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# Conflicts:
#	README.md
This commit is contained in:
Concedo 2023-04-11 16:53:41 +08:00
commit 1f6aa47b6e
3 changed files with 305 additions and 70 deletions
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367
ggml.c
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@ -26,14 +26,9 @@
#define static_assert(cond, msg) struct global_scope_noop_trick
#endif
#if defined _MSC_VER || defined(__MINGW32__)
#if defined(_WIN32)
#if !defined(__MINGW32__)
#include <Windows.h>
#else
// ref: https://github.com/ggerganov/whisper.cpp/issues/168
#include <windows.h>
#endif
typedef volatile LONG atomic_int;
typedef atomic_int atomic_bool;
@ -599,10 +594,7 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int
for (int l = 0; l < 2; l++) amaxv[4*l] = vmaxq_f32(amaxv[4*l], amaxv[4*l+2]);
for (int l = 0; l < 1; l++) amaxv[8*l] = vmaxq_f32(amaxv[8*l], amaxv[8*l+4]);
// absolute max
const float amax = MAX(
MAX(vgetq_lane_f32(amaxv[0], 0), vgetq_lane_f32(amaxv[0], 1)),
MAX(vgetq_lane_f32(amaxv[0], 2), vgetq_lane_f32(amaxv[0], 3)));
const float amax = vmaxvq_f32(amaxv[0]);
const float d = amax / ((1 << 3) - 1);
const float id = d ? 1.0f/d : 0.0f;
@ -924,7 +916,7 @@ static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int
float32x4_t minv[8];
float32x4_t maxv[8];
for (int l = 0; l < 8; l++) srcv[l] = vld1q_f32(x + i*32 + 4*l);
for (int l = 0; l < 8; l++) srcv[l] = vld1q_f32(x + i*QK + 4*l);
for (int l = 0; l < 4; l++) minv[2*l] = vminq_f32(srcv[2*l], srcv[2*l + 1]);
for (int l = 0; l < 2; l++) minv[4*l] = vminq_f32(minv[4*l], minv[4*l + 2]);
@ -947,7 +939,8 @@ static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int
for (int l = 0; l < 8; l++) {
const float32x4_t v = vmulq_n_f32(vsubq_f32(srcv[l], minv0), id);
const int32x4_t vi = vcvtq_s32_f32(v);
const float32x4_t vf = vaddq_f32(v, vdupq_n_f32(0.5f)); // needed to round to nearest
const int32x4_t vi = vcvtq_s32_f32(vf);
y[i].qs[2*l + 0] = vgetq_lane_s32(vi, 0) | (vgetq_lane_s32(vi, 1) << 4);
y[i].qs[2*l + 1] = vgetq_lane_s32(vi, 2) | (vgetq_lane_s32(vi, 3) << 4);
@ -1951,7 +1944,7 @@ static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * rest
// Initialize accumulator with zeros
__m256 acc = _mm256_setzero_ps();
/* Prepare the constants we will need during execution */
/* Prepare the constants we will need during execution */
const __m256i lowMask = _mm256_set1_epi8( 0xF );
const __m256i offset_8 = _mm256_set1_epi16( 8 );
@ -1961,61 +1954,59 @@ static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * rest
// Main loop
for (int i = 0; i < nb; i+=UNROLL_COUNT) {
// This loop will be unrolled by the compiler
// This loop will be unrolled by the compiler
for (int u=0;u<UNROLL_COUNT;u++) {
/* Compute combined scale for the block */
const __m256 scale = _mm256_mul_ps(
_mm256_broadcast_ss( &x[i+u].d ),
_mm256_broadcast_ss( &y[i+u].d ) );
/* Compute combined scale for the block */
const __m256 scale = _mm256_mul_ps(
_mm256_broadcast_ss( &x[i+u].d ),
_mm256_broadcast_ss( &y[i+u].d ) );
/* get input from x
Input: 32 Nibbles (16 bytes) at *x[i+u]
Output: 2 vectors with 16 values of type int16_t (x_high_q, x_low_q) */
/* Load 16 bytes from memory */
const __m128i tmp_x = _mm_loadu_si128( ( const __m128i* ) x[i+u].qs);
/* Expand bytes into uint16_t values */
const __m256i bytes_x = _mm256_cvtepu8_epi16(tmp_x);
/* get input from x
Input: 32 Nibbles (16 bytes) at *x[i+u]
Output: 2 vectors with 16 values of type int16_t (x_high_q, x_low_q) */
/* Load 16 bytes from memory */
const __m128i tmp_x = _mm_loadu_si128( ( const __m128i* ) x[i+u].qs);
/* Expand bytes into uint16_t values */
const __m256i bytes_x = _mm256_cvtepu8_epi16(tmp_x);
/* Unpack values into individual bytes */
__m256i x_low_q = _mm256_and_si256( lowMask, bytes_x );
const __m256i pre_shift_x_high_q = _mm256_andnot_si256( lowMask, bytes_x );
__m256i x_high_q = _mm256_srli_epi16( pre_shift_x_high_q, 4 );
__m256i x_high_q = _mm256_srli_epi16( pre_shift_x_high_q, 4 );
/* Now we have two vectors with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. */
x_high_q = _mm256_sub_epi16( x_high_q, offset_8 );
x_low_q = _mm256_sub_epi16( x_low_q, offset_8 );
x_high_q = _mm256_sub_epi16( x_high_q, offset_8 );
x_low_q = _mm256_sub_epi16( x_low_q, offset_8 );
/* get input from y
Input: 32 Nibbles (16 bytes) at *y[i+u]
Output: 2 vectors with 16 values of type int16_t (y_high_q, y_low_q) */
/* get input from y
Input: 32 Nibbles (16 bytes) at *y[i+u]
Output: 2 vectors with 16 values of type int16_t (y_high_q, y_low_q) */
/* Load 16 bytes from memory */
const __m128i tmp_y = _mm_loadu_si128( (const __m128i* ) y[i+u].qs);
/* Expand bytes into uint16_t values */
const __m256i bytes_y = _mm256_cvtepu8_epi16(tmp_y);
/* Load 16 bytes from memory */
const __m128i tmp_y = _mm_loadu_si128( (const __m128i* ) y[i+u].qs);
/* Expand bytes into uint16_t values */
const __m256i bytes_y = _mm256_cvtepu8_epi16(tmp_y);
/* Unpack values into individual bytes */
const __m256i pre_shift_y_high_q = _mm256_andnot_si256( lowMask, bytes_y );
__m256i y_high_q = _mm256_srli_epi16( pre_shift_y_high_q, 4 );
__m256i y_low_q = _mm256_and_si256( lowMask, bytes_y );
const __m256i pre_shift_y_high_q = _mm256_andnot_si256( lowMask, bytes_y );
__m256i y_high_q = _mm256_srli_epi16( pre_shift_y_high_q, 4 );
__m256i y_low_q = _mm256_and_si256( lowMask, bytes_y );
/* Now we have two vectors with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. */
y_high_q = _mm256_sub_epi16( y_high_q, offset_8 );
y_low_q = _mm256_sub_epi16( y_low_q, offset_8 );
y_high_q = _mm256_sub_epi16( y_high_q, offset_8 );
y_low_q = _mm256_sub_epi16( y_low_q, offset_8 );
/* Compute products of int16_t integers, add pairwise, store as int32_t */
__m256i xy_high_q = _mm256_madd_epi16( x_high_q, y_high_q );
__m256i xy_low_q = _mm256_madd_epi16( x_low_q, y_low_q );
/* Compute products of int16_t integers, add pairwise, store as int32_t */
__m256i xy_high_q = _mm256_madd_epi16( x_high_q, y_high_q );
__m256i xy_low_q = _mm256_madd_epi16( x_low_q, y_low_q );
/* Accumulate the products of int32_t integers -> we now have a vector of 8 int_32t */
__m256i xy_q = _mm256_add_epi32( xy_high_q, xy_low_q );
/* Accumulate the products of int32_t integers -> we now have a vector of 8 int_32t */
__m256i xy_q = _mm256_add_epi32( xy_high_q, xy_low_q );
/* Convert to vectore of 8 int32_t to 8 floats */
__m256 q = _mm256_cvtepi32_ps( xy_q );
/* Convert to vectore of 8 int32_t to 8 floats */
__m256 q = _mm256_cvtepi32_ps( xy_q );
/* Multiply q with scale and accumulate */
acc = _mm256_fmadd_ps( scale, q, acc );
/* Multiply q with scale and accumulate */
acc = _mm256_fmadd_ps( scale, q, acc );
}
}
}
// Return horizontal sum of the acc vector
__m128 res = _mm256_extractf128_ps( acc, 1 );
@ -2076,18 +2067,18 @@ static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * rest
float sum1 = 0.0f;
for (int i = 0; i < nb; i += 2) {
const block_q4_0 * restrict x0 = &px[i + 0];
const block_q4_0 * restrict y0 = &py[i + 0];
const block_q4_0 * restrict x1 = &px[i + 1];
const block_q4_0 * restrict y1 = &py[i + 1];
const block_q4_0 * restrict x0 = &x[i + 0];
const block_q4_0 * restrict y0 = &y[i + 0];
const block_q4_0 * restrict x1 = &x[i + 1];
const block_q4_0 * restrict y1 = &y[i + 1];
const v128_t m4b = wasm_u8x16_splat(0xf);
const v128_t s8b = wasm_i8x16_splat(0x8);
const v128_t v0_0 = wasm_v128_load(x0.qs);
const v128_t v0_1 = wasm_v128_load(y0.qs);
const v128_t v1_0 = wasm_v128_load(x1.qs);
const v128_t v1_1 = wasm_v128_load(y1.qs);
const v128_t v0_0 = wasm_v128_load(x0->qs);
const v128_t v0_1 = wasm_v128_load(y0->qs);
const v128_t v1_0 = wasm_v128_load(x1->qs);
const v128_t v1_1 = wasm_v128_load(y1->qs);
// 4-bit -> 8-bit
const v128_t v0_0l = wasm_v128_and(v0_0, m4b);
@ -2618,6 +2609,7 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"SCALE",
"CPY",
"CONT",
"RESHAPE",
"VIEW",
"PERMUTE",
@ -2633,7 +2625,7 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"FLASH_FF",
};
static_assert(GGML_OP_COUNT == 35, "GGML_OP_COUNT != 35");
static_assert(GGML_OP_COUNT == 36, "GGML_OP_COUNT != 36");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -2662,6 +2654,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"x*v",
"x-\\>y",
"cont(x)",
"reshape(x)",
"view(x)",
"permute(x)",
@ -2677,7 +2670,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"flash_ff(x)",
};
static_assert(GGML_OP_COUNT == 35, "GGML_OP_COUNT != 35");
static_assert(GGML_OP_COUNT == 36, "GGML_OP_COUNT != 36");
static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
@ -4310,6 +4303,41 @@ struct ggml_tensor * ggml_cpy_inplace(
return ggml_cpy_impl(ctx, a, b, true);
}
// ggml_cont
struct ggml_tensor * ggml_cont_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
bool inplace) {
bool is_node = false;
if (!inplace && a->grad) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
result->op = GGML_OP_CONT;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
return result;
}
struct ggml_tensor * ggml_cont(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_cont_impl(ctx, a, false);
}
struct ggml_tensor * ggml_cont_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_cont_impl(ctx, a, true);
}
// ggml_reshape
struct ggml_tensor * ggml_reshape(
@ -4852,6 +4880,85 @@ static void ggml_compute_forward_dup_f16(
// TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy
if (ggml_is_contiguous(dst)) {
if (src0->nb[0] == sizeof(ggml_fp16_t)) {
if (dst->type == GGML_TYPE_F16) {
size_t id = 0;
const size_t rs = ne00*nb00;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
char * dst_ptr = (char *) dst->data + id*rs;
memcpy(dst_ptr, src0_ptr, rs);
id++;
}
}
}
} else if (dst->type == GGML_TYPE_F32) {
size_t id = 0;
float * dst_ptr = (float *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr);
id++;
}
}
}
}
} else {
GGML_ASSERT(false); // TODO: implement
}
} else {
//printf("%s: this is not optimal - fix me\n", __func__);
if (dst->type == GGML_TYPE_F32) {
size_t id = 0;
float * dst_ptr = (float *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr);
id++;
}
}
}
}
} else if (dst->type == GGML_TYPE_F16) {
size_t id = 0;
ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = *src0_ptr;
id++;
}
}
}
}
} else {
GGML_ASSERT(false); // TODO: implement
}
}
return;
}
// dst counters
int64_t i10 = 0;
int64_t i11 = 0;
@ -4946,6 +5053,105 @@ static void ggml_compute_forward_dup_f32(
return;
}
if (src0->type == dst->type &&
src0->ne[0] == dst->ne[0] &&
src0->nb[0] == GGML_TYPE_SIZE[src0->type] && dst->nb[0] == GGML_TYPE_SIZE[dst->type]) {
// copy by rows
const size_t rs = ne00*nb00;
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
for (int64_t i01 = 0; i01 < ne01; i01++) {
memcpy(
((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3),
((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03),
rs);
}
}
}
return;
}
if (ggml_is_contiguous(dst)) {
// TODO: simplify
if (src0->nb[0] == sizeof(float)) {
if (dst->type == GGML_TYPE_F32) {
size_t id = 0;
const size_t rs = ne00*nb00;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
char * dst_ptr = (char *) dst->data + id*rs;
memcpy(dst_ptr, src0_ptr, rs);
id++;
}
}
}
