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# Conflicts:
#	.github/workflows/build.yml
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Concedo 2023-04-21 16:24:30 +08:00
commit 82d74ca1a6
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2
Makefile
View file

@ -126,7 +126,7 @@ endif
#note: koboldcpp does not officially support cublas. You can manually link it if you want, but it will not be a regular feature
ifdef LLAMA_CUBLAS
CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include
LDFLAGS += -lcublas_static -lculibos -lcudart_static -lcublasLt_static -lpthread -ldl -L/usr/local/cuda/lib64
LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64
OBJS += ggml-cuda.o
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
nvcc -arch=native -c -o $@ $<

20
SHA256SUMS
View file

@ -1,12 +1,27 @@
700df0d3013b703a806d2ae7f1bfb8e59814e3d06ae78be0c66368a50059f33d models/7B/consolidated.00.pth
666a4bb533b303bdaf89e1b6a3b6f93535d868de31d903afdc20983dc526c847 models/7B/ggml-model-f16.bin
fcb7664c2e69776920b526362a243e912f73c36b1ec892eb354bab940f5edb5a models/7B/ggml-model-q4_0.bin
cc061458339a3eb8bcecbf0a825e9924fb7d1a8150f63cd5d091caa99215aafe models/7B/ggml-model-q4_1.bin
1bc7484c24a87612726d756f1761890e7acf5f412e23378577ce50fbe789b5b8 models/7B/ggml-model-q4_2.bin
3429bf198ec771886cf81a574df45245f3ebf04f0ce0956b73ef5d0ab01ff48b models/7B/ggml-model-q4_3.bin
7e89e242ddc0dd6f060b43ca219ce8b3e8f08959a72cb3c0855df8bb04d46265 models/7B/params.json
745bf4e29a4dd6f411e72976d92b452da1b49168a4f41c951cfcc8051823cf08 models/13B/consolidated.00.pth
d5ccbcc465c71c0de439a5aeffebe8344c68a519bce70bc7f9f92654ee567085 models/13B/consolidated.01.pth
2b206e9b21fb1076f11cafc624e2af97c9e48ea09312a0962153acc20d45f808 models/13B/ggml-model-f16.bin
4b69e4d6b6e3275230955997b90407fceca7e5ab3daf2e63a2c9e7270a8e1e3e models/13B/ggml-model-q4_0.bin
d9581b5b88e5622532fe897c9f9b0e67a317d22dd27a6f90fa4ab8c6d23ccdbb models/13B/ggml-model-q4_1.bin
8d55a2077317ec9a928c7851d6a43e08e51f7e9e08360f2a7a7e1deefea3134f models/13B/ggml-model-q4_2.bin
4208cdec9788ffa48dc1a17af2c36a0299f5bf3eb0e2b87889dda7fad591fca3 models/13B/ggml-model-q4_3.bin
4ab77bec4d4405ccb66a97b282574c89a94417e3c32e5f68f37e2876fc21322f models/13B/params.json
e23294a58552d8cdec5b7e8abb87993b97ea6eced4178ff2697c02472539d067 models/30B/consolidated.00.pth
4e077b7136c7ae2302e954860cf64930458d3076fcde9443f4d0e939e95903ff models/30B/consolidated.01.pth
24a87f01028cbd3a12de551dcedb712346c0b5cbdeff1454e0ddf2df9b675378 models/30B/consolidated.02.pth
1adfcef71420886119544949767f6a56cb6339b4d5fcde755d80fe68b49de93b models/30B/consolidated.03.pth
7e1b524061a9f4b27c22a12d6d2a5bf13b8ebbea73e99f218809351ed9cf7d37 models/30B/ggml-model-f16.bin
7a679908ce31c9d6ae2e38d6059bcd4d0ad3a870cd58cc1c8f7b36f2b2f51c73 models/30B/ggml-model-q4_0.bin
7b75ac615fa369ee593493a7e6ef87542bf0350255db928b22c5a24f6d598bcd models/30B/ggml-model-q4_1.bin
2c82b4954a94a6a284f452f6011c1e4f0d20362c194a0b1eb5737f5fd8a20fb3 models/30B/ggml-model-q4_2.bin
a6188660199dbcb8d5658abe7d89169869e50423494385830d9e6b330ea7fc33 models/30B/ggml-model-q4_3.bin
2c07118ea98d69dbe7810d88520e30288fa994751b337f8fca02b171955f44cb models/30B/params.json
135c563f6b3938114458183afb01adc9a63bef3d8ff7cccc3977e5d3664ecafe models/65B/consolidated.00.pth
9a600b37b19d38c7e43809485f70d17d1dc12206c07efa83bc72bb498a568bde models/65B/consolidated.01.pth
@ -16,5 +31,10 @@ e7babf7c5606f165a3756f527cb0fedc4f83e67ef1290391e52fb1cce5f26770 models/65B/con
a287c0dfe49081626567c7fe87f74cce5831f58e459b427b5e05567641f47b78 models/65B/consolidated.05.pth
72b4eba67a1a3b18cb67a85b70f8f1640caae9b40033ea943fb166bd80a7b36b models/65B/consolidated.06.pth
d27f5b0677d7ff129ceacd73fd461c4d06910ad7787cf217b249948c3f3bc638 models/65B/consolidated.07.pth
60758f2384d74e423dffddfd020ffed9d3bb186ebc54506f9c4a787d0f5367b0 models/65B/ggml-model-f16.bin
c671fe1bce71499ac732ec999770ebe53ac486623a7891e42c9dfdb6962d2c64 models/65B/ggml-model-q4_0.bin
4743a28aac3e5f32a6e838a815f51d3779de44fbbe251d745251e66c23c5950f models/65B/ggml-model-q4_1.bin
4a145a210c56982389b1ed34387e0590c3e0d7325fa9be4f2284fe4d244a3633 models/65B/ggml-model-q4_2.bin
305e91a4608b4f627b9b8ad5b4af75187d2684254bfd76dcb9db571618ef293c models/65B/ggml-model-q4_3.bin
999ed1659b469ccc2a941714c0a9656fa571d17c9f7c8c7589817ca90edef51b models/65B/params.json
9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347 models/tokenizer.model

