Merge 'origin/master' into clfixes

This commit is contained in:
Henri Vasserman 2023-05-19 00:36:03 +03:00
commit 558c672c93
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GPG key ID: 2995FC0F58B1A986
13 changed files with 55 additions and 46 deletions

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@ -115,7 +115,7 @@ ifndef LLAMA_NO_ACCELERATE
endif
endif
ifdef LLAMA_OPENBLAS
CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas
CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas -I/usr/include/openblas
ifneq ($(shell grep -e "Arch Linux" -e "ID_LIKE=arch" /etc/os-release 2>/dev/null),)
LDFLAGS += -lopenblas -lcblas
else

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@ -121,7 +121,6 @@ def make_tensors_list() -> List[str]:
f'layers.{i}.feed_forward.w1.weight',
f'layers.{i}.feed_forward.w2.weight',
f'layers.{i}.feed_forward.w3.weight',
f'layers.{i}.atttention_norm.weight',
f'layers.{i}.ffn_norm.weight',
]
return ret
@ -1055,7 +1054,7 @@ def load_some_model(path: Path) -> ModelPlus:
files = list(path.glob("model-00001-of-*.safetensors"))
if not files:
# Try the PyTorch patterns too, with lower priority
globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt"]
globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin" ]
files = [file for glob in globs for file in path.glob(glob)]
if not files:
# Try GGML too, but with lower priority, since if both a non-GGML

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@ -211,6 +211,7 @@ int main(int argc, char ** argv) {
printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; gigaFLOPS\n");
printf("=====================================================================================\n");
double gflops_sum = 0;
for (int i=0;i<benchmark_params.n_iterations ;i++) {
long long int start = ggml_time_us();
@ -219,6 +220,7 @@ int main(int argc, char ** argv) {
long long int stop = ggml_time_us();
long long int usec = stop-start;
double gflops = (double)(flops_per_matrix)/usec/1000.0;
gflops_sum += gflops;
printf("%9i;%8i;%6i;%6i;%6i;%15lli;%18lli;%10.2f\n",
i,
gf31.n_threads,
@ -248,4 +250,7 @@ int main(int argc, char ** argv) {
// Running a different graph computation to make sure we override the CPU cache lines
ggml_graph_compute(ctx, &gf32);
}
printf("\n");
printf("Average%78.2f\n",gflops_sum/((double)benchmark_params.n_iterations));
printf("=====================================================================================\n");
}

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@ -8,6 +8,7 @@
#include <iterator>
#include <algorithm>
#include <sstream>
#include <unordered_set>
#if defined(__APPLE__) && defined(__MACH__)
#include <sys/types.h>
@ -28,21 +29,21 @@
int32_t get_num_physical_cores() {
#ifdef __linux__
std::ifstream cpuinfo("/proc/cpuinfo");
std::string line;
while (std::getline(cpuinfo, line)) {
std::size_t pos = line.find("cpu cores");
if (pos != std::string::npos) {
pos = line.find(": ", pos);
if (pos != std::string::npos) {
try {
// Extract the number and return it
return static_cast<int32_t>(std::stoul(line.substr(pos + 2)));
} catch (const std::invalid_argument &) {
// Ignore if we could not parse
}
}
// enumerate the set of thread siblings, num entries is num cores
std::unordered_set<std::string> siblings;
for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
std::ifstream thread_siblings("/sys/devices/system/cpu"
+ std::to_string(cpu) + "/topology/thread_siblings");
if (!thread_siblings.is_open()) {
break; // no more cpus
}
std::string line;
if (std::getline(thread_siblings, line)) {
siblings.insert(line);
}
}
if (siblings.size() > 0) {
return static_cast<int32_t>(siblings.size());
}
#elif defined(__APPLE__) && defined(__MACH__)
int32_t num_physical_cores;
@ -320,12 +321,6 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
invalid_param = true;
break;
}
} else if (arg == "--n-parts") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.n_parts = std::stoi(argv[i]);
} else if (arg == "-h" || arg == "--help") {
gpt_print_usage(argc, argv, default_params);
exit(0);
@ -417,7 +412,6 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stderr, " --no-penalize-nl do not penalize newline token\n");
fprintf(stderr, " --memory-f32 use f32 instead of f16 for memory key+value\n");
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");
fprintf(stderr, " --keep number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
@ -472,7 +466,6 @@ struct llama_context * llama_init_from_gpt_params(const gpt_params & params) {
auto lparams = llama_context_default_params();
lparams.n_ctx = params.n_ctx;
lparams.n_parts = params.n_parts;
lparams.n_gpu_layers = params.n_gpu_layers;
lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16;

View file

@ -24,7 +24,6 @@ struct gpt_params {
int32_t seed = -1; // RNG seed
int32_t n_threads = get_num_physical_cores();
int32_t n_predict = -1; // new tokens to predict
int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions)
int32_t n_ctx = 512; // context size
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
@ -45,7 +44,7 @@ struct gpt_params {
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
std::string model = "models/lamma-7B/ggml-model.bin"; // model path
std::string model = "models/7B/ggml-model.bin"; // model path
std::string prompt = "";
std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
std::string input_prefix = ""; // string to prefix user inputs with

View file

@ -6,7 +6,6 @@
int main(int argc, char ** argv) {
gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
if (gpt_params_parse(argc, argv, params) == false) {
return 1;

