vbatts/llama.cpp
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eiery 2023-04-22 00:02:09 -04:00 • committed by GitHub
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12 changed files with 481 additions and 164 deletions
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2
CMakeLists.txt
View file

@ -307,7 +307,7 @@ add_library(ggml OBJECT
target_include_directories(ggml PUBLIC .)
target_compile_features(ggml PUBLIC c_std_11) # don't bump
target_link_libraries(ggml PRIVATE Threads::Threads ${LLAMA_EXTRA_LIBS})
target_link_libraries(ggml PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS})
if (BUILD_SHARED_LIBS)
set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON)
endif()

10
Makefile
View file

@ -101,11 +101,13 @@ ifdef LLAMA_OPENBLAS
LDFLAGS += -lopenblas
endif
ifdef LLAMA_CUBLAS
CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include
LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64
OBJS += ggml-cuda.o
CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include
LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64
OBJS += ggml-cuda.o
NVCC = nvcc
NVCCFLAGS = --forward-unknown-to-host-linker -arch=native
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
nvcc -arch=native -c -o $@ $<
$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -c $< -o $@
endif
ifdef LLAMA_GPROF
CFLAGS += -pg

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

18
examples/main/main.cpp
View file

@ -264,7 +264,7 @@ int main(int argc, char ** argv) {
// infinite text generation via context swapping
// if we run out of context:
// - take the n_keep first tokens from the original prompt (via n_past)
// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in a batch
// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
if (n_past + (int) embd.size() > n_ctx) {
const int n_left = n_past - params.n_keep;
@ -282,13 +282,21 @@ int main(int argc, char ** argv) {
//printf("\n---\n");
}
if (llama_eval(ctx, embd.data(), embd.size(), n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
// evaluate tokens in batches
// embd is typically prepared beforehand to fit within a batch, but not always
for (int i = 0; i < (int) embd.size(); i += params.n_batch) {
int n_eval = (int) embd.size() - i;
if (n_eval > params.n_batch) {
n_eval = params.n_batch;
}
if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}
n_past += n_eval;
}
}
n_past += embd.size();
embd.clear();
if ((int) embd_inp.size() <= n_consumed && !is_interacting) {

8
examples/perplexity/perplexity.cpp
View file

@ -53,7 +53,13 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
auto end_t = std::chrono::high_resolution_clock::now();
if (i == 0) {
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));
printf("%.2f seconds per pass - ETA ", seconds);
int total_seconds = (int)(seconds * seq_count);
if (total_seconds >= 60*60) {
printf("%d hours ", total_seconds / (60*60));
total_seconds = total_seconds % (60*60);
}
printf("%d minutes\n", total_seconds / 60);
}
// We get the logits for all the tokens in the context window (params.n_ctx)
// from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity,

116
ggml-cuda.cu
View file

@ -1,5 +1,7 @@
#include <stdint.h>
#include <stdio.h>
#include <cuda_fp16.h>
#include <atomic>
#include "ggml-cuda.h"
typedef uint16_t ggml_fp16_t;
@ -29,14 +31,12 @@ static_assert(sizeof(block_q4_2) == sizeof(ggml_fp16_t) + QK4_2 / 2, "wrong q4_2
#define QK4_3 16
typedef struct {
__half d; // delta
__half m; // min
uint8_t qs[QK4_3 / 2]; // nibbles / quants
__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;
@ -131,24 +131,98 @@ static __global__ void dequantize_block_q4_3(const void * vx, float * y) {
}
}
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;
dequantize_block_q4_0<<<nb, 1, 0, stream>>>(vx, y);
}
void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_0;
dequantize_block_q4_0<<<nb, 1, 0, stream>>>(vx, y);
}
__host__ void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_1;
dequantize_block_q4_1<<<nb, 1, 0, stream>>>(vx, y);
}
void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_1;
dequantize_block_q4_1<<<nb, 1, 0, stream>>>(vx, y);
}
__host__ void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
const int nb = k / QK4_2;
dequantize_block_q4_2<<<nb, 1, 0, stream>>>(vx, y);
}
void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
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);
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);
}
// buffer pool for cuda
#define MAX_CUDA_BUFFERS 16
struct scoped_spin_lock {
std::atomic_flag& lock;
scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
while (lock.test_and_set(std::memory_order_acquire)) {
; // spin
}
}
~scoped_spin_lock() {
lock.clear(std::memory_order_release);
}
scoped_spin_lock(const scoped_spin_lock&) = delete;
scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
};
struct cuda_buffer {
void * ptr = nullptr;
size_t size = 0;
};
static cuda_buffer g_cuda_buffer_pool[MAX_CUDA_BUFFERS];
static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT;
void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
scoped_spin_lock lock(g_cuda_pool_lock);
for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
cuda_buffer& b = g_cuda_buffer_pool[i];
if (b.size >= size && b.ptr != nullptr) {
void * ptr = b.ptr;
*actual_size = b.size;
b.ptr = nullptr;
b.size = 0;
return ptr;
}
}
void * ptr;
CUDA_CHECK(cudaMalloc((void **) &ptr, size));
*actual_size = size;
return ptr;
}
void ggml_cuda_pool_free(void * ptr, size_t size) {
scoped_spin_lock lock(g_cuda_pool_lock);
for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
cuda_buffer& b = g_cuda_buffer_pool[i];
if (b.ptr == nullptr) {
b.ptr = ptr;
b.size = size;
return;
}
}
fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
CUDA_CHECK(cudaFree(ptr));
}
cublasHandle_t g_cublasH = NULL;
cudaStream_t g_cudaStream = NULL;
void ggml_init_cublas(void) {
if (g_cublasH == NULL) {
// create cublas handle, bind a stream
CUBLAS_CHECK(cublasCreate(&g_cublasH));
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStream, cudaStreamNonBlocking));
CUBLAS_CHECK(cublasSetStream(g_cublasH, g_cudaStream));
// configure logging to stdout
// CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, NULL));
}
}

