS_ROW) printf(", ..");
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}
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if(MAX_ELEMENTS_ROW < tensor->ne[0]) printf(", ..");
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printf("\n%s", sep);
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}
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else if (n_dims == 2) {
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for(int i = 0; i < tensor->ne[0] && i < MAX_ELEMENTS_ROW; i++){
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printf("| %d: ", i+1);
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for(int j = 0; j < tensor->ne[1] && j < MAX_ELEMENTS_COL; j++){
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ggml_print_f32_index(tensor, i, j, 0, 0);
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if(j == MAX_ELEMENTS_COL - 1 && tensor->ne[1] > MAX_ELEMENTS_COL) printf(", ..");
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}
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printf("\n");
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}
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if(MAX_ELEMENTS_ROW < tensor->ne[0]) printf(" .. additional rows\n");
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printf("%s", sep);
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} else if(n_dims == 3) {
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for(int i = 0; i < tensor->ne[0] && i < MAX_ELEMENTS_ROW; i++){
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printf("| Row %d: ", i+1);
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for(int j = 0; j < tensor->ne[1] && j < MAX_ELEMENTS_COL; j++){
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printf("[");
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for(int k = 0; k < tensor->ne[2] && k < MAX_ELEMENTS_LAYER; k++){
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ggml_print_f32_index(tensor, i, j, k, 0);
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if(k < tensor->ne[2] - 1 && k < MAX_ELEMENTS_LAYER - 1)
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printf(", ");
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}
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if(MAX_ELEMENTS_LAYER < tensor->ne[2]) printf(", ..");
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printf("] ");
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}
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printf("\n");
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}
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if(MAX_ELEMENTS_ROW < tensor->ne[0]) printf(" ... additional layers\n");
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printf("%s", sep);
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} else if(n_dims == 4) {
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for(int batch = 0; batch < tensor->ne[0] && batch < MAX_ELEMENTS_BATCH; batch++){
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printf("Batch %d\n", batch+1);
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for(int i = 0; i < tensor->ne[1] && i < MAX_ELEMENTS_ROW; i++){
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printf("| Row %d: ", i+1);
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for(int j = 0; j < tensor->ne[2] && j < MAX_ELEMENTS_COL; j++){
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printf("[");
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for(int k = 0; k < tensor->ne[3] && k < MAX_ELEMENTS_LAYER; k++){
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ggml_print_f32_index(tensor, batch, i, j, k);
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if(k < tensor->ne[3] - 1 && k < MAX_ELEMENTS_LAYER - 1)
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printf(", ");
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}
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if(MAX_ELEMENTS_LAYER < tensor->ne[3]) printf(", ..");
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printf("] ");
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}
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printf("\n");
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}
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if(MAX_ELEMENTS_BATCH < tensor->ne[0]) printf(" ... additional batches\n");
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printf("%s", sep);
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}
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}
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if (tensor->backend != GGML_BACKEND_CPU)
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free(tensor->data);
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}
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void ggml_tensor_printf(const struct ggml_tensor *tensor, char *prefix, int line, bool extended, bool print_sample) {
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char tmp_str[256] = {0};
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int pos=0;
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const char *sep = "+----------------------+----------------------+----------------------+----------------------+";
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const char *sep_border = "+======================+======================+======================+======================+";
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printf("%s\n", sep_border);
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printf("| %s:%d\n", prefix,line);
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printf("| %-32s [%s type]\n", tensor->name, ggml_type_name(tensor->type));
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printf("%s\n", sep);
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char strides[256] = {0};
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/**
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// nb[0] = sizeof(type)
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// nb[1] = nb[0] * ne[0] + padding
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// nb[i] = nb[i-1] * ne[i-1]
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*/
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{
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strides[0] = '\0';
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for (int i = 0; i < ggml_n_dims(tensor); i++) {
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char dim_str[20];
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snprintf(dim_str, sizeof(dim_str), "%" PRId64, tensor->nb[i]);
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strncat(strides, dim_str, sizeof(strides) - strlen(strides) - 1);
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if (i != ggml_n_dims(tensor) - 1) {
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strncat(strides, "x", sizeof(strides) - strlen(strides) - 1);
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}
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}
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}
