n
/
llama.cpp
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

all : be more strict about converting float to double (#458)

Browse Source 
* 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>
pull/509/head^2 master-436e561
Stephan Walter 1 year ago committed by GitHub
parent 20e1e84884
commit 436e561931
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
11 changed files with 185 additions and 117 deletions
Show Stats Download Patch File Download Diff File

5
CMakeLists.txt
Unescape Escape View File

@ -124,17 +124,18 @@ if (LLAMA_ALL_WARNINGS)
-Wall
-Wextra
-Wpedantic
-Wshadow
-Wcast-qual
-Wdouble-promotion
-Wshadow
-Wstrict-prototypes
-Wpointer-arith
-Wno-unused-function
)
set(cxx_flags
-Wall
-Wextra
-Wpedantic
-Wcast-qual
-Wdouble-promotion
)
else()
# todo : msvc

4
Makefile
Unescape Escape View File

@ -35,6 +35,10 @@ 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)

6
examples/common.cpp
Unescape Escape View File

@ -215,13 +215,13 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stderr, " prompt file to start generation.\n");
fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d, -1 = infinity)\n", params.n_predict);
fprintf(stderr, " --top_k N top-k sampling (default: %d)\n", params.top_k);
fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p);
fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", (double)params.top_p);
fprintf(stderr, " --repeat_last_n N last n tokens to consider for penalize (default: %d)\n", params.repeat_last_n);
fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty);
fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", (double)params.repeat_penalty);
fprintf(stderr, " -c N, --ctx_size N size of the prompt context (default: %d)\n", params.n_ctx);
fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating\n");
fprintf(stderr, " --memory_f32 use f32 instead of f16 for memory key+value\n");
fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
fprintf(stderr, " --temp N temperature (default: %.1f)\n", (double)params.temp);
fprintf(stderr, " --n_parts N number of model parts (default: -1 = determine from dimensions)\n");
fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
fprintf(stderr, " --perplexity compute perplexity over the prompt\n");

11
examples/main/main.cpp
Unescape Escape View File

@ -209,7 +209,8 @@ int main(int argc, char ** argv) {
fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str());
}
}
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);
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);
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");
@ -274,10 +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 float top_k = params.top_k;
const float top_p = params.top_p;
const float temp = params.temp;
const float repeat_penalty = 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;

20
examples/perplexity/perplexity.cpp
Unescape Escape View File

@ -1,15 +1,17 @@
#include "common.h"
#include "llama.h"
std::vector<double> softmax(const std::vector<float>& logits) {
std::vector<double> probs(logits.size());
#include <cmath>
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(logit);
const float logit = logits[i] - max_logit;
const float exp_logit = expf(logit);
sum_exp += exp_logit;
probs[i] = exp_logit;
}
@ -24,14 +26,16 @@ 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 +44,7 @@ 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 +67,7 @@ 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;
}

4
examples/quantize/quantize.cpp
Unescape Escape View File

@ -50,8 +50,8 @@ int main(int argc, char ** argv) {
const int64_t t_main_end_us = ggml_time_us();
printf("\n");
printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0f);
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0);
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0);
}
return 0;

138
ggml.c
Unescape Escape View File

@ -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) {
@ -488,8 +488,8 @@ static void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * r
const float v0 = x[i*QK + l + 0]*id;
const float v1 = x[i*QK + l + 1]*id;
const uint8_t vi0 = ((int8_t) (round(v0))) + 8;
const uint8_t vi1 = ((int8_t) (round(v1))) + 8;
const uint8_t vi0 = (int8_t)roundf(v0) + 8;
const uint8_t vi1 = (int8_t)roundf(v1) + 8;
assert(vi0 >= 0 && vi0 < 16);
assert(vi1 >= 0 && vi1 < 16);
@ -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;
@ -716,8 +716,8 @@ static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int
const float v0 = (x[i*QK + l + 0] - min)*id;
const float v1 = (x[i*QK + l + 1] - min)*id;
const uint8_t vi0 = round(v0);
const uint8_t vi1 = round(v1);
const uint8_t vi0 = roundf(v0);
const uint8_t vi1 = roundf(v1);
assert(vi0 >= 0 && vi0 < 16);
assert(vi1 >= 0 && vi1 < 16);
@ -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
@ -1437,9 +1437,8 @@ inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, co
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]; }
inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) {
ggml_float sumf = 0.0;
#ifdef GGML_SIMD
float sumf = 0.0f;
const int np = (n & ~(GGML_F32_STEP - 1));
GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
@ -1465,8 +1464,9 @@ inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float
}
#else
// scalar
ggml_float sumf = 0.0;
for (int i = 0; i < n; ++i) {
sumf += x[i]*y[i];
sumf += (ggml_float)(x[i]*y[i]);
}
#endif
@ -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();
@ -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
@ -2049,19 +2049,19 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float v) {
#endif
}
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); }
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); }
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]; }
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]); }
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]); }
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]); }
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); }
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; }
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; }
static const ggml_float GELU_COEF_A = 0.044715;
static const ggml_float SQRT_2_OVER_PI = 0.79788456080286535587989211986876;
static const float GELU_COEF_A = 0.044715f;
static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
inline static float ggml_gelu_f32(float x) {
return 0.5*x*(1.0 + tanh(SQRT_2_OVER_PI*x*(1.0 + GELU_COEF_A*x*x)));
return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
}
inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
@ -2090,7 +2090,7 @@ inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) {
// Sigmoid Linear Unit (SiLU) function
inline static float ggml_silu_f32(float x) {
return x/(1.0 + exp(-x));
return x/(1.0f + expf(-x));
}
inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {
@ -2121,7 +2121,7 @@ inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) {
#ifndef GGML_USE_ACCELERATE
ggml_float sum = 0.0;
for (int i = 0; i < n; ++i) {
sum += x[i];
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
Unescape Escape View File

@ -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
Unescape Escape View File

@ -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);

1
tests/CMakeLists.txt
Unescape Escape View File

@ -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
Unescape Escape View 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
}
Write Preview
Loading…
Cancel
Save