29
ggml-cuda.h
View file

@ -1,7 +1,36 @@
#include <cublas_v2.h>
#include <cuda_runtime.h>
#ifdef __cplusplus
extern "C" {
#endif
#define CUDA_CHECK(err) \
do { \
cudaError_t err_ = (err); \
if (err_ != cudaSuccess) { \
fprintf(stderr, "CUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \
cudaGetErrorString(err_)); \
exit(1); \
} \
} while (0)
#define CUBLAS_CHECK(err) \
do { \
cublasStatus_t err_ = (err); \
if (err_ != CUBLAS_STATUS_SUCCESS) { \
fprintf(stderr, "cuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \
exit(1); \
} \
} while (0)
extern cublasHandle_t g_cublasH;
extern cudaStream_t g_cudaStream;
void ggml_init_cublas(void);
void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size);
void ggml_cuda_pool_free(void * ptr, size_t size);
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);

240
ggml.c
View file

@ -148,44 +148,7 @@ inline static void* ggml_aligned_malloc(size_t size) {
#elif defined(GGML_USE_OPENBLAS)
#include <cblas.h>
#elif defined(GGML_USE_CUBLAS)
#include <cublas_v2.h>
#include <cuda_runtime.h>
#include "ggml-cuda.h"
#define CUDA_CHECK(err) \
do { \
cudaError_t err_ = (err); \
if (err_ != cudaSuccess) { \
printf("CUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \
cudaGetErrorString(err_)); \
exit(1); \
} \
} while (0)
#define CUBLAS_CHECK(err) \
do { \
cublasStatus_t err_ = (err); \
if (err_ != CUBLAS_STATUS_SUCCESS) { \
printf("cuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \
exit(1); \
} \
} while (0)
static cublasHandle_t cublasH = NULL;
static cudaStream_t cudaStream = NULL;
static void init_cublas(void) {
if (cublasH == NULL) {
// create cublas handle, bind a stream
CUBLAS_CHECK(cublasCreate(&cublasH));
CUDA_CHECK(cudaStreamCreateWithFlags(&cudaStream, cudaStreamNonBlocking));
CUBLAS_CHECK(cublasSetStream(cublasH, cudaStream));
// configure logging to stdout
// CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, NULL));
}
}
#endif
#undef MIN
@ -657,9 +620,10 @@ static_assert(sizeof(block_q4_3) == 2 * sizeof(ggml_fp16_t) + QK4_3 / 2, "wrong
#define QK8_0 32
typedef struct {
float d; // delta
float s; // d * sum(qs[i])
int8_t qs[QK8_0]; // quants
} block_q8_0;
static_assert(sizeof(block_q8_0) == sizeof(float) + QK8_0, "wrong q8_0 block size/padding");
static_assert(sizeof(block_q8_0) == 2*sizeof(float) + QK8_0, "wrong q8_0 block size/padding");
// reference implementation for deterministic creation of model files
@ -1299,13 +1263,39 @@ static void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * r
y[i].d = d;
int sum = 0;
for (int l = 0; l < QK8_0; ++l) {
const float v = x[i*QK8_0 + l]*id;
y[i].qs[l] = roundf(v);
sum += y[i].qs[l];
}
y[i].s = d * sum;
}
}
#ifdef __AVX2__
// There is no better way of doing this?
// I guess not, AVX is not very good at horizontal sums.
// The commented solution for a hotrizontal sum was suggested by @pubby as being slightly
// faster than the solution below. As I don't have an AVX2 system handt right now to test,
// keeping the original.
// TODO: Please try and if it does make a differece, uncomment and remove the implementation below.
//static inline float horizontal_sum(__m256i a) {
// __m256i b = _mm256_castps_si256(_mm256_movehdup_ps(_mm256_castsi256_ps(a)));
// __m256i sum = _mm256_add_epi32(a, b);
// __m256i hi = _mm256_unpackhi_epi64(sum, sum);
// sum = _mm256_add_epi32(sum, hi);
// return _mm256_cvtsi256_si32(sum) + _mm256_extract_epi32(sum, 4);
//}
static inline float horizontal_sum(__m256i a) {
__m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extracti128_si256(a, 1));
__m128i hi64 = _mm_unpackhi_epi64(sum128, sum128);
__m128i sum64 = _mm_add_epi32(hi64, sum128);
__m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1));
return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32));
}
#endif
static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int k) {
assert(k % QK8_0 == 0);