137
examples/quantize-stats/quantize-stats.cpp
View file

@ -15,6 +15,8 @@
#include <string>
#include <unordered_map>
#include <vector>
#include <thread>
#include <mutex>
struct quantize_stats_params {
std::string model = "models/7B/ggml-model-f16.bin";
@ -27,7 +29,6 @@ struct quantize_stats_params {
std::vector<enum ggml_type> include_types;
};
const int64_t SCRATCH_ELEMENTS = 32*32;
const size_t HISTOGRAM_BUCKETS = 150;
const double HISTOGRAM_RANGE = 0.03;
@ -90,6 +91,13 @@ void update_error_stats(int64_t nelements, const float * input, const float * ou
stats.num_samples += nelements;
}
void combine_error_stats(error_stats & into, const error_stats & from) {
into.num_samples += from.num_samples;
into.total_error += from.total_error;
if (from.max_error > into.max_error) into.max_error = from.max_error;
for (size_t i=0; i<HISTOGRAM_BUCKETS; ++i) into.error_histogram[i] += from.error_histogram[i];
}
double find_quantile(const error_stats & stats, double quantile) {
double sum = std::accumulate(std::begin(stats.error_histogram), std::end(stats.error_histogram), 0.0);
@ -130,6 +138,36 @@ static bool tensor_is_contiguous(const struct ggml_tensor * tensor) {
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
}
void test_roundtrip_on_chunk(
const ggml_tensor * layer,
int64_t offset,
int64_t chunk_size,
const quantize_fns_t & qfns,
bool use_reference,
float * input_scratch,
char * quantized_scratch,
float * output_scratch,
error_stats & stats) {
if (layer->type == GGML_TYPE_F16) {
for (int i = 0; i < chunk_size; i++) {
input_scratch[i] = ggml_get_f32_1d(layer, i + offset);
}
} else {
input_scratch = ggml_get_data_f32(layer) + offset;
}
if (use_reference) {
qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
} else {
qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
}
qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
update_error_stats(chunk_size, input_scratch, output_scratch, stats);
}
// Run quantization function for a single layer and update error stats
void test_roundtrip_on_layer(
std::string & name,
@ -137,40 +175,61 @@ void test_roundtrip_on_layer(
const quantize_fns_t & qfns,
bool use_reference,
const ggml_tensor * layer,
float * input_scratch,
char *quantized_scratch,
float * output_scratch,
error_stats & total_error) {
std::vector<float> & input_scratch,
std::vector<char> & quantized_scratch,
std::vector<float> & output_scratch,
error_stats & total_error,
int max_thread = 0) {
assert(tensor_is_contiguous(layer));
error_stats layer_error {};
int64_t nelements = ggml_nelements(layer);
uint64_t nelements = ggml_nelements(layer);
for (int64_t offset = 0; offset < nelements; offset += SCRATCH_ELEMENTS) {
int64_t chunk_size = std::min(SCRATCH_ELEMENTS, nelements - offset);
if (layer->type == GGML_TYPE_F16) {
for (int i = 0; i < chunk_size; i++) {
input_scratch[i] = ggml_get_f32_1d(layer, i + offset);
}
} else {
input_scratch = ggml_get_data_f32(layer) + offset;
}
if (use_reference) {
qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
} else {
qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
}
qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
update_error_stats(chunk_size, input_scratch, output_scratch, total_error);
if (print_layer_stats) {
update_error_stats(chunk_size, input_scratch, output_scratch, layer_error);
}
float* input_scratch_ptr = nullptr;
if (layer->type == GGML_TYPE_F16) {
if (input_scratch.size() < nelements) input_scratch.resize(nelements);
input_scratch_ptr = input_scratch.data();
}
if (quantized_scratch.size() < 4*nelements) quantized_scratch.resize(4*nelements);
if (output_scratch.size() < nelements) output_scratch.resize(nelements);
if (max_thread < 1) max_thread = std::thread::hardware_concurrency();
int chunk_size = 32*512;
int num_chunks = (nelements + chunk_size - 1)/chunk_size;
if (num_chunks < 2 || max_thread < 2) {
test_roundtrip_on_chunk(layer, 0, nelements, qfns, use_reference, input_scratch_ptr, quantized_scratch.data(),
output_scratch.data(), print_layer_stats ? layer_error : total_error);
} else {
auto & stats = print_layer_stats ? layer_error : total_error;
std::mutex mutex;
uint64_t counter = 0;
auto compute = [&mutex, &counter, &stats, &qfns, nelements, layer, use_reference, input_scratch_ptr,
&quantized_scratch, &output_scratch, chunk_size] () {
error_stats local_stats {};
while (true) {
std::unique_lock<std::mutex> lock(mutex);
uint64_t offset = counter; counter += chunk_size;
if (offset >= nelements) {
combine_error_stats(stats, local_stats);
break;
}
lock.unlock();
uint64_t chunk = offset + chunk_size < nelements ? chunk_size : nelements - offset;
test_roundtrip_on_chunk(layer, offset, chunk, qfns, use_reference, input_scratch_ptr + offset,
quantized_scratch.data() + 4*offset, output_scratch.data() + offset, local_stats);
}
};
int nthread = std::min(num_chunks, max_thread);
std::vector<std::thread> workers(nthread-1);
for (auto& w : workers) w = std::thread(compute);
compute();
for (auto& w : workers) w.join();
}
if (print_layer_stats) {
print_error_stats(name, layer_error, false);
combine_error_stats(total_error, layer_error);
}
}
@ -181,6 +240,7 @@ int main(int argc, char ** argv) {
// read command line
int max_thread = 0;
bool invalid_param = false;
std::string arg;
for (int i = 1; i < argc; i++) {
@ -230,6 +290,12 @@ int main(int argc, char ** argv) {
fprintf(stderr, "error: %s not in list of types\n", argv[i]);
invalid_param = true;
}
} else if (arg == "-n" || arg == "--num-threads") {
if (++i >= argc) {
invalid_param = true;
break;
}
max_thread = atoi(argv[i]);
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
quantize_stats_print_usage(argc, argv);
@ -295,9 +361,9 @@ int main(int argc, char ** argv) {
}
printf("testing %d layers with max size %" PRId64 "\n", included_layers, max_nelements);
// allocate scratch space
std::vector<float> input_scratch(SCRATCH_ELEMENTS);
std::vector<char> quantized_scratch(SCRATCH_ELEMENTS*4);
std::vector<float> output_scratch(SCRATCH_ELEMENTS);
std::vector<float> input_scratch;
std::vector<char> quantized_scratch;
std::vector<float> output_scratch;
// loop throught quantization types
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
@ -328,10 +394,11 @@ int main(int argc, char ** argv) {
qfns,
params.reference,
kv_tensor.second,
input_scratch.data(),
quantized_scratch.data(),
output_scratch.data(),
global_stats
input_scratch,
quantized_scratch,
output_scratch,
global_stats,
max_thread
);
}

