mirror of
https://github.com/ggerganov/llama.cpp.git
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all : be more strict about converting float to double (#458)
* Be more strict about converting float to double * Test equivalence of round, SILU implementations Test module is commented out in CMakeLists.txt because the tests may take a long time, depending on how much the compiler optimizes. * Fix softmax in perplexity.cpp * all : prefer float over double where appropriate * perplexity : add <cmath> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Stephan Walter
GitHub
parent
20e1e84884
commit
436e561931
@ -124,17 +124,18 @@ if (LLAMA_ALL_WARNINGS)
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-Wall
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-Wextra
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-Wpedantic
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-Wshadow
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-Wcast-qual
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-Wdouble-promotion
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-Wshadow
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-Wstrict-prototypes
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-Wpointer-arith
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-Wno-unused-function
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)
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set(cxx_flags
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-Wall
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-Wextra
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-Wpedantic
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-Wcast-qual
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-Wdouble-promotion
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)
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else()
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# todo : msvc
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4
Makefile
4
Makefile
@ -35,6 +35,10 @@ CFLAGS = -I. -O3 -DNDEBUG -std=c11 -fPIC
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CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC
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LDFLAGS =
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# warnings
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CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith -Wno-unused-function
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CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function
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# OS specific
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# TODO: support Windows
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ifeq ($(UNAME_S),Linux)
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@ -215,13 +215,13 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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fprintf(stderr, " prompt file to start generation.\n");
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fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d, -1 = infinity)\n", params.n_predict);
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fprintf(stderr, " --top_k N top-k sampling (default: %d)\n", params.top_k);
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fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p);
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fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", (double)params.top_p);
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fprintf(stderr, " --repeat_last_n N last n tokens to consider for penalize (default: %d)\n", params.repeat_last_n);
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fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty);
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fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", (double)params.repeat_penalty);
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fprintf(stderr, " -c N, --ctx_size N size of the prompt context (default: %d)\n", params.n_ctx);
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fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating\n");
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fprintf(stderr, " --memory_f32 use f32 instead of f16 for memory key+value\n");
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fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
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fprintf(stderr, " --temp N temperature (default: %.1f)\n", (double)params.temp);
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fprintf(stderr, " --n_parts N number of model parts (default: -1 = determine from dimensions)\n");
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fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
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fprintf(stderr, " --perplexity compute perplexity over the prompt\n");
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@ -209,7 +209,8 @@ int main(int argc, char ** argv) {
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fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str());
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}
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}
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fprintf(stderr, "sampling: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
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fprintf(stderr, "sampling: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n",
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params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
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fprintf(stderr, "generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
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fprintf(stderr, "\n\n");
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@ -274,7 +275,7 @@ int main(int argc, char ** argv) {
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if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
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// out of user input, sample next token
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const float top_k = params.top_k;
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const int32_t top_k = params.top_k;
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const float top_p = params.top_p;
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const float temp = params.temp;
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const float repeat_penalty = params.repeat_penalty;
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@ -1,15 +1,17 @@
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#include "common.h"
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#include "llama.h"
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std::vector<double> softmax(const std::vector<float>& logits) {
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std::vector<double> probs(logits.size());
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#include <cmath>
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std::vector<float> softmax(const std::vector<float>& logits) {
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std::vector<float> probs(logits.size());
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float max_logit = logits[0];
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for (float v : logits) max_logit = std::max(max_logit, v);
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double sum_exp = 0.0;
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for (size_t i = 0; i < logits.size(); i++) {
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// Subtract the maximum logit value from the current logit value for numerical stability
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float logit = logits[i] - max_logit;
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double exp_logit = std::exp(logit);
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const float logit = logits[i] - max_logit;
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const float exp_logit = expf(logit);
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sum_exp += exp_logit;
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probs[i] = exp_logit;
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}
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@ -24,14 +26,16 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
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auto tokens = ::llama_tokenize(ctx, params.prompt, true);
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int count = 0;
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double nll = 0.0;
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int seq_count = tokens.size() / params.n_ctx;
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double nll = 0.0;
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fprintf(stderr, "%s : calculating perplexity over %d chunks\n", __func__, seq_count);
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for (int i = 0; i < seq_count; ++i) {
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int start = i * params.n_ctx;
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int end = start + params.n_ctx - 1;
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int end = start + params.n_ctx - 1; // TODO: this is not optimal, e.g. it makes the batch 511 instead of 512
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// it is better to always be power of 2 for better performance
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std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end);
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auto start_t = std::chrono::high_resolution_clock::now();
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if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads)) {
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@ -40,7 +44,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
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}
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auto end_t = std::chrono::high_resolution_clock::now();
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if (i == 0) {
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double seconds = std::chrono::duration<double>(end_t - start_t).count();
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const float seconds = std::chrono::duration<float>(end_t - start_t).count();
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printf("%.2f seconds per pass - ETA %.2f hours\n", seconds, (seconds * seq_count) / (60.0*60.0));
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}
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// We get the logits for all the tokens in the context window (params.n_ctx)
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@ -63,7 +67,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
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std::vector<float> tok_logits(
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logits + j * n_vocab,
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logits + (j + 1) * n_vocab);
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double prob = softmax(tok_logits)[tokens[start + j + 1]];
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const float prob = softmax(tok_logits)[tokens[start + j + 1]];