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printf("| %-20s | %-20s | %-20s | %-20s |\n", "Dimensions", "Strides", "Layer id", "Backend");
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int layer_id=-1; // tensor->meta structure not available
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printf("| %-20d | %-20s | %-20d | %-20s |\n", ggml_n_dims(tensor), strides, layer_id, tensor->backend == GGML_BACKEND_CPU ? "CPU" : ((tensor->backend == GGML_BACKEND_GPU) ? "GPU" : "GPU_SPLIT"));
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printf("%s\n", sep);
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pos = 0;
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for (int i = 0; i < ggml_n_dims(tensor); i++) {
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pos += snprintf(tmp_str + pos, sizeof(tmp_str) - pos, "%" PRId64, tensor->ne[i]);
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if (i != ggml_n_dims(tensor) - 1) {
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pos += snprintf(tmp_str + pos, sizeof(tmp_str) - pos, " x ");
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}
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}
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printf("| %-20s | %-20s | %-20s | %-20s |\n", "Elements", "Src0", "Src1","Operation");
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printf("| %-20s |", tmp_str);
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if (tensor->src[0]) {
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pos = 0;
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for (int i = 0; i < ggml_n_dims(tensor->src[0]); i++) {
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pos += snprintf(tmp_str + pos, sizeof(tmp_str) - pos, "%" PRId64, tensor->src[0]->ne[i]);
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if (i != ggml_n_dims(tensor->src[0]) - 1) {
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pos += snprintf(tmp_str + pos, sizeof(tmp_str) - pos, " x ");
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}
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}
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printf(" %-20s |", tmp_str);
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} else {
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printf(" %-20s |", "N/A");
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}
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if (tensor->src[1]) {
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pos = 0;
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for (int i = 0; i < ggml_n_dims(tensor->src[1]); i++) {
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pos += snprintf(tmp_str + pos, sizeof(tmp_str) - pos, "%" PRId64, tensor->src[1]->ne[i]);
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if (i != ggml_n_dims(tensor->src[1]) - 1) {
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pos += snprintf(tmp_str + pos, sizeof(tmp_str) - pos, " x ");
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}
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}
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printf(" %-20s |", tmp_str);
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} else {
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printf(" %-20s |", "N/A");
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}
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printf(" %-20s |", tensor->op > 0 ? ggml_op_name(tensor->op) : "N/A");
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printf("\n");
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printf("%s\n", sep);
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if (extended) {
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bool is_transposed = ggml_is_transposed(tensor);
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bool is_permuted = ggml_is_permuted(tensor);
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bool is_cont = ggml_is_contiguous(tensor);
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printf("| %-17s%s | %-17s%s | %-17s%s | %-6s%11.2f MB |\n", "Transposed:", is_transposed ? "Yes" : "No ", "Permuted:", is_permuted ? "Yes" : "No ", "Contiguous:", is_cont ? "Yes" : "No ","Size:", ggml_nbytes(tensor)/(1024.0*1024.0));
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}
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if (extended) {
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if (tensor->src[0] && strlen(tensor->src[0]->name)) {
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printf("| %-20s | ", "Src0 name:");
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printf("%-66s |\n", tensor->src[0]->name);
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}
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if (tensor->src[1] && strlen(tensor->src[1]->name)) {
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printf("| %-20s | ", "Src1 name:");
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printf("%-66s |\n", tensor->src[1]->name);
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}
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printf("%s\n\n", sep);
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}
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if (print_sample) {
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if (extended) {
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if (tensor->src[0] && tensor->src[0]->ne[0]) {
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ggml_printTensorSample("src0", tensor->src[0]);
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}
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if (tensor->src[1] && tensor->src[1]->ne[0]) {
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ggml_printTensorSample("src1", tensor->src[1]);
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}
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}
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ggml_printTensorSample("dst", tensor);
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}
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printf("%s\n", sep_border);
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}
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@ -230,6 +230,10 @@ extern "C" {
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GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
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GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor);
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// Tensor Debug
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// visualize the tensor - extended adds more information - when printing sample content extended will also print src0 and src1 content
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GGML_API void ggml_tensor_printf(const struct ggml_tensor *tensor, char *prefix, int line, bool extended, bool print_sample);
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#ifdef __cplusplus
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}
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