8
examples/quantize/quantize.cpp
View file

@ -10,11 +10,12 @@
int main(int argc, char ** argv) {
ggml_time_init();
if (argc != 4) {
fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n", argv[0]);
if (argc < 4) {
fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type [nthread]\n", argv[0]);
fprintf(stderr, " type = %d - q4_0\n", LLAMA_FTYPE_MOSTLY_Q4_0);
fprintf(stderr, " type = %d - q4_1\n", LLAMA_FTYPE_MOSTLY_Q4_1);
fprintf(stderr, " type = %d - q4_2\n", LLAMA_FTYPE_MOSTLY_Q4_2);
fprintf(stderr, " type = %d - q4_3\n", LLAMA_FTYPE_MOSTLY_Q4_3);
return 1;
}
@ -29,6 +30,7 @@ int main(int argc, char ** argv) {
const std::string fname_out = argv[2];
const enum llama_ftype ftype = (enum llama_ftype)atoi(argv[3]);
int nthread = argc > 4 ? atoi(argv[4]) : 0;
const int64_t t_main_start_us = ggml_time_us();
@ -38,7 +40,7 @@ int main(int argc, char ** argv) {
{
const int64_t t_start_us = ggml_time_us();
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ftype)) {
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ftype, nthread)) {
fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
return 1;
}

40
ggml-cuda.cu
View file

@ -22,11 +22,20 @@ static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK4_1 / 2, "wrong q4_1 b
#define QK4_2 16
typedef struct {
__half d; // delta
__half d; // delta
uint8_t qs[QK4_2 / 2]; // nibbles / quants
} block_q4_2;
static_assert(sizeof(block_q4_2) == sizeof(ggml_fp16_t) + QK4_2 / 2, "wrong q4_2 block size/padding");
#define QK4_3 16
typedef struct {
__half d; // delta
__half m; // min
uint8_t qs[QK4_3 / 2]; // nibbles / quants
} block_q4_3;
static_assert(sizeof(block_q4_3) == 2 * sizeof(ggml_fp16_t) + QK4_3 / 2, "wrong q4_3 block size/padding");
static __global__ void dequantize_block_q4_0(const void * vx, float * y) {
const block_q4_0 * x = (const block_q4_0 *) vx;
@ -98,6 +107,30 @@ static __global__ void dequantize_block_q4_2(const void * vx, float * y) {
}
}
static __global__ void dequantize_block_q4_3(const void * vx, float * y) {
const block_q4_3 * x = (const block_q4_3 *) vx;
const int i = blockIdx.x;
const float d = x[i].d;
const float m = x[i].m;
const uint8_t * pp = x[i].qs;
for (int l = 0; l < QK4_3; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vi0 = vi & 0xf;
const int8_t vi1 = vi >> 4;
const float v0 = vi0*d + m;
const float v1 = vi1*d + m;
y[i*QK4_3 + l + 0] = v0;
y[i*QK4_3 + l + 1] = v1;
}
}
extern "C" {
__host__ void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_0;
@ -113,4 +146,9 @@ extern "C" {
const int nb = k / QK4_2;
dequantize_block_q4_2<<<nb, 1, 0, stream>>>(vx, y);
}
__host__ void dequantize_row_q4_3_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_3;
dequantize_block_q4_3<<<nb, 1, 0, stream>>>(vx, y);
}
}

1
ggml-cuda.h
View file

@ -5,6 +5,7 @@ extern "C" {
void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream);
void dequantize_row_q4_3_cuda(const void * vx, float * y, int k, cudaStream_t stream);
#ifdef __cplusplus
}