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nll += -std::log(prob);
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++count;
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}
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@ -50,8 +50,8 @@ int main(int argc, char ** argv) {
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const int64_t t_main_end_us = ggml_time_us();
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printf("\n");
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printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0f);
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printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
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printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0);
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printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0);
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}
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return 0;
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138
ggml.c
138
ggml.c
@ -150,10 +150,10 @@ typedef double ggml_float;
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//
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#include <arm_neon.h>
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#define GGML_COMPUTE_FP16_TO_FP32(x) (x)
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#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x))
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#define GGML_COMPUTE_FP32_TO_FP16(x) (x)
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#define GGML_FP16_TO_FP32(x) (x)
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#define GGML_FP16_TO_FP32(x) ((float) (x))
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#define GGML_FP32_TO_FP16(x) (x)
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#else
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@ -322,7 +322,7 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
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// note: do not use these inside ggml.c
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// these are meant to be used via the ggml.h API
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float ggml_fp16_to_fp32(ggml_fp16_t x) {
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return GGML_FP16_TO_FP32(x);
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return (float) GGML_FP16_TO_FP32(x);
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}
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ggml_fp16_t ggml_fp32_to_fp16(float x) {
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@ -488,8 +488,8 @@ static void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * r
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const float v0 = x[i*QK + l + 0]*id;
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const float v1 = x[i*QK + l + 1]*id;
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const uint8_t vi0 = ((int8_t) (round(v0))) + 8;
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const uint8_t vi1 = ((int8_t) (round(v1))) + 8;
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const uint8_t vi0 = (int8_t)roundf(v0) + 8;
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const uint8_t vi1 = (int8_t)roundf(v1) + 8;
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assert(vi0 >= 0 && vi0 < 16);
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assert(vi1 >= 0 && vi1 < 16);
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@ -566,7 +566,7 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int
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MAX(vgetq_lane_f32(amaxv[0], 2), vgetq_lane_f32(amaxv[0], 3)));
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const float d = amax / ((1 << 3) - 1);
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const float id = d ? 1.0/d : 0.0;
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const float id = d ? 1.0f/d : 0.0f;
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y[i].d = d;
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@ -716,8 +716,8 @@ static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int
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const float v0 = (x[i*QK + l + 0] - min)*id;
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const float v1 = (x[i*QK + l + 1] - min)*id;
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const uint8_t vi0 = round(v0);
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const uint8_t vi1 = round(v1);
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const uint8_t vi0 = roundf(v0);
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const uint8_t vi1 = roundf(v1);
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assert(vi0 >= 0 && vi0 < 16);
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assert(vi1 >= 0 && vi1 < 16);
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@ -1001,7 +1001,7 @@ static void dequantize_row_q4_1(const void * restrict vx, float * restrict y, in
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} \
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const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 (x[0])); \
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const float32x4_t t1 = vcvt_f32_f16(vget_high_f16(x[0])); \
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res = vaddvq_f32(vaddq_f32(t0, t1)); \
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res = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1)); \
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}
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#define GGML_F16_VEC GGML_F16x8
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@ -1437,9 +1437,8 @@ inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, co
