extern/whisper.cpp
1
0
Fork 1
mirror of https://github.com/ggerganov/whisper.cpp.git synced 2024-11-07 16:44:13 +01:00
Code Issues Packages Projects Releases Wiki Activity

ggml : add ggml_upscale_ext (ggml/814)

Browse Source 
* initial commit with CPU implementation of upscale to shape and test, cuda implementation next

* experimental commit to see if dst shape is correct

* test version

* test

* removed unnecessary params

* refactor

* fixed tests

* ggml : metal impl + cleanup + sycl dev warnings

* patched ggml_upscale cuda op to handle non-contiguous tensors, added test for non-contiguous behavior

* metal : fix upsacle op to support nb00 + style

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
John Balis 2024-05-15 03:52:33 -05:00 committed by Georgi Gerganov
parent 5b7073cae1
commit c4de1e19df
6 changed files with 117 additions and 57 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

63
ggml-cuda/upscale.cu
View File

@ -1,35 +1,36 @@
#include "upscale.cuh"
static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int ne00xne01, const int scale_factor) {
// blockIdx.z: idx of ne02*ne03
// blockIdx.y: idx of ne01*scale_factor， aka ne1
// blockIDx.x: idx of ne00*scale_factor / BLOCK_SIZE
// ne00xne01: ne00 * ne01
int ne0 = ne00 * scale_factor;
int nidx = threadIdx.x + blockIdx.x * blockDim.x;
if (nidx >= ne0) {
static __global__ void upscale_f32(const float * x, float * dst,
const int nb00, const int nb01, const int nb02, const int nb03,
const int ne10, const int ne11, const int ne12, const int ne13,
const float sf0, const float sf1, const float sf2, const float sf3) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
if (index >= ne10 * ne11 * ne12 * ne13) {
return;
}
// operation
int i00 = nidx / scale_factor;
int i01 = blockIdx.y / scale_factor;
int offset_src =
i00 +
i01 * ne00 +
blockIdx.z * ne00xne01;
int offset_dst =
nidx +
blockIdx.y * ne0 +
blockIdx.z * ne0 * gridDim.y;
dst[offset_dst] = x[offset_src];
int i10 = index % ne10;
int i11 = (index / ne10) % ne11;
int i12 = (index / (ne10 * ne11)) % ne12;
int i13 = (index / (ne10 * ne11 * ne12)) % ne13;
int i00 = i10 / sf0;
int i01 = i11 / sf1;
int i02 = i12 / sf2;
int i03 = i13 / sf3;
dst[index] = *(float *)((char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00);
}
static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int ne03,
const int scale_factor, cudaStream_t stream) {
int ne0 = (ne00 * scale_factor);
int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE;
dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02*ne03);
upscale_f32<<<gridDim, CUDA_UPSCALE_BLOCK_SIZE, 0, stream>>>(x, dst, ne00, ne00 * ne01, scale_factor);
static void upscale_f32_cuda(const float * x, float * dst,
const int nb00, const int nb01, const int nb02, const int nb03,
const int ne10, const int ne11, const int ne12, const int ne13,
const float sf0, const float sf1, const float sf2, const float sf3,
cudaStream_t stream) {
int dst_size = ne10 * ne11 * ne12 * ne13;
int num_blocks = (dst_size + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE;
upscale_f32<<<num_blocks, CUDA_UPSCALE_BLOCK_SIZE,0,stream>>>(x, dst, nb00, nb01, nb02, nb03, ne10, ne11, ne12, ne13, sf0, sf1, sf2, sf3);
}
void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@ -39,10 +40,12 @@ void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
cudaStream_t stream = ctx.stream();
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
GGML_ASSERT( dst->type == GGML_TYPE_F32);
const int scale_factor = dst->op_params[0];
const float sf0 = (float)dst->ne[0]/src0->ne[0];
const float sf1 = (float)dst->ne[1]/src0->ne[1];
const float sf2 = (float)dst->ne[2]/src0->ne[2];
const float sf3 = (float)dst->ne[3]/src0->ne[3];
upscale_f32_cuda(src0_d, dst_d, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], scale_factor, stream);
upscale_f32_cuda(src0_d, dst_d, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3, stream);
}