401
ggml.c
View file

@ -639,7 +639,7 @@ typedef struct {
float m; // min
uint8_t qs[QK4_1 / 2]; // nibbles / quants
} block_q4_1;
static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
static_assert(sizeof(block_q4_1) == 2 * sizeof(float) + QK4_1 / 2, "wrong q4_1 block size/padding");
#define QK4_2 16
typedef struct {
@ -648,6 +648,14 @@ typedef struct {
} block_q4_2;
static_assert(sizeof(block_q4_2) == sizeof(ggml_fp16_t) + QK4_2 / 2, "wrong q4_2 block size/padding");
#define QK4_3 16
typedef struct {
ggml_fp16_t d; // delta
ggml_fp16_t m; // min
uint8_t qs[QK4_3 / 2]; // nibbles / quants
} block_q4_3;
static_assert(sizeof(block_q4_3) == 2 * sizeof(ggml_fp16_t) + QK4_3 / 2, "wrong q4_3 block size/padding");
#define QK8_0 32
typedef struct {
float d; // delta
@ -1205,7 +1213,6 @@ static void quantize_row_q4_2_rmse(const float * restrict x, block_q4_2 * restri
const int nb = k / QK4_2;
for (int i = 0; i < nb; i++) {
float scale = kquantize_q4_with_bounds(QK4_2, -8, 7, x, CANDIDATE_COUNT, candidates, L);
y[i].d = GGML_FP32_TO_FP16(scale);
@ -1233,6 +1240,49 @@ static void quantize_row_q4_2(const float * restrict x, void * restrict vy, int
quantize_row_q4_2_rmse(x, y, k);
}
static void quantize_row_q4_3_reference(const float * restrict x, block_q4_3 * restrict y, int k) {
assert(k % QK4_3 == 0);
const int nb = k / QK4_3;
for (int i = 0; i < nb; i++) {
float min = FLT_MAX;
float max = -FLT_MAX;
for (int l = 0; l < QK4_3; l++) {
const float v = x[i*QK4_3 + l];
if (v < min) min = v;
if (v > max) max = v;
}
const float d = (max - min) / ((1 << 4) - 1);
const float id = d ? 1.0f/d : 0.0f;
y[i].d = GGML_FP32_TO_FP16(d);
y[i].m = GGML_FP32_TO_FP16(min);
for (int l = 0; l < QK4_3; l += 2) {
const float v0 = (x[i*QK4_3 + l + 0] - min)*id;
const float v1 = (x[i*QK4_3 + l + 1] - min)*id;
const uint8_t vi0 = (int) (v0 + 0.5f);
const uint8_t vi1 = (int) (v1 + 0.5f);
assert(vi0 < 16);
assert(vi1 < 16);
y[i].qs[l/2] = vi0 | (vi1 << 4);
}
}
}
static void quantize_row_q4_3(const float * restrict x, void * restrict vy, int k) {
assert(k % QK4_3 == 0);
block_q4_3 * restrict y = vy;
quantize_row_q4_3_reference(x, y, k);
}
// reference implementation for deterministic creation of model files
static void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k) {
assert(k % QK8_0 == 0);
@ -1637,9 +1687,40 @@ static void dequantize_row_q4_2(const void * restrict vx, float * restrict y, in
}
}
static void dequantize_row_q4_3(const void * restrict vx, float * restrict y, int k) {
assert(k % QK4_3 == 0);
const int nb = k / QK4_3;
const block_q4_3 * restrict x = vx;
for (int i = 0; i < nb; i++) {
const float d = GGML_FP16_TO_FP32(x[i].d);
const float m = GGML_FP16_TO_FP32(x[i].m);
const uint8_t * restrict pp = x[i].qs;
for (int l = 0; l < QK4_3; l += 2) {
const uint8_t vi = pp[l/2];
const int8_t vi0 = vi & 0xf;
const int8_t vi1 = vi >> 4;
const float v0 = vi0*d + m;
const float v1 = vi1*d + m;
y[i*QK4_3 + l + 0] = v0;
y[i*QK4_3 + l + 1] = v1;
assert(!isnan(y[i*QK4_3 + l + 0]));
assert(!isnan(y[i*QK4_3 + l + 1]));
}
}
}
static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static void ggml_vec_dot_q4_1_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static void ggml_vec_dot_q4_2_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static void ggml_vec_dot_q4_3_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
[GGML_TYPE_Q4_0] = {
@ -1663,6 +1744,13 @@ static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
.quantize_row_q_dot = quantize_row_q8_0,
.vec_dot_q = ggml_vec_dot_q4_2_q8_0,
},
[GGML_TYPE_Q4_3] = {
.dequantize_row_q = dequantize_row_q4_3,
.quantize_row_q = quantize_row_q4_3,
.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_3_reference, // TODO: RMSE optimization
.quantize_row_q_dot = quantize_row_q8_0,
.vec_dot_q = ggml_vec_dot_q4_3_q8_0,
},
[GGML_TYPE_Q8_0] = {
.dequantize_row_q = NULL, // TODO
.quantize_row_q = quantize_row_q8_0,
@ -2657,6 +2745,7 @@ static void ggml_vec_dot_q4_2_q8_0(const int n, float * restrict s, const void *
const block_q4_2 * restrict x0_1 = &x[2*(i + 0) + 1];
const block_q4_2 * restrict x1_0 = &x[2*(i + 1) + 0];
const block_q4_2 * restrict x1_1 = &x[2*(i + 1) + 1];
const block_q8_0 * restrict y0 = &y[i + 0];
const block_q8_0 * restrict y1 = &y[i + 1];
@ -2811,6 +2900,154 @@ static void ggml_vec_dot_q4_2_q8_0(const int n, float * restrict s, const void *
*s = sumf;
}
static void ggml_vec_dot_q4_3_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
const int nb = n / QK8_0;
assert(n % QK8_0 == 0);
assert(nb % 2 == 0);
assert(QK8_0 == 2*QK4_2);
const block_q4_3 * restrict x = vx;
const block_q8_0 * restrict y = vy;
float sumf = 0.0;
#if defined(__ARM_NEON)
float32x4_t sumv0 = vdupq_n_f32(0.0f);
float32x4_t sumv1 = vdupq_n_f32(0.0f);
for (int i = 0; i < nb; i += 2) {
const block_q4_3 * restrict x0_0 = &x[2*(i + 0) + 0];
const block_q4_3 * restrict x0_1 = &x[2*(i + 0) + 1];
const block_q4_3 * restrict x1_0 = &x[2*(i + 1) + 0];
const block_q4_3 * restrict x1_1 = &x[2*(i + 1) + 1];
const block_q8_0 * restrict y0 = &y[i + 0];
const block_q8_0 * restrict y1 = &y[i + 1];
const uint8x16_t m4b = vdupq_n_u8(0xf);
const float x0_0d = GGML_FP16_TO_FP32(x0_0->d);
const float x0_1d = GGML_FP16_TO_FP32(x0_1->d);
const float x1_0d = GGML_FP16_TO_FP32(x1_0->d);
const float x1_1d = GGML_FP16_TO_FP32(x1_1->d);
const float x0_0m = GGML_FP16_TO_FP32(x0_0->m);
const float x0_1m = GGML_FP16_TO_FP32(x0_1->m);
const float x1_0m = GGML_FP16_TO_FP32(x1_0->m);
const float x1_1m = GGML_FP16_TO_FP32(x1_1->m);
const uint8x16_t v0_0 = vcombine_u8(vld1_u8(x0_0->qs), vld1_u8(x0_1->qs));
const uint8x16_t v0_1 = vcombine_u8(vld1_u8(x1_0->qs), vld1_u8(x1_1->qs));
// 4-bit -> 8-bit
const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b));
const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b));
const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
// interleave
const int8x16_t v0_0lz = vzip1q_s8(v0_0l, v0_0h);
const int8x16_t v0_0hz = vzip2q_s8(v0_0l, v0_0h);