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inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; }
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inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) {
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ggml_float sumf = 0.0;
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#ifdef GGML_SIMD
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float sumf = 0.0f;
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const int np = (n & ~(GGML_F32_STEP - 1));
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GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
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@ -1465,8 +1464,9 @@ inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float
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}
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#else
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// scalar
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ggml_float sumf = 0.0;
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for (int i = 0; i < n; ++i) {
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sumf += x[i]*y[i];
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sumf += (ggml_float)(x[i]*y[i]);
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}
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#endif
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@ -1529,11 +1529,11 @@ inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t
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// leftovers
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for (int i = np; i < n; ++i) {
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sumf += GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]);
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sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]));
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}
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#else
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for (int i = 0; i < n; ++i) {
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sumf += GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]);
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sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]));
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}
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#endif
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@ -1549,7 +1549,7 @@ inline static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void
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const block_q4_0 * restrict x = vx;
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const block_q4_0 * restrict y = vy;
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float sumf = 0.0;
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ggml_float sumf = 0.0;
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#if defined(__ARM_NEON)
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float sum0 = 0.0f;
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@ -1644,7 +1644,7 @@ inline static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void
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#endif
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}
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sumf = sum0 + sum1;
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sumf = (ggml_float)(sum0 + sum1);
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#elif defined(__AVX512F__)
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// Initialize accumulator with zeros
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__m512 acc0 = _mm512_setzero_ps();
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@ -1972,13 +1972,13 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * re
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// leftovers
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for (int i = np; i < n; ++i) {
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for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
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sumf[j] += GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]);
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sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]));
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}
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}
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#else
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for (int i = 0; i < n; ++i) {
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for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
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sumf[j] += GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]);
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sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]));
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}
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}
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#endif
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@ -2049,19 +2049,19 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float v) {
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#endif
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}
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inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, x, x); *s = sqrt(*s); }
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inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, x, x); *s = sqrtf(*s); }
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inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; }