10
ggml-metal.m
View File

@ -2353,7 +2353,10 @@ static enum ggml_status ggml_metal_graph_compute(
{
GGML_ASSERT(src0->type == GGML_TYPE_F32);
const int sf = dst->op_params[0];
const float sf0 = (float)ne0/src0->ne[0];
const float sf1 = (float)ne1/src0->ne[1];
const float sf2 = (float)ne2/src0->ne[2];
const float sf3 = (float)ne3/src0->ne[3];
const id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline;
@ -2376,7 +2379,10 @@ static enum ggml_status ggml_metal_graph_compute(
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15];
[encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16];
[encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17];
[encoder setBytes:&sf length:sizeof(sf) atIndex:18];
[encoder setBytes:&sf0 length:sizeof(sf0) atIndex:18];
[encoder setBytes:&sf1 length:sizeof(sf1) atIndex:19];
[encoder setBytes:&sf2 length:sizeof(sf2) atIndex:20];
[encoder setBytes:&sf3 length:sizeof(sf3) atIndex:21];
const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0);

21
ggml-metal.metal
View File

@ -1852,7 +1852,10 @@ kernel void kernel_upscale_f32(
constant uint64_t & nb1,
constant uint64_t & nb2,
constant uint64_t & nb3,
constant int32_t & sf,
constant float & sf0,
constant float & sf1,
constant float & sf2,
constant float & sf3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
@ -1861,15 +1864,17 @@ kernel void kernel_upscale_f32(
const int64_t i2 = tgpig.y;
const int64_t i1 = tgpig.x;
const int64_t i03 = i3;
const int64_t i02 = i2;
const int64_t i01 = i1/sf;
device const float * src0_ptr = (device const float *) (src0 + i03*nb03 + i02*nb02 + i01*nb01);
device float * dst_ptr = (device float *) (dst + i3*nb3 + i2*nb2 + i1*nb1);
const int64_t i03 = i3/sf3;
const int64_t i02 = i2/sf2;
const int64_t i01 = i1/sf1;
for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) {
dst_ptr[i0] = src0_ptr[i0/sf];
const int64_t i00 = i0/sf0;
device const float * src0_ptr = (device const float *) (src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
device float * dst_ptr = (device float *) (dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
dst_ptr[0] = src0_ptr[0];
}
}

4
ggml-sycl.cpp
View File

@ -13987,6 +13987,10 @@ inline void ggml_sycl_op_upscale(const ggml_tensor *src0,
GGML_ASSERT(dst->type == GGML_TYPE_F32);
GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
#pragma message("TODO: generalize upscale operator")
#pragma message(" https://github.com/ggerganov/ggml/pull/814")
GGML_ASSERT(false && "TODO: generalize upscale operator);
const int scale_factor = dst->op_params[0];
upscale_f32_sycl(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], scale_factor, main_stream);