const int8x16_t v0_1lz = vzip1q_s8(v0_1l, v0_1h);
const int8x16_t v0_1hz = vzip2q_s8(v0_1l, v0_1h);
// load y
const int8x16_t v1_0l = vld1q_s8(y0->qs);
const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
const int8x16_t v1_1l = vld1q_s8(y1->qs);
const int8x16_t v1_1h = vld1q_s8(y1->qs + 16);
const int16x8_t sy0_0 = vaddq_s16(vmovl_s8(vget_low_s8(v1_0l)), vmovl_s8(vget_high_s8(v1_0l)));
const int16x8_t sy0_1 = vaddq_s16(vmovl_s8(vget_low_s8(v1_0h)), vmovl_s8(vget_high_s8(v1_0h)));
const int16x8_t sy1_0 = vaddq_s16(vmovl_s8(vget_low_s8(v1_1l)), vmovl_s8(vget_high_s8(v1_1l)));
const int16x8_t sy1_1 = vaddq_s16(vmovl_s8(vget_low_s8(v1_1h)), vmovl_s8(vget_high_s8(v1_1h)));
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddl_s16(vget_low_s16(sy0_0), vget_high_s16(sy0_0))), x0_0m*y0->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddl_s16(vget_low_s16(sy0_1), vget_high_s16(sy0_1))), x0_1m*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddl_s16(vget_low_s16(sy1_0), vget_high_s16(sy1_0))), x1_0m*y1->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddl_s16(vget_low_s16(sy1_1), vget_high_s16(sy1_1))), x1_1m*y1->d);
#if defined(__ARM_FEATURE_DOTPROD)
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_0lz, v1_0l)), x0_0d*y0->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_0hz, v1_0h)), x0_1d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_1lz, v1_1l)), x1_0d*y1->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vdotq_s32(vdupq_n_s32(0), v0_1hz, v1_1h)), x1_1d*y1->d);
#else
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0lz), vget_low_s8 (v1_0l));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0lz), vget_high_s8(v1_0l));
const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hz), vget_low_s8 (v1_0h));
const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hz), vget_high_s8(v1_0h));
const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1lz), vget_low_s8 (v1_1l));
const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1lz), vget_high_s8(v1_1l));
const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hz), vget_low_s8 (v1_1h));
const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hz), vget_high_s8(v1_1h));
const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
const int32x4_t pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
const int32x4_t ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(pl0), x0_0d*y0->d);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(ph0), x0_1d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(pl1), x1_0d*y1->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(ph1), x1_1d*y1->d);
#endif
}
sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
#else
// scalar
for (int i = 0; i < nb; i++) {
const uint8_t * restrict x0 = x[2*i + 0].qs;
const uint8_t * restrict x1 = x[2*i + 1].qs;
const int8_t * restrict y0 = y[i].qs;
const float d0 = GGML_FP16_TO_FP32(x[2*i + 0].d);
const float m0 = GGML_FP16_TO_FP32(x[2*i + 0].m);
const float d1 = GGML_FP16_TO_FP32(x[2*i + 1].d);
const float m1 = GGML_FP16_TO_FP32(x[2*i + 1].m);
int sy_0 = 0;
int sy_1 = 0;
int sxy_0 = 0;
int sxy_1 = 0;
for (int j = 0; j < QK8_0/4; j++) {
const uint8_t v0 = x0[j];
const uint8_t v1 = x1[j];
const int x0_0 = v0 & 0xf;
const int x1_0 = v0 >> 4;
const int x0_1 = v1 & 0xf;
const int x1_1 = v1 >> 4;
const int y0_0 = y0[2*j + 0];
const int y1_0 = y0[2*j + 1];
const int y0_1 = y0[2*(j + QK8_0/4) + 0];
const int y1_1 = y0[2*(j + QK8_0/4) + 1];
sy_0 += y0_0 + y1_0;
sy_1 += y0_1 + y1_1;
sxy_0 += x0_0*y0_0 + x1_0*y1_0;
sxy_1 += x0_1*y0_1 + x1_1*y1_1;
}
sumf += (d0*sxy_0 + m0*sy_0)*y[i].d;
sumf += (d1*sxy_1 + m1*sy_1)*y[i].d;
}
#endif
*s = sumf;
}
// 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) {
@ -3058,12 +3295,13 @@ static const int GGML_BLCK_SIZE[GGML_TYPE_COUNT] = {
[GGML_TYPE_Q4_0] = QK4_0,
[GGML_TYPE_Q4_1] = QK4_1,
[GGML_TYPE_Q4_2] = QK4_2,
[GGML_TYPE_Q4_3] = QK4_3,
[GGML_TYPE_Q8_0] = QK8_0,
[GGML_TYPE_I8] = 1,
[GGML_TYPE_I16] = 1,
[GGML_TYPE_I32] = 1,
};
static_assert(GGML_TYPE_COUNT == 9, "GGML_BLCK_SIZE is outdated");
static_assert(GGML_TYPE_COUNT == 10, "GGML_BLCK_SIZE is outdated");
static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
[GGML_TYPE_F32] = sizeof(float),
@ -3071,12 +3309,13 @@ static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
[GGML_TYPE_Q4_0] = sizeof(block_q4_0),
[GGML_TYPE_Q4_1] = sizeof(block_q4_1),
[GGML_TYPE_Q4_2] = sizeof(block_q4_2),
[GGML_TYPE_Q4_3] = sizeof(block_q4_3),
[GGML_TYPE_Q8_0] = sizeof(block_q8_0),
[GGML_TYPE_I8] = sizeof(int8_t),
[GGML_TYPE_I16] = sizeof(int16_t),
[GGML_TYPE_I32] = sizeof(int32_t),
};
static_assert(GGML_TYPE_COUNT == 9, "GGML_TYPE_SIZE is outdated");
static_assert(GGML_TYPE_COUNT == 10, "GGML_TYPE_SIZE is outdated");
static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = {
@ -3085,12 +3324,13 @@ static const char * GGML_TYPE_NAME[GGML_TYPE_COUNT] = {
[GGML_TYPE_Q4_0] = "q4_0",
[GGML_TYPE_Q4_1] = "q4_1",
[GGML_TYPE_Q4_2] = "q4_2",
[GGML_TYPE_Q4_3] = "q4_3",
[GGML_TYPE_Q8_0] = "q8_0",
[GGML_TYPE_I8] = "i8",
[GGML_TYPE_I16] = "i16",
[GGML_TYPE_I32] = "i32",
};
static_assert(GGML_TYPE_COUNT == 9, "GGML_TYPE_NAME is outdated");
static_assert(GGML_TYPE_COUNT == 10, "GGML_TYPE_NAME is outdated");
static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = {
[GGML_TYPE_F32] = false,
@ -3098,12 +3338,13 @@ static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = {
[GGML_TYPE_Q4_0] = true,
[GGML_TYPE_Q4_1] = true,
[GGML_TYPE_Q4_2] = true,
[GGML_TYPE_Q4_3] = true,
[GGML_TYPE_Q8_0] = true,
[GGML_TYPE_I8] = false,
[GGML_TYPE_I16] = false,
[GGML_TYPE_I32] = false,
};
static_assert(GGML_TYPE_COUNT == 9, "GGML_IS_QUANTIZED is outdated");
static_assert(GGML_TYPE_COUNT == 10, "GGML_IS_QUANTIZED is outdated");
static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"NONE",
@ -3365,7 +3606,7 @@ static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct
(t0->ne[3] == t1->ne[3]);
}
static inline bool ggml_is_quantized(enum ggml_type type) {
bool ggml_is_quantized(enum ggml_type type) {