const int nb = k / QK8_0;
@ -1332,6 +1322,8 @@ static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int
y[i].d = d;
int32x4_t accv = vdupq_n_s32(0);
for (int l = 0; l < 8; l++) {
const float32x4_t v = vmulq_n_f32(srcv[l], id);
const int32x4_t vi = vcvtnq_s32_f32(v);
@ -1340,7 +1332,11 @@ static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int
y[i].qs[4*l + 1] = vgetq_lane_s32(vi, 1);
y[i].qs[4*l + 2] = vgetq_lane_s32(vi, 2);
y[i].qs[4*l + 3] = vgetq_lane_s32(vi, 3);
accv = vaddq_s32(accv, vi);
}
int32_t sum = vaddvq_s32(accv);
y[i].s = d * sum;
}
#elif defined(__AVX2__) || defined(__AVX__)
for (int i = 0; i < nb; i++) {
@ -1388,6 +1384,10 @@ static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int
__m256i i3 = _mm256_cvtps_epi32( v3 );
#if defined(__AVX2__)
// Compute the sum of the quants and set y[i].s
y[i].s = d * horizontal_sum(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3)));
// Convert int32 to int16
i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15
i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31
@ -1430,6 +1430,14 @@ static void quantize_row_q8_0(const float * restrict x, void * restrict vy, int
// scalar
quantize_row_q8_0_reference(x, y, k);
#endif
#if defined __AVX__
// TODO: vectorize this
for (int i=0; i<nb; ++i) {
int sum = 0;
for (int l=0; l<QK8_0; ++l) sum += y[i].qs[l];
y[i].s = y[i].d * sum;
}
#endif
}
static void dequantize_row_q4_0(const void * restrict vx, float * restrict y, int k) {
@ -2372,14 +2380,17 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
float32x4_t sumv0 = vdupq_n_f32(0.0f);
float32x4_t sumv1 = vdupq_n_f32(0.0f);
float sum8 = 0;
for (int i = 0; i < nb; i += 2) {
const block_q4_0 * restrict x0 = &x[i + 0];
const block_q4_0 * restrict x1 = &x[i + 1];
const block_q8_0 * restrict y0 = &y[i + 0];
const block_q8_0 * restrict y1 = &y[i + 1];
sum8 += x0->d * y0->s + x1->d * y1->s;
const uint8x16_t m4b = vdupq_n_u8(0xf);
const int8x16_t s8b = vdupq_n_s8(0x8);
const uint8x16_t v0_0 = vld1q_u8(x0->qs);
const uint8x16_t v0_1 = vld1q_u8(x1->qs);
@ -2390,12 +2401,6 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
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));
// sub 8
const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b);
const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b);
const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b);
const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b);
// load y
const int8x16_t v1_0l = vld1q_s8(y0->qs);
const int8x16_t v1_0h = vld1q_s8(y0->qs + 16);
@ -2410,21 +2415,21 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
#if defined(__ARM_FEATURE_DOTPROD)
// dot product into int32x4_t
const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0ls), v0_0hs, v1_0hs);
const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1ls), v0_1hs, v1_1hs);
const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0ls), v0_0h, v1_0hs);
const int32x4_t p_1 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1ls), v0_1h, v1_1hs);
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), x0->d*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), x1->d*y1->d);
#else
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0ls));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0ls));
const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hs), vget_low_s8 (v1_0hs));
const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0hs), vget_high_s8(v1_0hs));
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0l), vget_low_s8 (v1_0ls));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0l), vget_high_s8(v1_0ls));
const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0h), vget_low_s8 (v1_0hs));
const int16x8_t ph0h = vmull_s8(vget_high_s8(v0_0h), vget_high_s8(v1_0hs));
const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1ls), vget_low_s8 (v1_1ls));
const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1ls), vget_high_s8(v1_1ls));
const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1hs), vget_low_s8 (v1_1hs));
const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1hs), vget_high_s8(v1_1hs));
const int16x8_t pl1l = vmull_s8(vget_low_s8 (v0_1l), vget_low_s8 (v1_1ls));
const int16x8_t pl1h = vmull_s8(vget_high_s8(v0_1l), vget_high_s8(v1_1ls));
const int16x8_t ph1l = vmull_s8(vget_low_s8 (v0_1h), vget_low_s8 (v1_1hs));
const int16x8_t ph1h = vmull_s8(vget_high_s8(v0_1h), vget_high_s8(v1_1hs));
const int32x4_t pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
const int32x4_t ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
@ -2436,7 +2441,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void *
#endif
}
sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) - 8 * sum8;
#elif defined(__AVX2__)
// Initialize accumulator with zeros
__m256 acc = _mm256_setzero_ps();
@ -2569,12 +2574,16 @@ static void ggml_vec_dot_q4_1_q8_0(const int n, float * restrict s, const void *
float32x4_t sumv0 = vdupq_n_f32(0.0f);
float32x4_t sumv1 = vdupq_n_f32(0.0f);
float summs = 0;
for (int i = 0; i < nb; i += 2) {
const block_q4_1 * restrict x0 = &x[i + 0];
const block_q4_1 * restrict x1 = &x[i + 1];
const block_q8_0 * restrict y0 = &y[i + 0];
const block_q8_0 * restrict y1 = &y[i + 1];
summs += x0->m * y0->s + x1->m * y1->s;
const uint8x16_t m4b = vdupq_n_u8(0xf);
const uint8x16_t v0_0 = vld1q_u8(x0->qs);
@ -2598,17 +2607,6 @@ static void ggml_vec_dot_q4_1_q8_0(const int n, float * restrict s, const void *
const int8x16_t v1_1ls = vuzp1q_s8(v1_1l, v1_1h);
const int8x16_t v1_1hs = vuzp2q_s8(v1_1l, v1_1h);
const int16x8_t s0i = vaddq_s16(
vaddq_s16(vmovl_s8(vget_low_s8(v1_0ls)), vmovl_s8(vget_high_s8(v1_0ls))),
vaddq_s16(vmovl_s8(vget_low_s8(v1_0hs)), vmovl_s8(vget_high_s8(v1_0hs))));
const int16x8_t s1i = vaddq_s16(
vaddq_s16(vmovl_s8(vget_low_s8(v1_1ls)), vmovl_s8(vget_high_s8(v1_1ls))),
vaddq_s16(vmovl_s8(vget_low_s8(v1_1hs)), vmovl_s8(vget_high_s8(v1_1hs))));
sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddl_s16(vget_low_s16(s0i), vget_high_s16(s0i))), x0->m*y0->d);
sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddl_s16(vget_low_s16(s1i), vget_high_s16(s1i))), x1->m*y1->d);
#if defined(__ARM_FEATURE_DOTPROD)
// dot product into int32x4_t
const int32x4_t p_0 = vdotq_s32(vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0ls), v0_0h, v1_0hs);
@ -2637,24 +2635,26 @@ static void ggml_vec_dot_q4_1_q8_0(const int n, float * restrict s, const void *
#endif
}
sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs;
#elif defined(__AVX2__)
// Initialize accumulator with zeros
__m256 acc = _mm256_setzero_ps();
float summs = 0;
// Main loop
for (int i = 0; i < nb; ++i) {
const float * d0 = &x[i].d;
const float * d1 = &y[i].d;
const float * m0 = &x[i].m;
//const float * m0 = &x[i].m;
summs += x[i].m * y[i].s;
const __m256 d0v = _mm256_broadcast_ss( d0 );
const __m256 d1v = _mm256_broadcast_ss( d1 );
const __m256 m0v = _mm256_broadcast_ss( m0 );
// Compute combined scales
const __m256 d0d1 = _mm256_mul_ps( d0v, d1v );
const __m256 d1m0 = _mm256_mul_ps( d1v, m0v );
// Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes
const __m256i bx = bytes_from_nibbles_32(x[i].qs);
@ -2676,15 +2676,6 @@ static void ggml_vec_dot_q4_1_q8_0(const int n, float * restrict s, const void *
// Accumulate d0*d1*x*y
acc = _mm256_fmadd_ps( d0d1, xy, acc );
// Compute sum of y values
const __m256i y16_l = _mm256_cvtepi8_epi16( _mm256_castsi256_si128( by ) );
const __m256i y16_h = _mm256_cvtepi8_epi16( _mm256_extracti128_si256( by, 1 ) );
const __m256i ysumi = _mm256_madd_epi16( _mm256_add_epi16(y16_l, y16_h), ones );
const __m256 ysum = _mm256_cvtepi32_ps( ysumi );
// Accumulate d1*m0*y
acc = _mm256_fmadd_ps( d1m0, ysum, acc );
}
// Return horizontal sum of the acc vector
@ -2693,7 +2684,7 @@ static void ggml_vec_dot_q4_1_q8_0(const int n, float * restrict s, const void *
res = _mm_add_ps( res, _mm_movehl_ps( res, res ) );
res = _mm_add_ss( res, _mm_movehdup_ps( res ) );
sumf = _mm_cvtss_f32( res );
sumf = _mm_cvtss_f32( res ) + summs;
#else
// scalar
for (int i = 0; i < nb; i++) {
@ -3720,7 +3711,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
// initialize cuBLAS
#if defined(GGML_USE_CUBLAS)
init_cublas();
ggml_init_cublas();
#endif
is_first_call = false;
@ -7566,18 +7557,16 @@ static void ggml_compute_forward_mul_mat_f32(
}
#if defined(GGML_USE_CUBLAS)
float *d_X = NULL;
float *d_Y = NULL;
float *d_D = NULL;
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne10;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
CUDA_CHECK(cudaMalloc((void **)(&d_X), sizeof(float) * x_ne));
CUDA_CHECK(cudaMalloc((void **)(&d_Y), sizeof(float) * y_ne));
CUDA_CHECK(cudaMalloc((void **)(&d_D), sizeof(float) * d_ne));
size_t x_size, y_size, d_size;
float *d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size);
float *d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size);
float *d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size);
#endif
for (int64_t i03 = 0; i03 < ne03; i03++) {
@ -7589,19 +7578,19 @@ static void ggml_compute_forward_mul_mat_f32(
#if defined(GGML_USE_CUBLAS)
// copy data to device
CUDA_CHECK(cudaMemcpyAsync(d_X, x, sizeof(float) * x_ne, cudaMemcpyHostToDevice, cudaStream));
CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(float) * y_ne, cudaMemcpyHostToDevice, cudaStream));
CUDA_CHECK(cudaMemcpyAsync(d_X, x, sizeof(float) * x_ne, cudaMemcpyHostToDevice, g_cudaStream));
CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(float) * y_ne, cudaMemcpyHostToDevice, g_cudaStream));
// compute
CUBLAS_CHECK(
cublasSgemm(cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, d_X, ne00,
d_Y, ne10,
&beta, d_D, ne01));
// copy data to host
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream));
#else
// zT = y * xT
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
@ -7613,10 +7602,10 @@ static void ggml_compute_forward_mul_mat_f32(
}
}
#if defined(GGML_USE_CUBLAS)
CUDA_CHECK(cudaStreamSynchronize(cudaStream));
CUDA_CHECK(cudaFree(d_X));
CUDA_CHECK(cudaFree(d_Y));
CUDA_CHECK(cudaFree(d_D));
CUDA_CHECK(cudaStreamSynchronize(g_cudaStream));
ggml_cuda_pool_free(d_X, x_size);
ggml_cuda_pool_free(d_Y, y_size);
ggml_cuda_pool_free(d_D, d_size);
#endif
//printf("CBLAS F32 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
@ -7766,18 +7755,16 @@ static void ggml_compute_forward_mul_mat_f16_f32(
#if defined(GGML_USE_CUBLAS)
ggml_fp16_t * const wdata = params->wdata;
float *d_X = NULL;
float *d_Y = NULL;
float *d_D = NULL;
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne10;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
CUDA_CHECK(cudaMalloc((void **)(&d_X), sizeof(ggml_fp16_t) * x_ne));
CUDA_CHECK(cudaMalloc((void **)(&d_Y), sizeof(float) * y_ne));
CUDA_CHECK(cudaMalloc((void **)(&d_D), sizeof(float) * d_ne));
size_t x_size, y_size, d_size;
float *d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size);
float *d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size);
float *d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size);
#else
float * const wdata = params->wdata;
#endif
@ -7811,12 +7798,12 @@ static void ggml_compute_forward_mul_mat_f16_f32(
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
// copy data to device
CUDA_CHECK(cudaMemcpyAsync(d_X, x, sizeof(ggml_fp16_t) * x_ne, cudaMemcpyHostToDevice, cudaStream));
CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(ggml_fp16_t) * y_ne, cudaMemcpyHostToDevice, cudaStream));
CUDA_CHECK(cudaMemcpyAsync(d_X, x, sizeof(ggml_fp16_t) * x_ne, cudaMemcpyHostToDevice, g_cudaStream));
CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(ggml_fp16_t) * y_ne, cudaMemcpyHostToDevice, g_cudaStream));
// compute
CUBLAS_CHECK(
cublasGemmEx(cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
cublasGemmEx(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, d_X, CUDA_R_16F, ne00,
d_Y, CUDA_R_16F, ne10,
@ -7825,7 +7812,7 @@ static void ggml_compute_forward_mul_mat_f16_f32(
CUBLAS_GEMM_DEFAULT));
// copy data to host
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream));
#else
const float * x = wdata;
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
@ -7843,10 +7830,10 @@ static void ggml_compute_forward_mul_mat_f16_f32(
}
#if defined(GGML_USE_CUBLAS)
CUDA_CHECK(cudaStreamSynchronize(cudaStream));
CUDA_CHECK(cudaFree(d_X));
CUDA_CHECK(cudaFree(d_Y));
CUDA_CHECK(cudaFree(d_D));
CUDA_CHECK(cudaStreamSynchronize(g_cudaStream));
ggml_cuda_pool_free(d_X, x_size);
ggml_cuda_pool_free(d_Y, y_size);
ggml_cuda_pool_free(d_D, d_size);
#endif
/*printf("CBLAS F16 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);*/
@ -8014,20 +8001,17 @@ static void ggml_compute_forward_mul_mat_q_f32(
}
#if defined(GGML_USE_CUBLAS)
float *d_X = NULL;
float *d_Y = NULL;
float *d_D = NULL;
float *d_Q = NULL;
const float alpha = 1.0f;
const float beta = 0.0f;
const int x_ne = ne01 * ne10;
const int y_ne = ne11 * ne10;
const int d_ne = ne11 * ne01;
CUDA_CHECK(cudaMalloc((void **)(&d_X), sizeof(float) * x_ne));
CUDA_CHECK(cudaMalloc((void **)(&d_Y), sizeof(float) * y_ne));
CUDA_CHECK(cudaMalloc((void **)(&d_D), sizeof(float) * d_ne));
CUDA_CHECK(cudaMalloc((void **)(&d_Q), GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type]));
size_t x_size, y_size, d_size, q_size;
float *d_X = ggml_cuda_pool_malloc(sizeof(float) * x_ne, &x_size);
float *d_Y = ggml_cuda_pool_malloc(sizeof(float) * y_ne, &y_size);
float *d_D = ggml_cuda_pool_malloc(sizeof(float) * d_ne, &d_size);
float *d_Q = ggml_cuda_pool_malloc(GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type], &q_size);
void (*dequantize_row_q_cuda)(const void * x, float * y, int k, cudaStream_t stream) = NULL;
if (type == GGML_TYPE_Q4_0) {
@ -8057,9 +8041,9 @@ static void ggml_compute_forward_mul_mat_q_f32(
// copy and dequantize on device
CUDA_CHECK(
cudaMemcpyAsync(d_Q, (char *) src0->data + i03*nb03 + i02*nb02,
GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type], cudaMemcpyHostToDevice, cudaStream));
GGML_TYPE_SIZE[type] * x_ne / GGML_BLCK_SIZE[type], cudaMemcpyHostToDevice, g_cudaStream));
dequantize_row_q_cuda(d_Q, d_X, ne01 * ne00, cudaStream);
dequantize_row_q_cuda(d_Q, d_X, ne01 * ne00, g_cudaStream);
CUDA_CHECK(cudaGetLastError());
#else
{
@ -8075,18 +8059,18 @@ static void ggml_compute_forward_mul_mat_q_f32(
#if defined(GGML_USE_CUBLAS)
// copy data to device
CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(float) * y_ne, cudaMemcpyHostToDevice, cudaStream));
CUDA_CHECK(cudaMemcpyAsync(d_Y, y, sizeof(float) * y_ne, cudaMemcpyHostToDevice, g_cudaStream));
// compute
CUBLAS_CHECK(
cublasSgemm(cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
cublasSgemm(g_cublasH, CUBLAS_OP_T, CUBLAS_OP_N,
ne01, ne11, ne10,
&alpha, d_X, ne00,
d_Y, ne10,
&beta, d_D, ne01));
// copy data to host
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, cudaStream));
CUDA_CHECK(cudaMemcpyAsync(d, d_D, sizeof(float) * d_ne, cudaMemcpyDeviceToHost, g_cudaStream));
#else
// zT = y * xT
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
@ -8099,11 +8083,11 @@ static void ggml_compute_forward_mul_mat_q_f32(
}
#if defined(GGML_USE_CUBLAS)
CUDA_CHECK(cudaStreamSynchronize(cudaStream));
CUDA_CHECK(cudaFree(d_X));
CUDA_CHECK(cudaFree(d_Y));
CUDA_CHECK(cudaFree(d_D));
CUDA_CHECK(cudaFree(d_Q));
CUDA_CHECK(cudaStreamSynchronize(g_cudaStream));
ggml_cuda_pool_free(d_X, x_size);
ggml_cuda_pool_free(d_Y, y_size);
ggml_cuda_pool_free(d_D, d_size);
ggml_cuda_pool_free(d_Q, q_size);
#endif
//printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);

8
llama.cpp
View file

@ -1618,8 +1618,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
// quantize only 2D tensors
quantize &= (tensor.ne.size() == 2);
// GG: uncomment this to keep the output layer in FP16
//if (tensor.name.rfind("output")) {
// uncomment this to keep the output layer in FP16
//if (tensor.name == "output.weight") {
// quantize = false;
//}
@ -2092,7 +2092,11 @@ void llama_set_kv_cache(
int n_token_count) {
// Make sure we have the same kv cache setup
LLAMA_ASSERT(ctx->model.kv_self.buf.size == n_size);
void * k_data = ctx->model.kv_self.k->data; // remember data pointers
void * v_data = ctx->model.kv_self.v->data; // because their value is stored in buf and overwritten by memcpy
memcpy(ctx->model.kv_self.buf.addr, kv_cache, n_size);
ctx->model.kv_self.k->data = k_data; // restore correct data pointers
ctx->model.kv_self.v->data = v_data;
ctx->model.kv_self.n = n_token_count;
}

17
llama_util.h
View file

@ -21,6 +21,9 @@
#if defined(_POSIX_MAPPED_FILES)
#include <sys/mman.h>
#endif
#if defined(_POSIX_MEMLOCK_RANGE)
#include <sys/resource.h>
#endif
#endif
#endif
@ -303,8 +306,18 @@ struct llama_mlock {
if (!mlock(addr, size)) {
return true;
} else {
fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n" MLOCK_SUGGESTION,
size, this->size, std::strerror(errno));
char* errmsg = std::strerror(errno);
bool suggest = (errno == ENOMEM);
// Check if the resource limit is fine after all
struct rlimit lock_limit;
if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit))
suggest = false;
if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size))
suggest = false;
fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
return false;
}
}

5
pocs/vdot/CMakeLists.txt
View file

@ -2,3 +2,8 @@ set(TARGET vdot)
add_executable(${TARGET} vdot.cpp)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
set(TARGET q8dot)
add_executable(${TARGET} q8dot.cpp)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