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inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrt(x[i]); }
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inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); }
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inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); }
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inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); }
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inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; }
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inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; }
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static const ggml_float GELU_COEF_A = 0.044715;
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static const ggml_float SQRT_2_OVER_PI = 0.79788456080286535587989211986876;
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static const float GELU_COEF_A = 0.044715f;
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static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
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inline static float ggml_gelu_f32(float x) {
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return 0.5*x*(1.0 + tanh(SQRT_2_OVER_PI*x*(1.0 + GELU_COEF_A*x*x)));
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return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
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}
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inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
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@ -2090,7 +2090,7 @@ inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) {
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// Sigmoid Linear Unit (SiLU) function
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inline static float ggml_silu_f32(float x) {
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return x/(1.0 + exp(-x));
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return x/(1.0f + expf(-x));
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}
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inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
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@ -2121,7 +2121,7 @@ inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) {
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#ifndef GGML_USE_ACCELERATE
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ggml_float sum = 0.0;
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for (int i = 0; i < n; ++i) {
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sum += x[i];
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||||
sum += (ggml_float)x[i];
|
||||
}
|
||||
*s = sum;
|
||||
#else
|
||||
@ -2131,7 +2131,7 @@ inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) {
|
||||
|
||||
inline static void ggml_vec_max_f32(const int n, float * s, const float * x) {
|
||||
#ifndef GGML_USE_ACCELERATE
|
||||
ggml_float max = -INFINITY;
|
||||
float max = -INFINITY;
|
||||
for (int i = 0; i < n; ++i) {
|
||||
max = MAX(max, x[i]);
|
||||
}
|
||||
@ -2141,7 +2141,10 @@ inline static void ggml_vec_max_f32(const int n, float * s, const float * x) {
|
||||
#endif
|
||||
}
|
||||
|
||||
inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) { ggml_vec_norm_f32(n, s, x); *s = 1./(*s); }
|
||||
inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) {
|
||||
ggml_vec_norm_f32(n, s, x);
|
||||
*s = 1.f/(*s);
|
||||
}
|
||||
|
||||
//
|
||||
// logging
|
||||
@ -2540,7 +2543,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
|
||||
const float f = table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii);
|
||||
table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f));
|
||||
table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f));
|
||||
table_exp_f16[i] = GGML_FP32_TO_FP16(exp(f));
|
||||
table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f));
|
||||
}
|
||||
|
||||
const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
|
||||
@ -5583,7 +5586,7 @@ static void ggml_compute_forward_norm_f32(
|
||||
const size_t nb2 = dst->nb[2];
|
||||
const size_t nb3 = dst->nb[3];
|
||||
|
||||
const ggml_float eps = 1e-5f; // TODO: make this a parameter
|
||||
const float eps = 1e-5f; // TODO: make this a parameter
|
||||
|
||||
// TODO: optimize
|
||||
for (int i03 = 0; i03 < ne03; i03++) {
|
||||
@ -5591,23 +5594,24 @@ static void ggml_compute_forward_norm_f32(
|
||||
for (int i01 = ith; i01 < ne01; i01 += nth) {
|
||||
const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
|
||||
|
||||
ggml_float mean = 0.0;
|
||||
ggml_float sum = 0.0;
|
||||
for (int i00 = 0; i00 < ne00; i00++) {
|
||||
mean += x[i00];
|
||||
sum += (ggml_float)x[i00];
|
||||
}
|
||||
|
||||
mean /= ne00;
|
||||
float mean = sum/ne00;
|
||||
|
||||
float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
|
||||
|
||||
ggml_float sum2 = 0.0;
|
||||
for (int i00 = 0; i00 < ne00; i00++) {
|
||||
ggml_float v = x[i00] - mean;
|
||||
float v = x[i00] - mean;
|
||||
y[i00] = v;
|
||||
sum2 += v*v;
|
||||
sum2 += (ggml_float)(v*v);
|
||||
}
|
||||
|
||||
const float scale = 1.0/sqrt(sum2/ne00 + eps);
|
||||
float variance = sum2/ne00;
|
||||
const float scale = 1.0f/sqrtf(variance + eps);
|
||||
|
||||
ggml_vec_scale_f32(ne00, y, scale);
|
||||
}
|
||||
@ -5665,7 +5669,7 @@ static void ggml_compute_forward_rms_norm_f32(
|
||||
const size_t nb2 = dst->nb[2];
|
||||
const size_t nb3 = dst->nb[3];
|
||||
|
||||
const ggml_float eps = 1e-6f; // TODO: make this a parameter
|
||||
const float eps = 1e-6f; // TODO: make this a parameter
|
||||
|
||||
// TODO: optimize
|
||||
