moved from ggml to ggml-backend - as backend retrieval needed but header not available in ggml.c
This commit is contained in:
John
GitHub
parent
31bfd4a52b
commit
0fa71d1760
4 changed files with 206 additions and 231 deletions
202
ggml-backend.c
202
ggml-backend.c
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@ -1722,3 +1722,205 @@ 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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void ggml_printTensorSample(const char *prefix, const struct ggml_tensor * tensor) {
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char *tensor_data;
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if (tensor->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->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));
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ggml_backend_tensor_get(tensor, tensor_data, 0, ggml_nbytes(tensor));
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} else
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{
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tensor_data = tensor->data;
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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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printf("%s| Content of %s \"%s\" (%d dim)\n", sep, prefix, tensor->name, ggml_n_dims(tensor));
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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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printf("%-7.4f, ", *(double *)((char *) tensor_data + i*tensor->nb[0]));
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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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printf("%-7.4f ", *(double *)((char *) tensor_data + i*tensor->nb[0] + j*tensor->nb[1]));
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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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printf("%-7.4f", *(double *)((char *) tensor_data + i*tensor->nb[0] + j*tensor->nb[1] + k*tensor->nb[2]));
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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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printf("%-7.4f", *(double *)((char *) tensor_data + batch*tensor->nb[0] + i*tensor->nb[1] + j*tensor->nb[2] + k*tensor->nb[3]));
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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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@ -203,6 +203,10 @@ 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(ggml_backend_buffer_t buffer, 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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227
ggml.c
227
ggml.c
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@ -20158,233 +20158,6 @@ void gguf_get_meta_data(const struct gguf_context * ctx, void * data) {
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////////////////////////////////////////////////////////////////////////////////
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void ggml_printTensorSample(const char *prefix, const struct ggml_tensor * tensor) {
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char *tensor_data;
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if (tensor->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->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));
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ggml_backend_tensor_get(tensor, tensor_data, 0, ggml_nbytes(tensor));
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} else
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{
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tensor_data = tensor->data;
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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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printf("%s| Content of %s \"%s\" (%d dim)\n", sep, prefix, tensor->name, ggml_n_dims(tensor));
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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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printf("%-7.4f, ", *(double *)((char *) tensor_data + i*tensor->nb[0]));
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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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printf("%-7.4f ", *(double *)((char *) tensor_data + i*tensor->nb[0] + j*tensor->nb[1]));
