all : prefer float over double where appropriate
This commit is contained in:
Georgi Gerganov
parent
f68345e9b1
commit
61733d3b49
7 changed files with 69 additions and 65 deletions
10
Makefile
10
Makefile
|
|
@ -31,12 +31,14 @@ endif
|
|||
#
|
||||
|
||||
# keep standard at C11 and C++11
|
||||
CFLAGS = -I. -O3 -DNDEBUG -std=c11 -fPIC \
|
||||
-Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith
|
||||
CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC \
|
||||
-Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion
|
||||
CFLAGS = -I. -O3 -DNDEBUG -std=c11 -fPIC
|
||||
CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC
|
||||
LDFLAGS =
|
||||
|
||||
# warnings
|
||||
CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith -Wno-unused-function
|
||||
CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function
|
||||
|
||||
# OS specific
|
||||
# TODO: support Windows
|
||||
ifeq ($(UNAME_S),Linux)
|
||||
|
|
|
|||
|
|
@ -89,7 +89,7 @@ int main(int argc, char ** argv) {
|
|||
const auto embeddings = llama_get_embeddings(ctx);
|
||||
|
||||
for (int i = 0; i < n_embd; i++) {
|
||||
printf("%f ", (double)embeddings[i]);
|
||||
printf("%f ", embeddings[i]);
|
||||
}
|
||||
printf("\n");
|
||||
}
|
||||
|
|
|
|||
|
|
@ -210,7 +210,7 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
fprintf(stderr, "sampling: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n",
|
||||
(double)params.temp, params.top_k, (double)params.top_p, params.repeat_last_n, (double)params.repeat_penalty);
|
||||
params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
|
||||
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);
|
||||
fprintf(stderr, "\n\n");
|
||||
|
||||
|
|
@ -275,10 +275,10 @@ int main(int argc, char ** argv) {
|
|||
|
||||
if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
|
||||
// out of user input, sample next token
|
||||
const int top_k = params.top_k;
|
||||
const double top_p = (double)params.top_p;
|
||||
const double temp = (double)params.temp;
|
||||
const double repeat_penalty = (double)params.repeat_penalty;
|
||||
const int32_t top_k = params.top_k;
|
||||
const float top_p = params.top_p;
|
||||
const float temp = params.temp;
|
||||
const float repeat_penalty = params.repeat_penalty;
|
||||
|
||||
llama_token id = 0;
|
||||
|
||||
|
|
|
|||
|
|
@ -1,15 +1,15 @@
|
|||
#include "common.h"
|
||||
#include "llama.h"
|
||||
|
||||
std::vector<double> softmax(const std::vector<float>& logits) {
|
||||
std::vector<double> probs(logits.size());
|
||||
std::vector<float> softmax(const std::vector<float>& logits) {
|
||||
std::vector<float> probs(logits.size());
|
||||
float max_logit = logits[0];
|
||||
for (float v : logits) max_logit = std::max(max_logit, v);
|
||||
double sum_exp = 0.0;
|
||||
for (size_t i = 0; i < logits.size(); i++) {
|
||||
// Subtract the maximum logit value from the current logit value for numerical stability
|
||||
float logit = logits[i] - max_logit;
|
||||
double exp_logit = std::exp((double)logit);
|
||||
const float logit = logits[i] - max_logit;
|
||||
const float exp_logit = std::expf(logit);
|
||||
sum_exp += exp_logit;
|
||||
probs[i] = exp_logit;
|
||||
}
|
||||
|
|
@ -24,14 +24,16 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
|
|||
auto tokens = ::llama_tokenize(ctx, params.prompt, true);
|
||||
|
||||
int count = 0;
|
||||
double nll = 0.0;
|
||||
int seq_count = tokens.size() / params.n_ctx;
|
||||
|
||||
double nll = 0.0;
|
||||
|
||||
fprintf(stderr, "%s : calculating perplexity over %d chunks\n", __func__, seq_count);
|
||||
|
||||
for (int i = 0; i < seq_count; ++i) {
|
||||
int start = i * params.n_ctx;
|
||||
int end = start + params.n_ctx - 1;
|
||||
int end = start + params.n_ctx - 1; // TODO: this is not optimal, e.g. it makes the batch 511 instead of 512
|
||||
// it is better to always be power of 2 for better performance
|
||||
std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end);
|
||||
auto start_t = std::chrono::high_resolution_clock::now();
|
||||
if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads)) {
|
||||
|
|
@ -40,7 +42,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
|
|||
}
|
||||
auto end_t = std::chrono::high_resolution_clock::now();
|
||||
if (i == 0) {
|
||||
double seconds = std::chrono::duration<double>(end_t - start_t).count();
|
||||
const float seconds = std::chrono::duration<float>(end_t - start_t).count();
|
||||
printf("%.2f seconds per pass - ETA %.2f hours\n", seconds, (seconds * seq_count) / (60.0*60.0));
|
||||
}
|
||||
// We get the logits for all the tokens in the context window (params.n_ctx)
|
||||
|
|
@ -63,7 +65,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
|
|||
std::vector<float> tok_logits(
|
||||
logits + j * n_vocab,
|
||||
logits + (j + 1) * n_vocab);
|
||||
double prob = softmax(tok_logits)[tokens[start + j + 1]];
|
||||
const float prob = softmax(tok_logits)[tokens[start + j + 1]];
|
||||
nll += -std::log(prob);
|
||||
++count;
|
||||
}
|
||||
|
|
|
|||
46
ggml.c
46
ggml.c
|
|
@ -150,10 +150,10 @@ typedef double ggml_float;
|
|||
//
|
||||
#include <arm_neon.h>
|
||||
|
||||
#define GGML_COMPUTE_FP16_TO_FP32(x) (x)
|
||||
#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x))
|
||||
#define GGML_COMPUTE_FP32_TO_FP16(x) (x)
|
||||
|
||||
#define GGML_FP16_TO_FP32(x) (x)
|
||||
#define GGML_FP16_TO_FP32(x) ((float) (x))
|
||||
#define GGML_FP32_TO_FP16(x) (x)
|
||||
|