for (int i03 = 0; i03 < ne03; i03++) {
|
||||
@ -5673,12 +5677,12 @@ static void ggml_compute_forward_rms_norm_f32(
|
||||
for (int i01 = ith; i01 < ne01; i01 += nth) {
|
||||
const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
|
||||
|
||||
ggml_float mean = 0.0;
|
||||
ggml_float sum = 0.0;
|
||||
for (int i00 = 0; i00 < ne00; i00++) {
|
||||
mean += x[i00] * x[i00];
|
||||
sum += (ggml_float)(x[i00] * x[i00]);
|
||||
}
|
||||
|
||||
mean /= ne00;
|
||||
float mean = sum/ne00;
|
||||
|
||||
float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
|
||||
|
||||
@ -5687,7 +5691,7 @@ static void ggml_compute_forward_rms_norm_f32(
|
||||
// y[i00] = x[i00];
|
||||
// }
|
||||
|
||||
const float scale = 1.0/sqrt(mean + eps);
|
||||
const float scale = 1.0f/sqrtf(mean + eps);
|
||||
|
||||
ggml_vec_scale_f32(ne00, y, scale);
|
||||
}
|
||||
@ -6913,12 +6917,12 @@ static void ggml_compute_forward_soft_max_f32(
|
||||
ggml_fp16_t s = GGML_FP32_TO_FP16(p[i] - max);
|
||||
memcpy(&scvt, &s, sizeof(scvt));
|
||||
const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
|
||||
sum += val;
|
||||
sum += (ggml_float)val;
|
||||
p[i] = val;
|
||||
}
|
||||
}
|
||||
|
||||
assert(sum > 0.0f);
|
||||
assert(sum > 0.0);
|
||||
|
||||
sum = 1.0/sum;
|
||||
ggml_vec_scale_f32(nc, p, sum);
|
||||
@ -6994,16 +6998,16 @@ static void ggml_compute_forward_rope_f32(
|
||||
const int p = (mode == 0 ? n_past + i2 : i2);
|
||||
for (int i1 = 0; i1 < ne1; i1++) {
|
||||
for (int i0 = 0; i0 < n_dims; i0 += 2) {
|
||||
const double theta = pow(10000.0, ((double)-i0)/n_dims);
|
||||
const float theta = powf(10000.0, ((float)-i0)/n_dims);
|
||||
|
||||
const double cos_theta = cos(p*theta);
|
||||
const double sin_theta = sin(p*theta);
|
||||
const float cos_theta = cosf(p*theta);
|
||||
const float sin_theta = sinf(p*theta);
|
||||
|
||||
const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
||||
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
||||
|
||||
double x0 = src[0];
|
||||
double x1 = src[1];
|
||||
const float x0 = src[0];
|
||||
const float x1 = src[1];
|
||||
|
||||
dst_data[0] = x0*cos_theta - x1*sin_theta;
|
||||
dst_data[1] = x0*sin_theta + x1*cos_theta;
|
||||
@ -7050,16 +7054,16 @@ static void ggml_compute_forward_rope_f16(
|
||||
const int p = (mode == 0 ? n_past + i2 : i2);
|
||||
for (int i1 = 0; i1 < ne1; i1++) {
|
||||
for (int i0 = 0; i0 < n_dims; i0 += 2) {
|
||||
const double theta = pow(10000.0, ((double)-i0)/n_dims);
|
||||
const float theta = powf(10000.0, ((float)-i0)/n_dims);
|
||||
|
||||
const double cos_theta = cos(p*theta);
|
||||
const double sin_theta = sin(p*theta);
|
||||
const float cos_theta = cosf(p*theta);
|
||||
const float sin_theta = sinf(p*theta);
|
||||
|
||||
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
||||
ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
||||
|
||||
double x0 = ggml_fp16_to_fp32(src[0]);
|
||||
double x1 = ggml_fp16_to_fp32(src[1]);
|
||||
const float x0 = ggml_fp16_to_fp32(src[0]);
|
||||
const float x1 = ggml_fp16_to_fp32(src[1]);
|
||||
|
||||
dst_data[0] = ggml_fp32_to_fp16(x0*cos_theta - x1*sin_theta);
|
||||
dst_data[1] = ggml_fp32_to_fp16(x0*sin_theta + x1*cos_theta);
|
||||
@ -7735,7 +7739,7 @@ static void ggml_compute_forward_flash_attn_f32(
|
||||
const int ir0 = dr*ith;
|
||||
const int ir1 = MIN(ir0 + dr, nr);
|
||||
|
||||
const float scale = 1.0/sqrt((double) D);
|
||||
const float scale = 1.0f/sqrtf(D);
|
||||
|
||||
//printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale);
|
||||
|
||||
@ -7782,7 +7786,7 @@ static void ggml_compute_forward_flash_attn_f32(
|
||||
float max = -INFINITY;
|
||||
ggml_vec_max_f32(M, &max, S);
|
||||
|
||||
float sum = 0.0f;
|
||||
ggml_float sum = 0.0;
|
||||
{
|
||||
#ifdef GGML_SOFT_MAX_ACCELERATE
|
||||
max = -max;
|
||||
@ -7803,7 +7807,7 @@ static void ggml_compute_forward_flash_attn_f32(
|
||||
ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max);
|
||||
memcpy(&scvt[j], &s, sizeof(uint16_t));
|
||||
const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
|
||||
sump[j] += val;
|
||||
sump[j] += (ggml_float)val;
|
||||
SS[j] = val;
|
||||
}
|
||||
}
|
||||
@ -7815,7 +7819,7 @@ static void ggml_compute_forward_flash_attn_f32(
|
||||
#endif
|
||||
}
|
||||
|
||||
assert(sum > 0.0f);
|
||||
assert(sum > 0.0);
|
||||
|
||||
sum = 1.0/sum;
|
||||
ggml_vec_scale_f32(M, S, sum);
|
||||
@ -7944,7 +7948,7 @@ static void ggml_compute_forward_flash_attn_f16(
|
||||
const int ir0 = dr*ith;
|
||||
const int ir1 = MIN(ir0 + dr, nr);
|
||||
|
||||
const float scale = 1.0/sqrt((double) D);
|
||||
const float scale = 1.0f/sqrtf(D);
|
||||
|
||||
//printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale);
|
||||
|
||||
@ -8008,7 +8012,7 @@ static void ggml_compute_forward_flash_attn_f16(
|
||||
float max = -INFINITY;
|
||||
ggml_vec_max_f32(M, &max, S);
|
||||
|
||||
float sum = 0.0f;
|
||||
ggml_float sum = 0.0;
|
||||
{
|
||||
#ifdef GGML_SOFT_MAX_ACCELERATE
|
||||
max = -max;
|
||||
@ -8029,7 +8033,7 @@ static void ggml_compute_forward_flash_attn_f16(
|
||||
ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max);
|
||||
memcpy(&scvt[j], &s, sizeof(uint16_t));
|
||||
const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]);
|
||||
sump[j] += val;
|
||||
sump[j] += (ggml_float)val;
|
||||
SS[j] = val;
|
||||
}
|
||||
}
|
||||
@ -8041,7 +8045,7 @@ static void ggml_compute_forward_flash_attn_f16(
|
||||
#endif
|
||||
}
|
||||
|
||||
assert(sum > 0.0f);
|
||||
assert(sum > 0.0);
|
||||
|
||||
sum = 1.0/sum;
|
||||
ggml_vec_scale_f32(M, S, sum);
|
||||
@ -9566,7 +9570,7 @@ label=\"%d [%d, %d] | <x>%s",
|
||||
fprintf(fp, " \"%p\" [ \
|
||||
style = filled; fillcolor = %s; shape = record; \
|
||||
label=\"<x>%.1e\"; ]\n",
|
||||
(void *) node, color, ggml_get_f32_1d(node, 0));