return GGML_IS_QUANTIZED[type];
}
@ -5901,7 +6142,6 @@ static void ggml_compute_forward_dup_f32(
i10 += ne00 * ir0;
while (i10 >= ne0) {
i10 -= ne0;
i11++;
if (++i11 == ne1) {
i11 = 0;
if (++i12 == ne2) {
@ -6315,6 +6555,7 @@ static void ggml_compute_forward_add(
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_2:
case GGML_TYPE_Q4_3:
{
ggml_compute_forward_add_q_f32(params, src0, src1, dst);
} break;
@ -7964,6 +8205,7 @@ static void ggml_compute_forward_mul_mat(
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_2:
case GGML_TYPE_Q4_3:
case GGML_TYPE_Q8_0:
{
ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst);
@ -7981,34 +8223,6 @@ static void ggml_compute_forward_mul_mat(
GGML_ASSERT(false);
} break;
}
#if 0
if (src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_Q4_1) {
static int first = 8;
printf("src0: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src0->ne[0], src0->ne[1], src0->ne[2]);
printf("src1: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src1->ne[0], src1->ne[1], src1->ne[2]);
printf("dst: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", dst->ne[0], dst->ne[1], dst->ne[2]);
if (first) {
--first;
} else {
for (int k = 0; k < dst->ne[1]; ++k) {
for (int j = 0; j < dst->ne[0]/16; ++j) {
for (int i = 0; i < 16; ++i) {
printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]);
}
printf("\n");
}
printf("\n");
}
printf("\n");
exit(0);
}
} else {
printf("aaaa src0: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src0->ne[0], src0->ne[1], src0->ne[2]);
printf("aaaa src1: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", src1->ne[0], src1->ne[1], src1->ne[2]);
printf("aaaa dst: ne0 = %5d, ne1 = %5d, ne2 = %5d\n", dst->ne[0], dst->ne[1], dst->ne[2]);
}
#endif
}
// ggml_compute_forward_scale
@ -8220,6 +8434,7 @@ static void ggml_compute_forward_get_rows(
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_2:
case GGML_TYPE_Q4_3:
case GGML_TYPE_Q8_0:
{
ggml_compute_forward_get_rows_q(params, src0, src1, dst);
@ -8450,9 +8665,11 @@ static void ggml_compute_forward_rope_f32(
const float theta_scale = powf(10000.0, -2.0f/n_dims);
const bool is_neox = mode & 2;
for (int64_t i3 = 0; i3 < ne3; i3++) {
for (int64_t i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) {
const int p = (mode == 0 ? n_past + i2 : i2);
for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
const int p = ((mode & 1) == 0 ? n_past + i2 : i2);
for (int64_t i1 = 0; i1 < ne1; i1++) {
if (ir++ < ir0) continue;
if (ir > ir1) break;
@ -8465,14 +8682,25 @@ static void ggml_compute_forward_rope_f32(
theta *= theta_scale;
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);
if (!is_neox) {
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);
const float x0 = src[0];
const float 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;
dst_data[0] = x0*cos_theta - x1*sin_theta;
dst_data[1] = x0*sin_theta + x1*cos_theta;
} else {
const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0);
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0);
const float x0 = src[0];
const float x1 = src[n_dims/2];
dst_data[0] = x0*cos_theta - x1*sin_theta;
dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
}
}
}
}
@ -8527,9 +8755,11 @@ static void ggml_compute_forward_rope_f16(
const float theta_scale = powf(10000.0, -2.0f/n_dims);
const bool is_neox = mode & 2;
for (int64_t i3 = 0; i3 < ne3; i3++) {
for (int64_t i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) {
const int p = (mode == 0 ? n_past + i2 : i2);
for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) {
const int p = ((mode & 1) == 0 ? n_past + i2 : i2);
for (int64_t i1 = 0; i1 < ne1; i1++) {
if (ir++ < ir0) continue;
if (ir > ir1) break;
@ -8542,14 +8772,25 @@ static void ggml_compute_forward_rope_f16(
theta *= theta_scale;
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);
if (!is_neox) {
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);
const float x0 = GGML_FP16_TO_FP32(src[0]);
const 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);
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);
} else {
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0);
ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + (i0/2)*nb0);
const float x0 = GGML_FP16_TO_FP32(src[0]);
const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]);
dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
}
}
}
}
@ -11959,6 +12200,62 @@ size_t ggml_quantize_q4_2(const float * src, void * dst, int n, int k, int64_t *
return (n/QK4_2*sizeof(block_q4_2));
}
size_t ggml_quantize_q4_3(const float * src, void * dst, int n, int k, int64_t * hist) {
assert(k % QK4_3 == 0);
const int nb = k / QK4_3;
for (int j = 0; j < n; j += k) {
block_q4_3 * restrict y = (block_q4_3 *)dst + j/QK4_3;
quantize_row_q4_3_reference(src + j, y, k);
for (int i = 0; i < nb; i++) {
for (int l = 0; l < QK4_3; l += 2) {
const uint8_t vi0 = y[i].qs[l/2] & 0xF;
const uint8_t vi1 = y[i].qs[l/2] >> 4;
hist[vi0]++;
hist[vi1]++;
}
}
}
return (n/QK4_3*sizeof(block_q4_3));
}
size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist) {
size_t result = 0;
switch (type) {
case GGML_TYPE_Q4_0:
{
GGML_ASSERT(start % QK4_0 == 0);
block_q4_0 * block = (block_q4_0*)dst + start / QK4_0;
result = ggml_quantize_q4_0(src + start, block, n, n, hist);
} break;
case GGML_TYPE_Q4_1:
{
GGML_ASSERT(start % QK4_1 == 0);
block_q4_1 * block = (block_q4_1*)dst + start / QK4_1;
result = ggml_quantize_q4_1(src + start, block, n, n, hist);
} break;
case GGML_TYPE_Q4_2:
{
GGML_ASSERT(start % QK4_2 == 0);
block_q4_2 * block = (block_q4_2*)dst + start / QK4_2;
result = ggml_quantize_q4_2(src + start, block, n, n, hist);
} break;
case GGML_TYPE_Q4_3:
{
GGML_ASSERT(start % QK4_3 == 0);
block_q4_3 * block = (block_q4_3*)dst + start / QK4_3;
result = ggml_quantize_q4_3(src + start, block, n, n, hist);
} break;
default:
assert(false);
}
return result;
}
////////////////////////////////////////////////////////////////////////////////
int ggml_cpu_has_avx(void) {