172
pocs/vdot/q8dot.cpp Normal file
View file

@ -0,0 +1,172 @@
#include <cstdio>
#include <type_traits>
#include <vector>
#include <random>
#include <chrono>
#include <cstdlib>
#include <cmath>
#include <cassert>
#include <cstring>
#include <array>
#include <type_traits>
#include <ggml.h>
constexpr int kVecSize = 1 << 16;
// Copy-pasted from ggml.c
#define QK4_0 32
typedef struct {
float d; // delta
uint8_t qs[QK4_0 / 2]; // nibbles / quants
} block_q4_0;
static_assert(sizeof(block_q4_0) == sizeof(float) + QK4_0 / 2, "wrong q4_0 block size/padding");
#define QK4_1 32
typedef struct {
float d; // delta
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");
// Copy-pasted from ggml.c
#define QK8_0 32
typedef struct {
float d; // delta
float s; // d * sum(qs[i])
int8_t qs[QK8_0]; // quants
} block_q8_0;
static_assert(sizeof(block_q8_0) == 2*sizeof(float) + QK8_0, "wrong q8_0 block size/padding");
static_assert(QK4_1 == QK8_0, "QK4_1 and QK8_0 must be the same");
static_assert(QK4_0 == QK8_0, "QK4_0 and QK8_0 must be the same");
template <typename T>
void fillQ4blocks(std::vector<T>& blocks, std::mt19937& rndm) {
for (auto& b : blocks) {
b.d = 1;
for (int i=0; i<QK4_1/2; ++i) {
uint8_t v1 = rndm() >> 28;
uint8_t v2 = rndm() >> 28;
b.qs[i] = v1 | (v2 << 4);
}
}
}
void fillQ80blocks(std::vector<block_q8_0>& blocks, std::mt19937& rndm) {
for (auto& b : blocks) {
b.d = 1;
int sum = 0;
for (int i=0; i<QK8_0; ++i) {
b.qs[i] = (rndm() >> 24) - 128;
sum += b.qs[i];
}
b.s = b.d * sum;
}
}
float simpleDot(const block_q4_0& x, const block_q8_0& y) {
int s1 = 0; //, s2 = 0;
for (int i=0; i<QK4_1/2; i+=2) {
int v1 = x.qs[i+0] & 0xf;
int v2 = x.qs[i+0] >> 4;
int v3 = x.qs[i+1] & 0xf;
int v4 = x.qs[i+1] >> 4;
int j = 2*i;
s1 += v1*y.qs[j] + v2*y.qs[j+1] + v3*y.qs[j+2] + v4*y.qs[j+3];
//s2 += y.qs[j] + y.qs[j+1] + y.qs[j+2] + y.qs[j+3];
}
return y.d * x.d * s1 - 8 * x.d * y.s;
//return y.d * x.d * (s1 - 8 * s2);
}
float simpleDot(const block_q4_1& x, const block_q8_0& y) {
int s1 = 0; //, s2 = 0;
for (int i=0; i<QK4_1/2; i+=2) {
int v1 = x.qs[i+0] & 0xf;
int v2 = x.qs[i+0] >> 4;
int v3 = x.qs[i+1] & 0xf;
int v4 = x.qs[i+1] >> 4;
int j = 2*i;
s1 += v1*y.qs[j] + v2*y.qs[j+1] + v3*y.qs[j+2] + v4*y.qs[j+3];
//s2 += y.qs[j] + y.qs[j+1] + y.qs[j+2] + y.qs[j+3];
}
return y.d * x.d * s1 + y.s * x.m;
//return y.d * (x.d * s1 + x.m * s2);
}
struct Stat {
double sum = 0, sumt = 0, sumt2 = 0, maxt = 0;
int nloop = 0;
void addResult(double s, double t) {
sum += s;
sumt += t; sumt2 += t*t; maxt = std::max(maxt, t);
++nloop;
}
void reportResult(const char* title) const {
if (nloop < 1) {
printf("%s(%s): no result\n",__func__,title);
return;
}
printf("============ %s\n",title);
printf("<dot> = %g\n",sum/nloop);
auto t = sumt/nloop, dt = sumt2/nloop - t*t;
if (dt > 0) dt = sqrt(dt);
printf("<time> = %g +/- %g us. Max. time = %g us.\n",t,dt,maxt);
}
};
int main(int argc, char** argv) {
int nloop = argc > 1 ? atoi(argv[1]) : 10;
int type = argc > 2 ? atoi(argv[2]) : 1;
std::mt19937 rndm(1234);
std::vector<block_q4_1> x41;
std::vector<block_q4_0> x40;
std::vector<block_q8_0> y(kVecSize);
if (type == 0) x40.resize(kVecSize);
else {
x41.resize(kVecSize);
for (auto& b : x41) b.m = 1;
}
auto ggml_type = type == 0 ? GGML_TYPE_Q4_0 : GGML_TYPE_Q4_1;
auto funcs = ggml_internal_get_quantize_fn(ggml_type);
Stat simple, ggml;
for (int iloop=0; iloop<nloop; ++iloop) {
if (type == 0) fillQ4blocks(x40, rndm);
else fillQ4blocks(x41, rndm);
fillQ80blocks(y, rndm);
auto t1 = std::chrono::high_resolution_clock::now();
double s = 0;
if (type == 0) for (int i=0; i<kVecSize; ++i) s += simpleDot(x40[i], y[i]);
else for (int i=0; i<kVecSize; ++i) s += simpleDot(x41[i], y[i]);
auto t2 = std::chrono::high_resolution_clock::now();
auto t = 1e-3*std::chrono::duration_cast<std::chrono::nanoseconds>(t2-t1).count();
if (iloop > 3) simple.addResult(s, t);
t1 = std::chrono::high_resolution_clock::now();
float fs;
if (type == 0) funcs.vec_dot_q(kVecSize * QK4_1, &fs, x40.data(), y.data());
else funcs.vec_dot_q(kVecSize * QK4_1, &fs, x41.data(), y.data());
t2 = std::chrono::high_resolution_clock::now();
t = 1e-3*std::chrono::duration_cast<std::chrono::nanoseconds>(t2-t1).count();
if (iloop > 3) ggml.addResult(fs, t);
}
// Report the time (and the average of the dot products so the compiler does not come up with the idea
// of optimizing away the function calls after figuring that the result is not used).
simple.reportResult("Simple");
ggml.reportResult("ggml");
return 0;
}