|
||||
(void *) node, color, (double)ggml_get_f32_1d(node, 0));
|
||||
} else {
|
||||
fprintf(fp, " \"%p\" [ \
|
||||
style = filled; fillcolor = %s; shape = record; \
|
||||
@ -9804,7 +9808,7 @@ static enum ggml_opt_result ggml_opt_adam(
|
||||
if (params.past <= t) {
|
||||
const float rate = (pf[t%params.past] - fx)/fx;
|
||||
|
||||
if (fabs(rate) < params.delta) {
|
||||
if (fabsf(rate) < params.delta) {
|
||||
return GGML_OPT_OK;
|
||||
}
|
||||
}
|
||||
@ -9883,7 +9887,7 @@ static enum ggml_opt_result linesearch_backtracking(
|
||||
const float dec = 0.5f;
|
||||
const float inc = 2.1f;
|
||||
|
||||
if (*step <= 0.) {
|
||||
if (*step <= 0.f) {
|
||||
return GGML_LINESEARCH_INVALID_PARAMETERS;
|
||||
}
|
||||
|
||||
@ -9971,7 +9975,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
|
||||
struct ggml_cgraph * gb) {
|
||||
if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE ||
|
||||
params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) {
|
||||
if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1. <= params.lbfgs.wolfe) {
|
||||
if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1.f <= params.lbfgs.wolfe) {
|
||||
return GGML_OPT_INVALID_WOLFE;
|
||||
}
|
||||
}
|
||||
@ -10092,8 +10096,8 @@ static enum ggml_opt_result ggml_opt_lbfgs(
|
||||
|
||||
GGML_PRINT_DEBUG("f = %10.6f\n", ggml_get_f32_1d(f, 0));
|
||||
|
||||
if (xnorm < 1.0) {
|
||||
xnorm = 1.0;
|
||||
if (xnorm < 1.0f) {
|
||||
xnorm = 1.0f;
|
||||
}
|
||||
if (gnorm/xnorm <= params.lbfgs.eps) {
|
||||
// converged
|
||||
@ -10106,7 +10110,7 @@ static enum ggml_opt_result ggml_opt_lbfgs(
|
||||
if (params.past <= k) {
|
||||
const float rate = (pf[k%params.past] - fx)/fx;
|
||||
|
||||
if (fabs(rate) < params.delta) {
|
||||
if (fabsf(rate) < params.delta) {
|
||||
return GGML_OPT_OK;
|
||||
}
|
||||
}
|
||||
|
||||
52
llama.cpp
52
llama.cpp
@ -779,8 +779,8 @@ static bool llama_model_load(
|
||||
|
||||
// progress
|
||||
if (progress_callback) {
|
||||
double current_file_progress = double(size_t(fin.tellg()) - file_offset) / double(file_size - file_offset);
|
||||
double current_progress = (double(i) + current_file_progress) / double(n_parts);
|
||||
float current_file_progress = float(size_t(fin.tellg()) - file_offset) / float(file_size - file_offset);
|
||||
float current_progress = (float(i) + current_file_progress) / float(n_parts);
|
||||
progress_callback(current_progress, progress_callback_user_data);
|
||||
}
|
||||
if (model.n_loaded % 8 == 0) {
|
||||
@ -922,7 +922,7 @@ static bool llama_eval_internal(
|
||||
struct ggml_tensor * KQ_scaled =
|
||||
ggml_scale(ctx0,
|
||||
KQ,
|
||||
ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head)));
|
||||
ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
|
||||
|
||||
// KQ_masked = mask_past(KQ_scaled)
|
||||
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
|
||||
@ -1240,12 +1240,12 @@ static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, co
|
||||
// sampling
|
||||
//
|
||||
|
||||
static void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k) {
|
||||
static void sample_top_k(std::vector<std::pair<float, llama_vocab::id>> & logits_id, int top_k) {
|
||||
// find the top k tokens
|
||||
std::partial_sort(
|
||||
logits_id.begin(),
|
||||
logits_id.begin() + top_k, logits_id.end(),
|
||||
[](const std::pair<double, llama_vocab::id> & a, const std::pair<double, llama_vocab::id> & b) {
|
||||
[](const std::pair<float, llama_vocab::id> & a, const std::pair<float, llama_vocab::id> & b) {
|
||||
return a.first > b.first;
|
||||
});
|
||||
|
||||
@ -1256,9 +1256,9 @@ static llama_vocab::id llama_sample_top_p_top_k(
|
||||
llama_context & lctx,
|
||||
const std::vector<llama_vocab::id> & last_n_tokens,
|
||||
int top_k,
|
||||
double top_p,
|
||||
double temp,
|
||||
double repeat_penalty) {
|
||||
float top_p,
|
||||
float temp,
|
||||
float repeat_penalty) {
|
||||
auto & rng = lctx.rng;
|
||||
|
||||
const int n_logits = lctx.model.hparams.n_vocab;
|
||||
@ -1266,17 +1266,17 @@ static llama_vocab::id llama_sample_top_p_top_k(
|
||||
const auto & logits = lctx.logits;
|
||||
const auto * plogits = logits.data() + logits.size() - n_logits;
|
||||
|
||||
std::vector<std::pair<double, llama_vocab::id>> logits_id;
|
||||
std::vector<std::pair<float, llama_vocab::id>> logits_id;
|
||||
logits_id.reserve(n_logits);
|
||||
|
||||
{
|
||||
const double scale = 1.0/temp;
|
||||
const float scale = 1.0f/temp;
|
||||
for (int i = 0; i < n_logits; ++i) {
|
||||
// repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
|
||||
// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
|
||||
if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
|
||||
// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
|
||||
if (plogits[i] < 0.0) {
|
||||
if (plogits[i] < 0.0f) {
|
||||
logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
|
||||
} else {
|
||||
logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));
|
||||
@ -1289,18 +1289,18 @@ static llama_vocab::id llama_sample_top_p_top_k(
|
||||
|
||||
sample_top_k(logits_id, top_k);
|
||||
|
||||
double maxl = -std::numeric_limits<double>::infinity();
|
||||
float maxl = -std::numeric_limits<float>::infinity();
|
||||
for (const auto & kv : logits_id) {
|
||||
maxl = std::max(maxl, kv.first);
|
||||
}
|
||||
|
||||
// compute probs for the top k tokens
|
||||
std::vector<double> probs;
|
||||
std::vector<float> probs;
|
||||
probs.reserve(logits_id.size());
|
||||
|
||||
double sum = 0.0;
|
||||
for (const auto & kv : logits_id) {
|
||||
double p = exp(kv.first - maxl);
|
||||
const float p = expf(kv.first - maxl);
|
||||
probs.push_back(p);
|
||||
sum += p;
|
||||
}
|
||||
@ -1310,8 +1310,8 @@ static llama_vocab::id llama_sample_top_p_top_k(
|
||||
p /= sum;
|