64
ggml.c
View File

@ -6293,7 +6293,10 @@ struct ggml_tensor * ggml_pool_2d(
static struct ggml_tensor * ggml_upscale_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
int scale_factor) {
int ne0,
int ne1,
int ne2,
int ne3) {
bool is_node = false;
if (a->grad) {
@ -6301,19 +6304,45 @@ static struct ggml_tensor * ggml_upscale_impl(
is_node = true;
}
GGML_ASSERT(a->ne[0] <= ne0);
GGML_ASSERT(a->ne[1] <= ne1);
GGML_ASSERT(a->ne[2] <= ne2);
GGML_ASSERT(a->ne[3] <= ne3);
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type,
a->ne[0] * scale_factor,
a->ne[1] * scale_factor,
a->ne[2], a->ne[3]);
ne0,
ne1,
ne2,
ne3
);
result->op = GGML_OP_UPSCALE;
result->op_params[0] = scale_factor;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
return result;
}
struct ggml_tensor * ggml_upscale(
struct ggml_context * ctx,
struct ggml_tensor * a,
int scale_factor) {
return ggml_upscale_impl(ctx, a, a->ne[0] * scale_factor, a->ne[1] * scale_factor, a->ne[2], a->ne[3]);
}
struct ggml_tensor * ggml_upscale_ext(
struct ggml_context * ctx,
struct ggml_tensor * a,
int ne0,
int ne1,
int ne2,
int ne3) {
return ggml_upscale_impl(ctx, a, ne0, ne1, ne2, ne3);
}
// ggml_pad
struct ggml_tensor * ggml_pad(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -6338,12 +6367,7 @@ struct ggml_tensor * ggml_pad(
return result;
}
struct ggml_tensor * ggml_upscale(
struct ggml_context * ctx,
struct ggml_tensor * a,
int scale_factor) {
return ggml_upscale_impl(ctx, a, scale_factor);
}
// ggml_arange
struct ggml_tensor * ggml_arange(
struct ggml_context * ctx,
@ -6365,6 +6389,8 @@ struct ggml_tensor * ggml_arange(
return result;
}
// ggml_timestep_embedding
struct ggml_tensor * ggml_timestep_embedding(
struct ggml_context * ctx,
struct ggml_tensor * timesteps,
@ -14820,25 +14846,28 @@ static void ggml_compute_forward_upscale_f32(
return;
}
GGML_ASSERT(src0->nb[0] == sizeof(float));
GGML_ASSERT(src0->type == GGML_TYPE_F32);
const int ith = params->ith;
const int nth = params->nth;
GGML_TENSOR_UNARY_OP_LOCALS
const int scale_factor = dst->op_params[0];
const float sf0 = (float)ne0/src0->ne[0];
const float sf1 = (float)ne1/src0->ne[1];
const float sf2 = (float)ne2/src0->ne[2];
const float sf3 = (float)ne3/src0->ne[3];
// TODO: optimize
for (int64_t i3 = 0; i3 < ne3; i3++) {
const int64_t i03 = i3;
const int64_t i03 = i3 / sf3;
for (int64_t i2 = ith; i2 < ne2; i2 += nth) {
const int64_t i02 = i2;
const int64_t i02 = i2 / sf2;
for (int64_t i1 = 0; i1 < ne1; i1++) {
const int64_t i01 = i1 / scale_factor;
const int64_t i01 = i1 / sf1;
for (int64_t i0 = 0; i0 < ne0; i0++) {
const int64_t i00 = i0 / scale_factor;
const int64_t i00 = i0 / sf0;
const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
float * y = (float *)((char *) dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
@ -14868,6 +14897,7 @@ static void ggml_compute_forward_upscale(
}
}
// ggml_compute_forward_pad
static void ggml_compute_forward_pad_f32(

12
ggml.h
View File

@ -1674,12 +1674,24 @@ extern "C" {
float p1);
// nearest interpolate
// multiplies ne0 and ne1 by scale factor
// used in stable-diffusion
GGML_API struct ggml_tensor * ggml_upscale(
struct ggml_context * ctx,
struct ggml_tensor * a,
int scale_factor);
// nearest interpolate
// nearest interpolate to specified dimensions
// used in tortoise.cpp
GGML_API struct ggml_tensor * ggml_upscale_ext(
struct ggml_context * ctx,
struct ggml_tensor * a,
int ne0,
int ne1,
int ne2,
int ne3);
// pad each dimension with zeros: [x, ..., x] -> [x, ..., x, 0, ..., 0]
GGML_API struct ggml_tensor * ggml_pad(
struct ggml_context * ctx,