11
ggml.h
View file

@ -205,7 +205,8 @@ enum ggml_type {
GGML_TYPE_Q4_0 = 2,
GGML_TYPE_Q4_1 = 3,
GGML_TYPE_Q4_2 = 4,
GGML_TYPE_Q8_0 = 5,
GGML_TYPE_Q4_3 = 5,
GGML_TYPE_Q8_0 = 6,
GGML_TYPE_I8,
GGML_TYPE_I16,
GGML_TYPE_I32,
@ -360,6 +361,8 @@ const char * ggml_type_name(enum ggml_type type);
size_t ggml_element_size(const struct ggml_tensor * tensor);
bool ggml_is_quantized(enum ggml_type type);
struct ggml_context * ggml_init(struct ggml_init_params params);
void ggml_free(struct ggml_context * ctx);
@ -627,7 +630,8 @@ struct ggml_tensor * ggml_soft_max(
// rotary position embedding
// in-place, returns view(a)
// if mode == 1, skip n_past elements
// if mode & 1 == 1, skip n_past elements
// if mode & 2 == 1, GPT-NeoX style
// TODO: avoid creating a new tensor every time
struct ggml_tensor * ggml_rope(
struct ggml_context * ctx,
@ -808,6 +812,9 @@ enum ggml_opt_result ggml_opt(
size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
size_t ggml_quantize_q4_2(const float * src, void * dst, int n, int k, int64_t * hist);
size_t ggml_quantize_q4_3(const float * src, void * dst, int n, int k, int64_t * hist);
size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
//
// system info

74
llama.cpp
View file

@ -24,6 +24,9 @@
#include <memory>
#include <algorithm>
#include <initializer_list>
#include <thread>
#include <atomic>
#include <mutex>
#define LLAMA_USE_SCRATCH
#define LLAMA_MAX_SCRATCH_BUFFERS 16
@ -486,6 +489,7 @@ struct llama_file_loader {
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_2:
case GGML_TYPE_Q4_3:
break;
default: {
throw format("unrecognized tensor type %u\n", shard.type);
@ -559,6 +563,7 @@ struct llama_file_saver {
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q4_2:
case GGML_TYPE_Q4_3:
break;
default: LLAMA_ASSERT(false);
}
@ -848,6 +853,7 @@ static const char *llama_ftype_name(enum llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
return "mostly Q4_1, some F16";
case LLAMA_FTYPE_MOSTLY_Q4_2: return "mostly Q4_2";
case LLAMA_FTYPE_MOSTLY_Q4_3: return "mostly Q4_3";
default: return "unknown, may not work";
}
}
@ -1576,15 +1582,20 @@ static llama_vocab::id llama_sample_top_p_top_k(
// quantization
//
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, enum llama_ftype ftype) {
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, enum llama_ftype ftype, int nthread) {
ggml_type quantized_type;
switch (ftype) {
case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
case LLAMA_FTYPE_MOSTLY_Q4_2: quantized_type = GGML_TYPE_Q4_2; break;
case LLAMA_FTYPE_MOSTLY_Q4_3: quantized_type = GGML_TYPE_Q4_3; break;
default: throw format("invalid output file type %d\n", ftype);
};
if (nthread <= 0) {
nthread = std::thread::hardware_concurrency();
}
std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp.c_str(), /*use_mmap*/ false,
/*vocab_only*/ false));
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), ftype);
@ -1593,6 +1604,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
size_t total_size_new = 0;
std::vector<int64_t> hist_all(1 << 4, 0);
std::vector<std::thread> workers;
std::mutex mutex;
size_t idx = 0;
for (llama_load_tensor & tensor : model_loader->tensors_map.tensors) {
llama_buffer read_data;
@ -1611,6 +1625,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
// quantize only 2D tensors
quantize &= (tensor.ne.size() == 2);
// uncomment this to keep the output layer in FP16
//if (tensor.name == "output.weight") {
// quantize = false;
//}
enum ggml_type new_type;
void * new_data;
size_t new_size;
@ -1646,21 +1665,37 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
new_data = work.addr;
std::vector<int64_t> hist_cur(1 << 4, 0);
switch (new_type) {
case GGML_TYPE_Q4_0:
{
new_size = ggml_quantize_q4_0(f32_data, new_data, nelements, (int) tensor.ne.at(0), hist_cur.data());
} break;
case GGML_TYPE_Q4_1:
{
new_size = ggml_quantize_q4_1(f32_data, new_data, nelements, (int) tensor.ne.at(0), hist_cur.data());
} break;
case GGML_TYPE_Q4_2:
{
new_size = ggml_quantize_q4_2(f32_data, new_data, nelements, (int) tensor.ne.at(0), hist_cur.data());
} break;
default:
LLAMA_ASSERT(false);
int chunk_size = 32 * 512;
const int nchunk = (nelements + chunk_size - 1)/chunk_size;
const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
if (nthread_use < 2) {
new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nelements, hist_cur.data());
} else {
size_t counter = 0;
new_size = 0;
auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements, chunk_size] () {
std::vector<int64_t> local_hist;
size_t local_size = 0;
while (true) {
std::unique_lock<std::mutex> lock(mutex);
size_t first = counter; counter += chunk_size;
if (first >= nelements) {
if (!local_hist.empty()) {
for (int j=0; j<int(local_hist.size()); ++j) hist_cur[j] += local_hist[j];
new_size += local_size;
}
break;
}
lock.unlock();
size_t last = std::min(nelements, first + chunk_size);
if (local_hist.empty()) local_hist.resize(hist_cur.size(), 0);
local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first, last - first, local_hist.data());
}
};
if (int(workers.size()) < nthread_use - 1) workers.resize(nthread_use - 1);
for (int it = 0; it < nthread_use - 1; ++it) workers[it] = std::thread(compute);
compute();
for (int it = 0; it < nthread_use - 1; ++it) workers[it].join();
}
printf("size = %8.2f MB -> %8.2f MB | hist: ", tensor.size/1024.0/1024.0, new_size/1024.0/1024.0);
@ -1782,9 +1817,10 @@ void llama_free(struct llama_context * ctx) {
int llama_model_quantize(
const char * fname_inp,
const char * fname_out,
enum llama_ftype ftype) {
enum llama_ftype ftype,
int nthread) {
try {
llama_model_quantize_internal(fname_inp, fname_out, ftype);
llama_model_quantize_internal(fname_inp, fname_out, ftype, nthread);
return 0;
} catch (const std::string & err) {
fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.c_str());
@ -1970,7 +2006,7 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char *
base_t = dest_t;
}
if (base_t->type == GGML_TYPE_Q4_0 || base_t->type == GGML_TYPE_Q4_1 || base_t->type == GGML_TYPE_Q4_2) {
if (ggml_is_quantized(base_t->type)) {
if (!warned) {
fprintf(stderr, "%s: warning: using a lora adapter with a quantized model may result in poor quality, "
"use a f16 or f32 base model with --lora-base\n", __func__);

5
llama.h
View file

@ -73,6 +73,7 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // except 1d tensors
};
LLAMA_API struct llama_context_params llama_context_default_params();
@ -92,10 +93,12 @@ extern "C" {
// TODO: not great API - very likely to change
// Returns 0 on success
// nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given
LLAMA_API int llama_model_quantize(
const char * fname_inp,
const char * fname_out,
enum llama_ftype ftype);
enum llama_ftype ftype,
int nthread);
// Apply a LoRA adapter to a loaded model
// path_base_model is the path to a higher quality model to use as a base for