||||
}
|
||||
|
||||
if (top_p < 1.0f) {
|
||||
double cumsum = 0.0f;
|
||||
if (top_p < 1.0) {
|
||||
double cumsum = 0.0;
|
||||
for (int i = 0; i < (int) probs.size(); i++) {
|
||||
cumsum += probs[i];
|
||||
if (cumsum >= top_p) {
|
||||
@ -1590,7 +1590,7 @@ static bool llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
}
|
||||
|
||||
for (int i = 0; i < (int) hist_cur.size(); ++i) {
|
||||
printf("%5.3f ", hist_cur[i] / (float)nelements);
|
||||
printf("%5.3f ", hist_cur[i] / float(nelements));
|
||||
}
|
||||
printf("\n");
|
||||
} else {
|
||||
@ -1613,7 +1613,7 @@ static bool llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
|
||||
printf("%s: hist: ", __func__);
|
||||
for (int i = 0; i < (int) hist_all.size(); ++i) {
|
||||
printf("%5.3f ", hist_all[i] / (float)sum_all);
|
||||
printf("%5.3f ", hist_all[i] / float(sum_all));
|
||||
}
|
||||
printf("\n");
|
||||
}
|
||||
@ -1795,9 +1795,9 @@ llama_token llama_sample_top_p_top_k(
|
||||
const llama_token * last_n_tokens_data,
|
||||
int last_n_tokens_size,
|
||||
int top_k,
|
||||
double top_p,
|
||||
double temp,
|
||||
double repeat_penalty) {
|
||||
float top_p,
|
||||
float temp,
|
||||
float repeat_penalty) {
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
llama_token result = 0;
|
||||
@ -1828,11 +1828,11 @@ void llama_print_timings(struct llama_context * ctx) {
|
||||
const int32_t n_p_eval = std::max(1, ctx->n_p_eval);
|
||||
|
||||
fprintf(stderr, "\n");
|
||||
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0f);
|
||||
fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->t_sample_us, n_sample, 1e-3f * ctx->t_sample_us / n_sample);
|
||||
fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token)\n", __func__, 1e-3f * ctx->t_p_eval_us, n_p_eval, 1e-3f * ctx->t_p_eval_us / n_p_eval);
|
||||
fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->t_eval_us, n_eval, 1e-3f * ctx->t_eval_us / n_eval);
|
||||
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f);
|
||||
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0);
|
||||
fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3 * ctx->t_sample_us, n_sample, 1e-3 * ctx->t_sample_us / n_sample);
|
||||
fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token)\n", __func__, 1e-3 * ctx->t_p_eval_us, n_p_eval, 1e-3 * ctx->t_p_eval_us / n_p_eval);
|
||||
fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3 * ctx->t_eval_us, n_eval, 1e-3 * ctx->t_eval_us / n_eval);
|
||||
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0);
|
||||
}
|
||||
|
||||
void llama_reset_timings(struct llama_context * ctx) {
|
||||
|
||||
8
llama.h
8
llama.h
@ -45,7 +45,7 @@ extern "C" {
|
||||
|
||||
} llama_token_data;
|
||||
|
||||
typedef void (*llama_progress_callback)(double progress, void *ctx);
|
||||
typedef void (*llama_progress_callback)(float progress, void *ctx);
|
||||
|
||||
struct llama_context_params {
|
||||
int n_ctx; // text context
|
||||
@ -134,9 +134,9 @@ extern "C" {
|
||||
const llama_token * last_n_tokens_data,
|
||||
int last_n_tokens_size,
|
||||
int top_k,
|
||||
double top_p,
|
||||
double temp,
|
||||
double repeat_penalty);
|
||||
float top_p,
|
||||
float temp,
|
||||
float repeat_penalty);
|
||||
|
||||
// Performance information
|
||||
LLAMA_API void llama_print_timings(struct llama_context * ctx);
|
||||
|
||||
@ -5,5 +5,6 @@ function(llama_add_test source)
|
||||
add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${ARGN})
|
||||
endfunction()
|
||||
|
||||
# llama_add_test(test-double-float.c) # SLOW
|
||||
llama_add_test(test-quantize.c)
|
||||
llama_add_test(test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab.bin)
|
||||
|
||||
53
tests/test-double-float.c
Normal file
53
tests/test-double-float.c
Normal file
@ -0,0 +1,53 @@
|
||||
// These tests may take a long time!
|
||||
// They are to prove that conversion from double to float of various functions in ggml.c doesn't affect the result.
|
||||
// This is done by checking all finite (non-NaN, non-infinite) floats.
|
||||
|
||||
#undef NDEBUG
|
||||
#include <assert.h>
|
||||
#include <immintrin.h>
|
||||
#include <math.h>
|
||||
#include <stdint.h>
|
||||
|
||||
#pragma GCC diagnostic push
|
||||
#pragma GCC diagnostic ignored "-Wdouble-promotion"
|
||||
|
||||
// ggml.c::quantize_row_q4_0_reference
|
||||
inline static uint8_t round_orig(float v0) { return ((int8_t) (round(v0))) + 8; }
|
||||
|
||||
// ggml.c::ggml_silu_f32
|
||||
inline static float silu_orig(float x) {
|
||||
return x/(1.0 + exp(-x));
|
||||
}
|
||||
|
||||
#pragma GCC diagnostic pop
|
||||
|
||||
// ggml.c::quantize_row_q4_0_reference
|
||||
inline static uint8_t round_float(float v0) { return (int8_t)roundf(v0) + 8; }
|
||||
|
||||
// ggml.c::ggml_silu_f32
|
||||
inline static float silu_float(float x) {
|
||||
return x/(1.0f + expf(-x));
|
||||
}
|
||||
|
||||
int main(void) {
|
||||
uint32_t x = UINT32_MAX;
|
||||
do {
|
||||
float f = *(float *)&x;
|
||||
assert(!isfinite(f) || (round_orig(f) == round_float(f)));
|
||||
} while (x--);
|
||||
|
||||
#ifdef __F16C__
|
||||
// GELU and SILU implementations are used with a FP16 lookup table.
|
||||
// The original and float-only results are not equal for all inputs after converting to FP16.
|
||||
// GELU is an approximation anyway (tanh), not tested here.
|
||||
// For SILU, verify that the results are at least the closest floating point numbers, if the FP16 values don't match.
|
||||
for (x = 0; x <= UINT16_MAX; x++) {
|
||||
float f = _cvtsh_ss(x);
|
||||
const float so = silu_orig(f);
|
||||
const float sf = silu_float(f);
|
||||
assert( (_cvtss_sh(so, 0) == _cvtss_sh(sf, 0))
|
||||
|| (nextafterf(so, sf) == sf)
|
||||
|| (nextafterf(sf, so) == so));
|
||||
}
|
||||
#endif
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user