||||
#else
|
||||
|
|
@ -322,7 +322,7 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
|
|||
// note: do not use these inside ggml.c
|
||||
// these are meant to be used via the ggml.h API
|
||||
float ggml_fp16_to_fp32(ggml_fp16_t x) {
|
||||
return GGML_FP16_TO_FP32(x);
|
||||
return (float) GGML_FP16_TO_FP32(x);
|
||||
}
|
||||
|
||||
ggml_fp16_t ggml_fp32_to_fp16(float x) {
|
||||
|
|
@ -566,7 +566,7 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int
|
|||
MAX(vgetq_lane_f32(amaxv[0], 2), vgetq_lane_f32(amaxv[0], 3)));
|
||||
|
||||
const float d = amax / ((1 << 3) - 1);
|
||||
const float id = d ? 1.0/d : 0.0;
|
||||
const float id = d ? 1.0f/d : 0.0f;
|
||||
|
||||
y[i].d = d;
|
||||
|
||||
|
|
@ -1001,7 +1001,7 @@ static void dequantize_row_q4_1(const void * restrict vx, float * restrict y, in
|
|||
} \
|
||||
const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 (x[0])); \
|
||||
const float32x4_t t1 = vcvt_f32_f16(vget_high_f16(x[0])); \
|
||||
res = vaddvq_f32(vaddq_f32(t0, t1)); \
|
||||
res = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1)); \
|
||||
}
|
||||
|
||||
#define GGML_F16_VEC GGML_F16x8
|
||||
|
|
@ -1505,7 +1505,7 @@ static inline __m512 dot_q4_0_oneblock_avx512(
|
|||
#endif
|
||||
|
||||
inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) {
|
||||
float sumf = 0.0f;
|
||||
ggml_float sumf = 0.0;
|
||||
|
||||
#if defined(GGML_SIMD)
|
||||
const int np = (n & ~(GGML_F16_STEP - 1));
|
||||
|
|
@ -1529,11 +1529,11 @@ inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t
|
|||
|
||||
// leftovers
|
||||
for (int i = np; i < n; ++i) {
|
||||
sumf += GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]);
|
||||
sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]));
|
||||
}
|
||||
#else
|
||||
for (int i = 0; i < n; ++i) {
|
||||
sumf += GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]);
|
||||
sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]));
|
||||
}
|
||||
#endif
|
||||
|
||||
|
|
@ -1549,7 +1549,7 @@ inline static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void
|
|||
const block_q4_0 * restrict x = vx;
|
||||
const block_q4_0 * restrict y = vy;
|
||||
|
||||
float sumf = 0.0;
|
||||
ggml_float sumf = 0.0;
|
||||
|
||||
#if defined(__ARM_NEON)
|
||||
float sum0 = 0.0f;
|
||||
|
|
@ -1644,7 +1644,7 @@ inline static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void
|
|||
#endif
|
||||
}
|
||||
|
||||
sumf = sum0 + sum1;
|
||||
sumf = (ggml_float)(sum0 + sum1);
|
||||
#elif defined(__AVX512F__)
|
||||
// Initialize accumulator with zeros
|
||||
__m512 acc0 = _mm512_setzero_ps();
|
||||
|
|
@ -1936,7 +1936,7 @@ inline static void ggml_vec_dot_q4_1(const int n, float * restrict s, const void
|
|||
// compute GGML_VEC_DOT_UNROLL dot products at once
|
||||
// xs - x row stride in bytes
|
||||
inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv, ggml_fp16_t * restrict y) {
|
||||
float sumf[GGML_VEC_DOT_UNROLL] = { 0.0f };
|
||||
ggml_float sumf[GGML_VEC_DOT_UNROLL] = { 0.0 };
|
||||
|
||||
ggml_fp16_t * restrict x[GGML_VEC_DOT_UNROLL];
|
||||
|
||||
|
|
@ -1972,13 +1972,13 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * re
|
|||
// leftovers
|
||||
for (int i = np; i < n; ++i) {
|
||||
for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
|
||||
sumf[j] += GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]);
|
||||
sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]));
|
||||
}
|
||||
}
|
||||
#else
|
||||
for (int i = 0; i < n; ++i) {
|
||||
for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
|
||||
sumf[j] += GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]);
|
||||
sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]));
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
|
@ -6998,16 +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 = (double)src[0];
|
||||
double x1 = (double)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;
|
||||
|
|
@ -7054,16 +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 float cos_theta = cos(p*theta);
|
||||
const float 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);
|
||||
|
||||
float x0 = ggml_fp16_to_fp32(src[0]);
|
||||
float 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);
|
||||
|
|
|
|||
40
llama.cpp
40
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) {
|
||||
|
|
@ -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;
|
||||
|
|
@ -1266,41 +1266,41 @@ 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] < 0.0f) {
|
||||
logits_id.push_back(std::make_pair((double)plogits[i]*scale*repeat_penalty, i));
|
||||
logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
|
||||
} else {
|
||||
logits_id.push_back(std::make_pair((double)plogits[i]*scale/repeat_penalty, i));
|
||||
logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));
|
||||
}
|
||||
} else {
|
||||
logits_id.push_back(std::make_pair((double)plogits[i]*scale, i));
|
||||
logits_id.push_back(std::make_pair(plogits[i]*scale, i));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
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;
|
||||
}
|
||||
|
|
@ -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] / (double)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] / (double)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;
|
||||
|
|
|
|||
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);
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue