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https://github.com/ggerganov/llama.cpp.git
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minor : clean-up some warnings and style (#5094)
* minor : clean-up some warnings and style ggml-ci * ggml : add comment
This commit is contained in:
Georgi Gerganov
GitHub
parent
2bed4aa3f3
commit
89758723c7
@ -216,12 +216,10 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
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}
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// store the external file name in params
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params.prompt_file = argv[i];
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file.seekg(0, std::ios::end);
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size_t size = file.tellg();
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file.seekg(0, std::ios::beg);
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params.prompt.resize(size);
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file.read((char *)params.prompt.data(), size);
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fprintf(stderr, "Read %zu bytes from binary file %s\n", size, argv[i]);
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std::ostringstream ss;
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ss << file.rdbuf();
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params.prompt = ss.str();
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fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]);
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} else if (arg == "-f" || arg == "--file") {
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if (++i >= argc) {
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invalid_param = true;
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@ -2,18 +2,6 @@
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// so there might be still unnecessary artifacts hanging around
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// I'll gradually clean and extend it
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#include <cassert>
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#include <cmath>
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#include <cstdlib>
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#include <cstring>
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#include <fstream>
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#include <iostream>
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#include <map>
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#include <regex>
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#include <stdexcept>
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#include <vector>
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#include <sstream>
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#include "clip.h"
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#include "ggml.h"
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#include "ggml-alloc.h"
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@ -30,6 +18,19 @@
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#define STB_IMAGE_IMPLEMENTATION
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#include "stb_image.h"
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#include <cassert>
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#include <cmath>
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#include <cstdlib>
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#include <cstring>
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#include <fstream>
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#include <iostream>
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#include <map>
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#include <regex>
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#include <stdexcept>
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#include <vector>
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#include <sstream>
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#include <cinttypes>
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static std::string format(const char * fmt, ...) {
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va_list ap;
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va_list ap2;
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@ -217,9 +218,9 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
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static void print_tensor_info(const ggml_tensor* tensor, const char* prefix = "") {
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size_t tensor_size = ggml_nbytes(tensor);
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printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%d, %d, %d, %d], type: %d\n",
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printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
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prefix, ggml_n_dims(tensor), tensor->name, tensor_size,
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tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->type);
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tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type));
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}
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static projector_type clip_projector_type_from_string(const std::string & name) {
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@ -592,7 +593,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
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mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3));
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mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]);
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// stride = 1, padding = 1, bias is nullptr
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block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, nullptr, 1, 1, 1, 1, 1, 1);
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block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);
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// layer norm
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// // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
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@ -640,7 +641,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
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// block_2
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{
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// stride = 2
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block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, nullptr, 2, 2, 1, 1, 1, 1);
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block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1);
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// block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
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// layer norm
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@ -741,18 +742,10 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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{
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std::map<enum ggml_type, uint32_t> n_type;
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uint32_t n_type_max = 0;
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enum ggml_type type_max = GGML_TYPE_F32;
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for (int i = 0; i < n_tensors; i++) {
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enum ggml_type type = gguf_get_tensor_type(ctx, i);
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n_type[type]++;
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if (n_type_max < n_type[type]) {
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n_type_max = n_type[type];
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type_max = type;
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}
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}
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printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
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@ -795,14 +788,12 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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size_t tensor_size = ggml_nbytes(cur);
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buffer_size += tensor_size;
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if (verbosity >= 3) {
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printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%d, %d, %d, %d], type: %d\n", __func__, i,
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ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], type);
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printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
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__func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type));
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}
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}
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}
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buffer_size += n_tensors * 128 /* CLIP PADDING */;
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clip_ctx * new_clip = new clip_ctx;
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@ -1206,7 +1206,7 @@ static bool deserialize_string(std::istream& in, std::string& str) {
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uint32_t size;
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if (!in.read((char *)&size, sizeof(size)).fail()) {
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str.resize(size);
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if (!in.read((char *)str.data(), size).fail()) return true;
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if (!in.read((char *)&str[0], size).fail()) return true;
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}
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return false;
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}
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9
ggml.c
9
ggml.c
@ -5368,14 +5368,12 @@ struct ggml_tensor * ggml_conv_depthwise_2d(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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struct ggml_tensor * c,
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int s0,
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int s1,
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int p0,
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int p1,
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int d0,
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int d1) {
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struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]);
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struct ggml_tensor * im2col = ggml_im2col(ctx, new_a,
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ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]),
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@ -9991,7 +9989,7 @@ static void ggml_compute_forward_mul_mat(
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return;
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}
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const int64_t tgemm0 = ggml_perf_time_us();
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//const int64_t tgemm0 = ggml_perf_time_us();
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for (int64_t i13 = 0; i13 < ne13; i13++) {
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for (int64_t i12 = 0; i12 < ne12; i12++) {
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const int64_t i03 = i13/r3;
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@ -16934,7 +16932,10 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
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if (ggml_compute_forward_mul_mat_use_blas(node)) {
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if (node->src[0]->type != GGML_TYPE_F32) {
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// here we need memory for fully dequantized matrix from src0
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cur = ggml_type_size(GGML_TYPE_F32)*ggml_nelements(node->src[0]);
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// take into account that src0 can be broadcasted into src1[2,3]
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cur = ggml_type_size(GGML_TYPE_F32)
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* node->src[0]->ne[0]*node->src[0]->ne[1]
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* node->src[1]->ne[2]*node->src[1]->ne[3];
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}
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} else
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#endif
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