View file

@ -50,7 +50,6 @@ void sigint_handler(int signo) {
int main(int argc, char ** argv) {
gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
@ -242,7 +241,7 @@ int main(int argc, char ** argv) {
sigint_action.sa_flags = 0;
sigaction(SIGINT, &sigint_action, NULL);
#elif defined (_WIN32)
auto console_ctrl_handler = [](DWORD ctrl_type) -> BOOL {
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
};
SetConsoleCtrlHandler(static_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);

View file

@ -116,7 +116,6 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
int main(int argc, char ** argv) {
gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
params.n_batch = 512;
if (gpt_params_parse(argc, argv, params) == false) {

View file

@ -321,7 +321,6 @@ int main(int argc, char ** argv) {
auto lparams = llama_context_default_params();
lparams.n_ctx = 256;
lparams.n_parts = 1;
lparams.seed = 1;
lparams.f16_kv = false;
lparams.use_mlock = false;

View file

@ -8,7 +8,6 @@
int main(int argc, char ** argv) {
gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
params.seed = 42;
params.n_threads = 4;
params.repeat_last_n = 64;
@ -27,7 +26,6 @@ int main(int argc, char ** argv) {
auto lparams = llama_context_default_params();
lparams.n_ctx = params.n_ctx;
lparams.n_parts = params.n_parts;
lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16;
lparams.use_mmap = params.use_mmap;

39
ggml.c
View file

@ -543,12 +543,7 @@ static inline __m256 sum_i16_pairs_float(const __m256i x) {
return _mm256_cvtepi32_ps(summed_pairs);
}
// multiply int8_t, add results pairwise twice and return as float vector
static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
// Get absolute values of x vectors
const __m256i ax = _mm256_sign_epi8(x, x);
// Sign the values of the y vectors
const __m256i sy = _mm256_sign_epi8(y, x);
static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
#if __AVXVNNI__
const __m256i zero = _mm256_setzero_si256();
const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy);
@ -560,6 +555,21 @@ static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
#endif
}
// multiply int8_t, add results pairwise twice and return as float vector
static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
#if __AVXVNNIINT8__
const __m256i zero = _mm256_setzero_si256();
const __m256i summed_pairs = _mm256_dpbssd_epi32(zero, x, y);
return _mm256_cvtepi32_ps(summed_pairs);
#else
// Get absolute values of x vectors
const __m256i ax = _mm256_sign_epi8(x, x);
// Sign the values of the y vectors
const __m256i sy = _mm256_sign_epi8(y, x);
return mul_sum_us8_pairs_float(ax, sy);
#endif
}
static inline __m128i packNibbles( __m256i bytes )
{
// Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh
@ -619,6 +629,17 @@ static inline __m256 sum_i16_pairs_float(const __m128i xh, const __m128i xl) {
return _mm256_cvtepi32_ps(summed_pairs);
}
static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) {
const __m128i axl = _mm256_castsi256_si128(ax);
const __m128i axh = _mm256_extractf128_si256(ax, 1);
const __m128i syl = _mm256_castsi256_si128(sy);
const __m128i syh = _mm256_extractf128_si256(sy, 1);
// Perform multiplication and create 16-bit values
const __m128i dotl = _mm_maddubs_epi16(axl, syl);
const __m128i doth = _mm_maddubs_epi16(axh, syh);
return sum_i16_pairs_float(doth, dotl);
}
// multiply int8_t, add results pairwise twice and return as float vector
static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) {
const __m128i xl = _mm256_castsi256_si128(x);
@ -2434,7 +2455,7 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void *
const __m256i bx = bytes_from_nibbles_32(x[i].qs);
const __m256i by = _mm256_loadu_si256( (const __m256i *)y[i].qs );
const __m256 xy = mul_sum_i8_pairs_float(bx, by);
const __m256 xy = mul_sum_us8_pairs_float(bx, by);
// Accumulate d0*d1*x*y
#if defined(__AVX2__)
@ -2906,7 +2927,7 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
const __m256 dy = _mm256_broadcast_ss(&y[i].d);
const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
const __m256 q = mul_sum_i8_pairs_float(bx, by);
const __m256 q = mul_sum_us8_pairs_float(bx, by);
acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc);
}
@ -2940,7 +2961,7 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void *
const __m256 dy = _mm256_broadcast_ss(&y[i].d);
const __m256i by = _mm256_loadu_si256((const __m256i *)y[i].qs);
const __m256 q = mul_sum_i8_pairs_float(bx, by);
const __m256 q = mul_sum_us8_pairs_float(bx, by);
acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc);
}

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@ -812,10 +812,9 @@ static bool kv_cache_init(
struct llama_context_params llama_context_default_params() {
struct llama_context_params result = {
/*.n_ctx =*/ 512,
/*.n_parts =*/ -1,
/*.gpu_layers =*/ 0,
/*.seed =*/ -1,
/*.f16_kv =*/ false,
/*.f16_kv =*/ true,
/*.logits_all =*/ false,
/*.vocab_only =*/ false,
/*.use_mmap =*/ true,

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@ -55,7 +55,6 @@ extern "C" {
struct llama_context_params {
int n_ctx; // text context
int n_parts; // -1 for default
int n_gpu_layers; // number of layers to store in VRAM
int seed; // RNG seed, -1 for random