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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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printf("%-7.4f", *(double *)((char *) tensor_data + i*tensor->nb[0] + j*tensor->nb[1] + k*tensor->nb[2]));
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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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printf("%-7.4f", *(double *)((char *) tensor_data + batch*tensor->nb[0] + i*tensor->nb[1] + j*tensor->nb[2] + k*tensor->nb[3]));
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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);
|
||||
|
||||
if (tensor->src[0]) {
|
||||
pos = 0;
|
||||
for (int i = 0; i < ggml_n_dims(tensor->src[0]); i++) {
|
||||
pos += snprintf(tmp_str + pos, sizeof(tmp_str) - pos, "%" PRId64, tensor->src[0]->ne[i]);
|
||||
if (i != ggml_n_dims(tensor->src[0]) - 1) {
|
||||
pos += snprintf(tmp_str + pos, sizeof(tmp_str) - pos, " x ");
|
||||
}
|
||||
}
|
||||
printf(" %-20s |", tmp_str);
|
||||
} else {
|
||||
printf(" %-20s |", "N/A");
|
||||
}
|
||||
if (tensor->src[1]) {
|
||||
pos = 0;
|
||||
for (int i = 0; i < ggml_n_dims(tensor->src[1]); i++) {
|
||||
pos += snprintf(tmp_str + pos, sizeof(tmp_str) - pos, "%" PRId64, tensor->src[1]->ne[i]);
|
||||
if (i != ggml_n_dims(tensor->src[1]) - 1) {
|
||||
pos += snprintf(tmp_str + pos, sizeof(tmp_str) - pos, " x ");
|
||||
}
|
||||
}
|
||||
printf(" %-20s |", tmp_str);
|
||||
} else {
|
||||
printf(" %-20s |", "N/A");
|
||||
}
|
||||
printf(" %-20s |", tensor->op > 0 ? GGML_OP_NAME[tensor->op] : "N/A");
|
||||
printf("\n");
|
||||
printf("%s\n", sep);
|
||||
|
||||
if (extended) {
|
||||
bool is_transposed = ggml_is_transposed(tensor);
|
||||
bool is_permuted = ggml_is_permuted(tensor);
|
||||
bool is_cont = ggml_is_contiguous(tensor);
|
||||
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));
|
||||
}
|
||||
|
||||
if (extended) {
|
||||
if (tensor->src[0] && strlen(tensor->src[0]->name)) {
|
||||
printf("| %-20s | ", "Src0 name:");
|
||||
printf("%-66s |\n", tensor->src[0]->name);
|
||||
}
|
||||
if (tensor->src[1] && strlen(tensor->src[1]->name)) {
|
||||
printf("| %-20s | ", "Src1 name:");
|
||||
printf("%-66s |\n", tensor->src[1]->name);
|
||||
}
|
||||
printf("%s\n\n", sep);
|
||||
}
|
||||
|
||||
if (print_sample) {
|
||||
if (extended) {
|
||||
if (tensor->src[0] && tensor->src[0]->ne[0]) {
|
||||
ggml_printTensorSample("src0", tensor->src[0]);
|
||||
}
|
||||
if (tensor->src[1] && tensor->src[1]->ne[0]) {
|
||||
ggml_printTensorSample("src1", tensor->src[1]);
|
||||
}
|
||||
}
|
||||
ggml_printTensorSample("dst", tensor);
|
||||
}
|
||||
printf("%s\n", sep_border);
|
||||
}
|
||||
|
||||
float ggml_get_tensor_index(const struct ggml_tensor* tensor, int ind1, int ind2, int ind3, int ind4) {
|
||||
int n_dims = ggml_n_dims(tensor);
|
||||
if (n_dims < 1 || n_dims > 4) {
|
||||
printf("Error: Incorrect dimension number %d\n", n_dims);
|
||||
return -1; // handle error
|
||||
}
|
||||
|
||||
int indices[4] = {ind1, ind2, ind3, ind4};
|
||||
int total_offset = 0;
|
||||
|
||||
for (int i = 0; i < n_dims; i++) {
|
||||
if (indices[i] >= tensor->ne[i] || indices[i] < 0) {
|
||||
printf("Error: Incorrect index for dimension %d\n", i);
|
||||
printf("Index: %d, Dimension size: %" PRId64 "\n", indices[i], tensor->ne[i]);
|
||||
return -1; // handle error
|
||||
}
|
||||
|
||||
total_offset += indices[i] * tensor->nb[i];
|
||||
}
|
||||
|
||||
// Return the value at the calculated offset
|
||||
return *(float *)((char *) tensor->data + total_offset);
|
||||
}
|
||||
|
||||
////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
int ggml_cpu_has_avx(void) {
|
||||
#if defined(__AVX__)
|
||||
return 1;
|
||||
|
|
|
|||
4
ggml.h
4
ggml.h
|
|
@ -1899,10 +1899,6 @@ extern "C" {
|
|||
// dump the graph into a file using the dot format
|
||||
GGML_API void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename);
|
||||
|
||||
// visualize the tensor - extended adds more information - when printing sample content extended will also print src0 and src1 content
|
||||
// example: ggml_tensor_printf(some_ggml_tensor,"function_name",0,true,true);
|
||||
void ggml_tensor_printf(const struct ggml_tensor *tensor, char *prefix, int line, bool extended, bool print_sample);
|
||||
|
||||
// build gradient checkpointing backward graph gb for gf using provided checkpoints
|
||||
// gb_tmp will contain original backward graph with rewritten backward process nodes,
|
||||
// but without the second forward pass nodes.
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue