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whisper.cpp
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sync : ggml (HBM + Metal + style) (#1264)

pull/1265/head
Georgi Gerganov 2023-09-08 17:58:31 +03:00 committed by GitHub
parent 3e9edc6845
commit b39809668a
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 42 additions and 39 deletions
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2
ggml-metal.m
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@ -1141,7 +1141,7 @@ void ggml_metal_graph_compute(
[encoder setBytes:&freq_base length:sizeof(float) atIndex:21];
[encoder setBytes:&freq_scale length:sizeof(float) atIndex:22];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)];
} break;
case GGML_OP_DUP:
case GGML_OP_CPY:

49
ggml-metal.metal
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@ -220,14 +220,10 @@ kernel void kernel_norm(
}
threadgroup_barrier(mem_flags::mem_threadgroup);
}
//// broadcast
//if (tpitg == 0) {
// sum[0] /= ne00;
//}
//threadgroup_barrier(mem_flags::mem_threadgroup);
const float mean = sum[0];
const float mean = sum[0] / ne00;
// recenter and VARIANCE
threadgroup_barrier(mem_flags::mem_threadgroup);
device float * y = dst + tgpig*ne00;
sum[tpitg] = 0.0f;
for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
@ -235,12 +231,6 @@ kernel void kernel_norm(
sum[tpitg] += y[i00] * y[i00];
}
//// VARIANCE
//// parallel sum
//sum[tpitg] = 0.0f;
//for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
// sum[tpitg] += y[i00] * y[i00];
//}
// reduce
threadgroup_barrier(mem_flags::mem_threadgroup);
for (uint i = ntg/2; i > 0; i /= 2) {
@ -249,12 +239,7 @@ kernel void kernel_norm(
}
threadgroup_barrier(mem_flags::mem_threadgroup);
}
//// broadcast
//if (tpitg == 0) {
// sum[0] /= ne00;
//}
//threadgroup_barrier(mem_flags::mem_threadgroup);
const float variance = sum[0];
const float variance = sum[0] / ne00;
const float scale = 1.0f/sqrt(variance + eps);
for (int i00 = tpitg; i00 < ne00; i00 += ntg) {
@ -262,7 +247,6 @@ kernel void kernel_norm(
}
}
kernel void kernel_rms_norm(
device const void * src0,
device float * dst,
@ -630,7 +614,6 @@ kernel void kernel_mul_mat_f16_f32(
}
}
}
}
kernel void kernel_alibi_f32(
@ -699,25 +682,27 @@ kernel void kernel_rope(
constant int & mode,
constant float & freq_base,
constant float & freq_scale,
uint3 tpig[[thread_position_in_grid]]) {
const int64_t i3 = tpig[2];
const int64_t i2 = tpig[1];
const int64_t i1 = tpig[0];
uint tiitg[[thread_index_in_threadgroup]],
uint3 tptg[[threads_per_threadgroup]],
uint3 tgpig[[threadgroup_position_in_grid]]) {
const int64_t i3 = tgpig[2];
const int64_t i2 = tgpig[1];
const int64_t i1 = tgpig[0];
const bool is_neox = mode & 2;
const float theta_scale = pow(freq_base, -2.0f/n_dims);
const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2);
float theta = freq_scale * (float)p;
const float theta_0 = freq_scale * (float)p;
const float inv_ndims = -1.f/n_dims;
if (!is_neox) {
for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) {
const float theta = theta_0 * pow(freq_base, inv_ndims*i0);
const float cos_theta = cos(theta);
const float sin_theta = sin(theta);
theta *= theta_scale;
device const float * const src = (device float *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
device float * dst_data = (device float *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
@ -729,12 +714,12 @@ kernel void kernel_rope(
}
} else {
for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
for (int64_t ic = 0; ic < n_dims; ic += 2) {
for (int64_t ic = 2*tiitg; ic < n_dims; ic += 2*tptg.x) {
const float theta = theta_0 * pow(freq_base, inv_ndims*ic - ib);
const float cos_theta = cos(theta);
const float sin_theta = sin(theta);
theta *= theta_scale;
const int64_t i0 = ib*n_dims + ic/2;
device const float * const src = (device float *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);

30
ggml.c
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@ -106,6 +106,9 @@ typedef void * thread_ret_t;
#include <sys/stat.h>
#include <unistd.h>
#endif
#ifdef GGML_USE_CPU_HBM
#include <hbwmalloc.h>
#endif
// __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512
@ -195,8 +198,14 @@ typedef void * thread_ret_t;
#define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr)
#else
inline static void * ggml_aligned_malloc(size_t size) {
if (size == 0) {
GGML_PRINT("WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_aligned_malloc!\n");
return NULL;
}
void * aligned_memory = NULL;
#ifdef GGML_USE_METAL
#ifdef GGML_USE_CPU_HBM
int result = hbw_posix_memalign(&aligned_memory, 16, size);
#elif GGML_USE_METAL
int result = posix_memalign(&aligned_memory, sysconf(_SC_PAGESIZE), size);
#else
int result = posix_memalign(&aligned_memory, GGML_MEM_ALIGN, size);
@ -218,8 +227,12 @@ inline static void * ggml_aligned_malloc(size_t size) {
return aligned_memory;
}
#define GGML_ALIGNED_MALLOC(size) ggml_aligned_malloc(size)
#ifdef GGML_USE_CPU_HBM
#define GGML_ALIGNED_FREE(ptr) if(NULL != ptr) hbw_free(ptr)
#else
#define GGML_ALIGNED_FREE(ptr) free(ptr)
#endif
#endif
#define UNUSED GGML_UNUSED
#define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0)
@ -4571,6 +4584,11 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
return NULL;
}
// allow to call ggml_init with 0 size
if (params.mem_size == 0) {
params.mem_size = GGML_MEM_ALIGN;
}
const size_t mem_size = params.mem_buffer ? params.mem_size : GGML_PAD(params.mem_size, GGML_MEM_ALIGN);
*ctx = (struct ggml_context) {
@ -4773,7 +4791,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
size_t obj_alloc_size = 0;
if (view_src == NULL && ctx->no_alloc == false) {
if (view_src == NULL && !ctx->no_alloc) {
if (ctx->scratch.data != NULL) {
// allocate tensor data in the scratch buffer
if (ctx->scratch.offs + data_size > ctx->scratch.size) {
@ -5474,7 +5492,7 @@ static struct ggml_tensor * ggml_mul_impl(
}
if (inplace) {
GGML_ASSERT(is_node == false);
GGML_ASSERT(!is_node);
}
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
@ -5517,7 +5535,7 @@ static struct ggml_tensor * ggml_div_impl(
}
if (inplace) {
GGML_ASSERT(is_node == false);
GGML_ASSERT(!is_node);
}
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
@ -19961,7 +19979,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
struct ggml_tensor * data = NULL;
if (params.no_alloc == false) {
if (!params.no_alloc) {
data = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I8, ctx->size);
ok = ok && data != NULL;
@ -20002,7 +20020,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
}
// point the data member to the appropriate location in the binary blob using the tensor infos
if (params.no_alloc == false) {
if (!params.no_alloc) {
//cur->data = (char *) data->data + ctx->infos[i].offset - ctx->offset; // offset from start of file
cur->data = (char *) data->data + ctx->infos[i].offset; // offset from data
}