} else if (dst->type == GGML_TYPE_F16) {
size_t id = 0;
ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
id++;
}
}
}
}
} else {
GGML_ASSERT(false); // TODO: implement
}
} else {
//printf("%s: this is not optimal - fix me\n", __func__);
if (dst->type == GGML_TYPE_F32) {
size_t id = 0;
float * dst_ptr = (float *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = *src0_ptr;
id++;
}
}
}
}
} else if (dst->type == GGML_TYPE_F16) {
size_t id = 0;
ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
id++;
}
}
}
}
} else {
GGML_ASSERT(false); // TODO: implement
}
}
return;
}
// dst counters
int64_t i10 = 0;
int64_t i11 = 0;
@ -5066,14 +5272,18 @@ static void ggml_compute_forward_add_f32(
GGML_ASSERT(nb00 == sizeof(float));
if (nb10 == sizeof(float)) {
const int j0 = (n/nth)*ith;
const int j1 = ith == nth - 1 ? n : (n/nth)*(ith + 1);
for (int j = j0; j < j1; j++) {
for (int j = ith; j < n; j += nth) {
#ifdef GGML_USE_ACCELERATE
vDSP_vadd(
(float *) ((char *) src0->data + j*nb01), 1,
(float *) ((char *) src1->data + j*nb11), 1,
(float *) ((char *) dst->data + j*nb1), 1, nc);
#else
ggml_vec_add_f32(nc,
(float *) ((char *) dst->data + j*nb1),
(float *) ((char *) src0->data + j*nb01),
(float *) ((char *) src1->data + j*nb11));
#endif
}
} else {
// src1 is not contiguous
@ -6821,6 +7031,15 @@ static void ggml_compute_forward_cpy(
ggml_compute_forward_dup(params, src0, dst);
}
// ggml_compute_forward_cont
static void ggml_compute_forward_cont(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
struct ggml_tensor * dst) {
ggml_compute_forward_dup(params, src0, dst);
}
// ggml_compute_forward_reshape
static void ggml_compute_forward_reshape(
@ -8651,6 +8870,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_cpy(params, tensor->src0, tensor);
} break;
case GGML_OP_CONT:
{
ggml_compute_forward_cont(params, tensor->src0, tensor);
} break;
case GGML_OP_RESHAPE:
{
ggml_compute_forward_reshape(params, tensor->src0, tensor);
@ -8895,8 +9118,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src1->grad =
ggml_add_impl(ctx,
src1->grad,
// TODO: fix transpose, the node will break the graph connections
ggml_mul_mat(ctx, ggml_transpose(ctx, src0), tensor->grad),
ggml_mul_mat(ctx,
ggml_cont(ctx, ggml_transpose(ctx, src0)),
tensor->grad),
inplace);
}
} break;
@ -8908,6 +9132,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_CONT:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_RESHAPE:
{
GGML_ASSERT(false); // TODO: not implemented
@ -9362,6 +9590,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
node->n_tasks = n_threads;
} break;
case GGML_OP_CPY:
case GGML_OP_CONT:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:

6
ggml.h
View file

@ -236,6 +236,7 @@ enum ggml_op {
GGML_OP_SCALE,
GGML_OP_CPY,
GGML_OP_CONT,
GGML_OP_RESHAPE,
GGML_OP_VIEW,
GGML_OP_PERMUTE,
@ -525,6 +526,11 @@ struct ggml_tensor * ggml_cpy(
struct ggml_tensor * a,
struct ggml_tensor * b);
// make contiguous
struct ggml_tensor * ggml_cont(
struct ggml_context * ctx,
struct ggml_tensor * a);
// return view(a), b specifies the new shape
// TODO: when we start computing gradient, make a copy instead of view
struct ggml_tensor * ggml_reshape(

2
koboldcpp.py
View file

@ -378,7 +378,7 @@ def main(args):
RunServerMultiThreaded(args.host, args.port, embedded_kailite)
if __name__ == '__main__':
print("Welcome to KoboldCpp - Version 1.4") # just update version manually
print("Welcome to KoboldCpp - Version 1.5") # just update version manually
parser = argparse.ArgumentParser(description='Kobold llama.cpp server')
parser.add_argument("model_file", help="Model file to load", nargs="?")
portgroup = parser.add_mutually_exclusive_group() #we want to be backwards compatible with the unnamed positional args